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# Automatically generated by generator.py in splunk/security_content
# On Date: 2023-10-26T19:38:53 UTC
# Author: Splunk Threat Research Team - Splunk
# Contact: research@splunk.com
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### ESCU DETECTIONS ###

[ESCU - Detect New Login Attempts to Routers - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.
action.escu.how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.
action.escu.known_false_positives = Legitimate router connections may appear as new connections
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Login Attempts to Routers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect New Login Attempts to Routers - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.
action.notable.param.rule_title = Detect New Login Attempts to Routers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter`

[ESCU - Detect Risky SPL using Pretrained ML Model - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses a pretrained machine learning text classifier to detect potentially risky commands. The model is trained independently and then the model file is packaged within ESCU for usage. A command is deemed risky based on the presence of certain trigger keywords, along with the context and the role of the user (please see references). The model uses custom features to predict whether a SPL is risky using text classification. The model takes as input the command text, user and search type and outputs a risk score between [0,1]. A high score indicates higher likelihood of a command being risky. This model is on-prem only.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Splunk_Audit"]
action.escu.eli5 = The following analytic uses a pretrained machine learning text classifier to detect potentially risky commands. The model is trained independently and then the model file is packaged within ESCU for usage. A command is deemed risky based on the presence of certain trigger keywords, along with the context and the role of the user (please see references). The model uses custom features to predict whether a SPL is risky using text classification. The model takes as input the command text, user and search type and outputs a risk score between [0,1]. A high score indicates higher likelihood of a command being risky. This model is on-prem only.
action.escu.how_to_implement = This detection depends on the MLTK app which can be found here - https://splunkbase.splunk.com/app/2890/ and the Splunk Audit datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. Additionally, you need to be ingesting logs which include Search_Activity.search, Search_Activity.user, Search_Activity.search_type from your endpoints. The risk score threshold should be adjusted based on the environment. The detection uses a custom MLTK model hence we need a few more steps for deployment, as outlined here - https://gist.github.com/ksharad-splunk/be2a62227966049047f5e5c4f2adcabb.
action.escu.known_false_positives = False positives may be present if suspicious behavior is observed, as determined by frequent usage of risky keywords.
action.escu.creation_date = 2022-06-16
action.escu.modification_date = 2022-06-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Risky SPL using Pretrained ML Model - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = A potentially risky Splunk command has been run by $user$, kindly review.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Risky SPL using Pretrained ML Model - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 40, "cve": ["CVE-2022-32154"], "impact": 50, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where Search_Activity.search_type=adhoc Search_Activity.user!=splunk-system-user by Search_Activity.search Search_Activity.user Search_Activity.search_type | eval spl_text = 'Search_Activity.search'. " " .'Search_Activity.user'. " " .'Search_Activity.search_type'| dedup spl_text | apply risky_spl_pre_trained_model | where risk_score > 0.5 | `drop_dm_object_name(Search_Activity)` | table search, user, search_type, risk_score | `detect_risky_spl_using_pretrained_ml_model_filter`

[ESCU - Email Attachments With Lots Of Spaces - Rule]
action.escu = 0
action.escu.enabled = 1
description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.
action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \
 **Splunk Phantom Playbook Integration**\
If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.
action.escu.known_false_positives = None at this time
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Email Attachments With Lots Of Spaces - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "Emotet Malware  DHS Report TA18-201A ", "Hermetic Wiper", "Suspicious Emails"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Email Attachments With Lots Of Spaces - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Emotet Malware  DHS Report TA18-201A ", "Hermetic Wiper", "Suspicious Emails"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 |  rex field=recipient_address "(?<recipient_user>.*)@" | `email_attachments_with_lots_of_spaces_filter`

[ESCU - Email files written outside of the Outlook directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1114", "T1114.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
action.escu.known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Email files written outside of the Outlook directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Collection and Staging"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Email files written outside of the Outlook directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1114", "T1114.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.
action.notable.param.rule_title = Email files written outside of the Outlook directory
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*"  Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` 

[ESCU - Email servers sending high volume traffic to hosts - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1114", "T1114.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.
action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model.  Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.
action.escu.known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Email servers sending high volume traffic to hosts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Collection and Staging", "HAFNIUM Group"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Email servers sending high volume traffic to hosts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging", "HAFNIUM Group"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1114", "T1114.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter`

[ESCU - Monitor Email For Brand Abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.
action.escu.how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated.  You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
action.escu.known_false_positives = None at this time
action.escu.creation_date = 2018-01-05
action.escu.modification_date = 2018-01-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Monitor Email For Brand Abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Monitor Email For Brand Abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brand Monitoring", "Suspicious Emails"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.
action.notable.param.rule_title = Monitor Email For Brand Abuse
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter`

[ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule]
action.escu = 0
action.escu.enabled = 1
description = This hunting analytic identifies multiple failed logon attempts from a single IP. Use this analytic to identify patterns of suspicious logins from a single source and filter as needed or use this to drive tuning for higher fidelity analytics.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This hunting analytic identifies multiple failed logon attempts from a single IP. Use this analytic to identify patterns of suspicious logins from a single source and filter as needed or use this to drive tuning for higher fidelity analytics.
action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
action.escu.known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(src_user) as distinct_users values(src_user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` 

[ESCU - No Windows Updates in a time frame - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Updates"]
action.escu.eli5 = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason.
action.escu.how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2017-09-15
action.escu.modification_date = 2017-09-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - No Windows Updates in a time frame - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Monitor for Updates"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - No Windows Updates in a time frame - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor for Updates"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0)  | `security_content_ctime(lastTime)`  | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter`

[ESCU - Okta Account Locked Out - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes the user.acount.lock event to identify associates who are locked out of Okta. An adversary attempting to brute force or password spray account names may lock accounts out depending on the threshold.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes the user.acount.lock event to identify associates who are locked out of Okta. An adversary attempting to brute force or password spray account names may lock accounts out depending on the threshold.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Account Locked Out - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity", "Okta MFA Exhaustion"]
action.risk = 1
action.risk.param._risk_message = $src_user$ account has been locked out.
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Account Locked Out - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity", "Okta MFA Exhaustion"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType=user.account.lock | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by src_user src_ip eventType status | where count >=3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `okta_account_locked_out_filter`

[ESCU - Okta Account Lockout Events - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following anomaly will generate based on account lockout events utilizing Okta eventTypes of user.account.lock.limit or user.account.lock. Per the Okta docs site, this event is fired when a user account has reached the lockout limit. The account will not auto-unlock and a user or client cannot gain access to the account. This event indicates an account that will not be able to log in until remedial action is taken by the account admin. This event can be used to understand the specifics of an account lockout. Often this indicates a client application that is repeatedly attempting to authenticate with invalid credentials such as an old password.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following anomaly will generate based on account lockout events utilizing Okta eventTypes of user.account.lock.limit or user.account.lock. Per the Okta docs site, this event is fired when a user account has reached the lockout limit. The account will not auto-unlock and a user or client cannot gain access to the account. This event indicates an account that will not be able to log in until remedial action is taken by the account admin. This event can be used to understand the specifics of an account lockout. Often this indicates a client application that is repeatedly attempting to authenticate with invalid credentials such as an old password.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor.
action.escu.creation_date = 2022-09-19
action.escu.modification_date = 2022-09-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Account Lockout Events - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = The following user $src_user$ has locked out their account within Okta.
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Account Lockout Events - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType IN (user.account.lock.limit,user.account.lock) | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats count min(_time) as firstTime max(_time) as lastTime values(src_user) by displayMessage, country, state, city, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_account_lockout_events_filter`

[ESCU - Okta Failed SSO Attempts - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following anomaly identifies failed Okta SSO events utilizing the legacy Okta event "unauth app access attempt".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following anomaly identifies failed Okta SSO events utilizing the legacy Okta event "unauth app access attempt".
action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
action.escu.known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Failed SSO Attempts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = $src_user$ failed SSO authentication to the app.
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 16}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Failed SSO Attempts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 40, "impact": 40, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType=app.generic.unauth_app_access_attempt | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by src_user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` 

[ESCU - Okta MFA Exhaustion Hunt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies patterns within Okta data to determine the amount of successful and failed pushes. Based on that, eval statements determine a finding of whether this is suspicious or not. The events are within a window of time and may be tuned as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = The following analytic identifies patterns within Okta data to determine the amount of successful and failed pushes. Based on that, eval statements determine a finding of whether this is suspicious or not. The events are within a window of time and may be tuned as needed.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete.
action.escu.creation_date = 2022-09-27
action.escu.modification_date = 2022-09-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta MFA Exhaustion Hunt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity", "Okta MFA Exhaustion"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta MFA Exhaustion Hunt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity", "Okta MFA Exhaustion"], "cis20": ["CIS 10"], "confidence": 60, "impact": 30, "mitre_attack": ["T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) | stats count(eval(legacyEventType="core.user.factor.attempt_success")) as successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures count(eval(eventType="system.push.send_factor_verify_push")) as pushes by authenticationContext.externalSessionId,user,_time | stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as successes sum(failures) as failures sum(pushes) as pushes by authenticationContext.externalSessionId,user | eval seconds=lasttime-firsttime | eval lasttime=strftime(lasttime, "%c") | search (pushes>1) | eval totalattempts=successes+failures | eval finding="Normal authentication pattern" | eval finding=if(failures==pushes AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding) | eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) | eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple pushes sent, eventual successful authentication!",finding) | where seconds<600 AND finding="Multiple pushes sent, eventual successful authentication!" AND totalattempts > 5 | sort - pushes, totalattempts | `okta_mfa_exhaustion_hunt_filter`

[ESCU - Okta Mismatch Between Source and Response for Verify Push Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies variations in client-based values for source and response events to identify suspicious request behavior. The detection is enhanced if the org is evaluating behavior conditions in sign-on policies using Okta Behavior Detection. NOTE: This detection requires the use of Okta Identity Engine (OIE) and will not function on Okta Classic.\
For each Okta Verify Push challenge, the following two events are recorded in Okta System Log \
Source of Push (Sign-In) \
eventType eq \"system.push.send_factor_verify_push\" \
User Push Response (Okta Verify client) \
eventType eq "user.authentication.auth_via_mfa" AND debugContext.debugData.factor eq "OKTA_VERIFY_PUSH" \
In sequence, the logic for the analytic - \
* Groups by SessionID and retrieves any system.push.send_factor_verify_push events (the source of the push) and user.authentication.auth_via_mfa events where the factor is OKTA_VERIFY_PUSH - (the user response to the push) \
* Counts the total number of push events, successful authentication events, and any push sources where the client is a new device. \ * Creates a ratio of successful sign-ins to pushes. \
* If the ratio (currently tuned aggressively) indicates push spam, or if a user has rejected a push, the detection proceeds to evaluate whether there is more than one IP address used during the session (session roaming) and the presence of both a new IP and new device during the session.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1621"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies variations in client-based values for source and response events to identify suspicious request behavior. The detection is enhanced if the org is evaluating behavior conditions in sign-on policies using Okta Behavior Detection. NOTE: This detection requires the use of Okta Identity Engine (OIE) and will not function on Okta Classic.\
For each Okta Verify Push challenge, the following two events are recorded in Okta System Log \
Source of Push (Sign-In) \
eventType eq \"system.push.send_factor_verify_push\" \
User Push Response (Okta Verify client) \
eventType eq "user.authentication.auth_via_mfa" AND debugContext.debugData.factor eq "OKTA_VERIFY_PUSH" \
In sequence, the logic for the analytic - \
* Groups by SessionID and retrieves any system.push.send_factor_verify_push events (the source of the push) and user.authentication.auth_via_mfa events where the factor is OKTA_VERIFY_PUSH - (the user response to the push) \
* Counts the total number of push events, successful authentication events, and any push sources where the client is a new device. \ * Creates a ratio of successful sign-ins to pushes. \
* If the ratio (currently tuned aggressively) indicates push spam, or if a user has rejected a push, the detection proceeds to evaluate whether there is more than one IP address used during the session (session roaming) and the presence of both a new IP and new device during the session.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta:im2 logs to be ingested.
action.escu.known_false_positives = False positives may be present based on organization size and configuration of Okta. Monitor, tune and filter as needed.
action.escu.creation_date = 2023-03-17
action.escu.modification_date = 2023-03-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Mismatch Between Source and Response for Verify Push Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity", "Okta MFA Exhaustion"]
action.risk = 1
action.risk.param._risk_message = A mismatch between source and response for verifying a push request has occurred for $actor.alternateId$
action.risk.param._risk = [{"risk_object_field": "actor.alternateId", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Mismatch Between Source and Response for Verify Push Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity", "Okta MFA Exhaustion"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1621"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies variations in client-based values for source and response events to identify suspicious request behavior. The detection is enhanced if the org is evaluating behavior conditions in sign-on policies using Okta Behavior Detection. NOTE: This detection requires the use of Okta Identity Engine (OIE) and will not function on Okta Classic.\
For each Okta Verify Push challenge, the following two events are recorded in Okta System Log \
Source of Push (Sign-In) \
eventType eq \"system.push.send_factor_verify_push\" \
User Push Response (Okta Verify client) \
eventType eq "user.authentication.auth_via_mfa" AND debugContext.debugData.factor eq "OKTA_VERIFY_PUSH" \
In sequence, the logic for the analytic - \
* Groups by SessionID and retrieves any system.push.send_factor_verify_push events (the source of the push) and user.authentication.auth_via_mfa events where the factor is OKTA_VERIFY_PUSH - (the user response to the push) \
* Counts the total number of push events, successful authentication events, and any push sources where the client is a new device. \ * Creates a ratio of successful sign-ins to pushes. \
* If the ratio (currently tuned aggressively) indicates push spam, or if a user has rejected a push, the detection proceeds to evaluate whether there is more than one IP address used during the session (session roaming) and the presence of both a new IP and new device during the session.
action.notable.param.rule_title = Okta Mismatch Between Source and Response for Verify Push Request
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH") | eval groupby="authenticationContext.externalSessionId" | eval group_push_time=_time | bin span=2s group_push_time | fillnull value=NULL | stats min(_time) as _time by authenticationContext.externalSessionId eventType debugContext.debugData.factor outcome.result actor.alternateId client.device client.ipAddress client.userAgent.rawUserAgent debugContext.debugData.behaviors group_push_time groupby | iplocation client.ipAddress | fields - lat, lon, group_push_time | stats min(_time) as _time dc(client.ipAddress) as dc_ip sum(eval(if(eventType="system.push.send_factor_verify_push" AND "outcome.result"="SUCCESS",1,0))) as total_pushes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="SUCCESS",1,0))) as total_successes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="FAILURE",1,0))) as total_rejected sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors" LIKE "%New Device=POSITIVE%",1,0))) as suspect_device_from_source sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors" LIKE "%New IP=POSITIVE%",0,0))) as suspect_ip_from_source values(eval(if(eventType="system.push.send_factor_verify_push","client.ipAddress",""))) as src values(eval(if(eventType="user.authentication.auth_via_mfa","client.ipAddress",""))) as dest values(*) as * by groupby | eval ratio = round(total_successes/total_pushes,2) | search ((ratio < 0.5 AND total_pushes > 1) OR (total_rejected > 0)) AND dc_ip > 1 AND suspect_device_from_source > 0 AND suspect_ip_from_source > 0 | `okta_mismatch_between_source_and_response_for_verify_push_request_filter`

[ESCU - Okta Multiple Failed Requests to Access Applications - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies multiple failed app requests in an attempt to identify the reuse a stolen web session cookie. The logic of the analytic is as follows: \ * Retrieves policy evaluation and SSO details in events that contain the Application requested \
* Formats target fields so we can aggregate specifically on Applications (AppInstances) \
* Groups by User, Session and IP \
* Creates a ratio of successful SSO events to total MFA challenges related to Application Sign On Policies \
* Alerts when more than half of app sign on events are unsuccessful, and challenges were unsatisfied for more than three apps.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550.004", "T1538"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies multiple failed app requests in an attempt to identify the reuse a stolen web session cookie. The logic of the analytic is as follows: \ * Retrieves policy evaluation and SSO details in events that contain the Application requested \
* Formats target fields so we can aggregate specifically on Applications (AppInstances) \
* Groups by User, Session and IP \
* Creates a ratio of successful SSO events to total MFA challenges related to Application Sign On Policies \
* Alerts when more than half of app sign on events are unsuccessful, and challenges were unsatisfied for more than three apps.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta:im2 logs to be ingested.
action.escu.known_false_positives = False positives may be present based on organization size and configuration of Okta.
action.escu.creation_date = 2023-03-17
action.escu.modification_date = 2023-03-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Multiple Failed Requests to Access Applications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Multiple Failed Requests to Access Applications - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1550.004", "T1538"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE) OR (eventType=user.authentication.sso outcome.result=SUCCESS) | eval targets=mvzip('target{}.type', 'target{}.displayName', ": ") | eval targets=mvfilter(targets LIKE "AppInstance%") | stats count min(_time) as _time values(outcome.result) as outcome.result dc(eval(if(eventType="policy.evaluate_sign_on",targets,NULL))) as total_challenges sum(eval(if(eventType="user.authentication.sso",1,0))) as total_successes by authenticationContext.externalSessionId targets actor.alternateId client.ipAddress | search total_challenges > 0 | stats min(_time) as _time values(*) as * sum(total_challenges) as total_challenges sum(total_successes) as total_successes values(eval(if("outcome.result"="SUCCESS",targets,NULL))) as success_apps values(eval(if(":outcome.result"!="SUCCESS",targets,NULL))) as no_success_apps by authenticationContext.externalSessionId actor.alternateId client.ipAddress | fillnull | eval ratio=round(total_successes/total_challenges,2), severity="HIGH", mitre_technique_id="T1538", description="actor.alternateId". " from " . "client.ipAddress" . " seen opening " . total_challenges . " chiclets/apps with " . total_successes . " challenges successfully passed" | fields - count, targets | search ratio < 0.5 total_challenges > 2 | `okta_multiple_failed_requests_to_access_applications_filter`

[ESCU - Okta New API Token Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when a new API token is created. Adversaries who have gained access to a privileged account may add a new token for persistence. This analytic uses the eventType system.api_token.create.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when a new API token is created. Adversaries who have gained access to a privileged account may add a new token for persistence. This analytic uses the eventType system.api_token.create.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta New API Token Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = $src_user$ has created a new API token within Okta.
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta New API Token Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when a new API token is created. Adversaries who have gained access to a privileged account may add a new token for persistence. This analytic uses the eventType system.api_token.create.
action.notable.param.rule_title = Okta New API Token Created
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType=system.api_token.create | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by src_user src_ip eventType status | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_new_api_token_created_filter`

[ESCU - Okta New Device Enrolled on Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following anomaly will be generated when a new device is added to an account. Albeit not malicious, risk is set low, but should be monitored. This anomaly utilizes the legacy events from Okta.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following anomaly will be generated when a new device is added to an account. Albeit not malicious, risk is set low, but should be monitored. This anomaly utilizes the legacy events from Okta.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = Tune the risk score as needed based on your organization.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta New Device Enrolled on Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = $user$ has added a new device to their account.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta New Device Enrolled on Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 60, "impact": 40, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType=system.email.new_device_notification.sent_message displayMessage="Send user new device notification email" | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser client.geographicalContext.city client.geographicalContext.country | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_new_device_enrolled_on_account_filter`

[ESCU - Okta Phishing Detection with FastPass Origin Check - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when Okta''s FastPass prevents known phishing sites. When your users are enrolled in FastPass, Okta can provide defenders a high-fidelity signal for when user applications are being targeted by attackers wielding real-time (AiTM) proxies. Okta''s Defensive Cyber Operations team routinely identifies phishing infrastructure configured to imitate an Okta sign-in page and proactively notify Okta customers when suspicious infrastructure we detect appears to be targeting their users. Since March 2020, we have delivered over 1000 notifications to customers.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001", "T1556"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when Okta''s FastPass prevents known phishing sites. When your users are enrolled in FastPass, Okta can provide defenders a high-fidelity signal for when user applications are being targeted by attackers wielding real-time (AiTM) proxies. Okta''s Defensive Cyber Operations team routinely identifies phishing infrastructure configured to imitate an Okta sign-in page and proactively notify Okta customers when suspicious infrastructure we detect appears to be targeting their users. Since March 2020, we have delivered over 1000 notifications to customers.
action.escu.how_to_implement = This search is specific to Okta and requires Okta logs to be ingested in your Splunk deployment.
action.escu.known_false_positives = Fidelity of this is high as Okta is specifying malicious infrastructure. Filter and modify as needed.
action.escu.creation_date = 2023-03-09
action.escu.modification_date = 2023-03-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Phishing Detection with FastPass Origin Check - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = Okta FastPass has prevented $user$ from authenticating to a malicious site.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Phishing Detection with FastPass Origin Check - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1078", "T1078.001", "T1556"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when Okta''s FastPass prevents known phishing sites. When your users are enrolled in FastPass, Okta can provide defenders a high-fidelity signal for when user applications are being targeted by attackers wielding real-time (AiTM) proxies. Okta''s Defensive Cyber Operations team routinely identifies phishing infrastructure configured to imitate an Okta sign-in page and proactively notify Okta customers when suspicious infrastructure we detect appears to be targeting their users. Since March 2020, we have delivered over 1000 notifications to customers.
action.notable.param.rule_title = Okta Phishing Detection with FastPass Origin Check
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType="user.authentication.auth_via_mfa" AND result="FAILURE" AND outcome.reason="FastPass declined phishing attempt" | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_phishing_detection_with_fastpass_origin_check_filter`

[ESCU - Okta Risk Threshold Exceeded - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following correlation will take risk associated with the content from  "Suspicious Okta Activity" and "Okta MFA Exhaustion" analytic stories and tally it up. Once it hits the threshold of 100 (can be changed), it will trigger an a notable. As needed, reduce or raise the risk scores assocaited with the anomaly and TTP analytics tagged to these two analytic stories.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following correlation will take risk associated with the content from  "Suspicious Okta Activity" and "Okta MFA Exhaustion" analytic stories and tally it up. Once it hits the threshold of 100 (can be changed), it will trigger an a notable. As needed, reduce or raise the risk scores assocaited with the anomaly and TTP analytics tagged to these two analytic stories.
action.escu.how_to_implement = Ensure "Suspicious Okta Activity" and "Okta MFA Exhaustion" analytic stories are enabled. TTP may be set to Notables for point detections, anomaly should not be notables but risk generators. The correlation relies on risk before generating a notable. Modify the value as needed. Default threshold is 100. This value may need to be increased based on activity in your environment.
action.escu.known_false_positives = False positives will be limited to the amount of events generated by the analytics tied to the stories. Analytics will need to be tesetd and tuned, risk score reduced, as needed based on organization.
action.escu.creation_date = 2022-09-29
action.escu.modification_date = 2022-09-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Risk Threshold Exceeded - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity", "Okta MFA Exhaustion"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Okta Risk Threshold Exceeded - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity", "Okta MFA Exhaustion"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1078", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following correlation will take risk associated with the content from  "Suspicious Okta Activity" and "Okta MFA Exhaustion" analytic stories and tally it up. Once it hits the threshold of 100 (can be changed), it will trigger an a notable. As needed, reduce or raise the risk scores assocaited with the anomaly and TTP analytics tagged to these two analytic stories.
action.notable.param.rule_title = RBA: Okta Risk Threshold Exceeded
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count,values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk by All_Risk.risk_object,All_Risk.risk_object_type All_Risk.analyticstories | `drop_dm_object_name("All_Risk")` | eval "annotations.mitre_attack"="annotations.mitre_attack.mitre_technique_id", risk_threshold=100 | where All_Risk.analyticstories IN ("Suspicious Okta Activity", "Okta MFA Exhaustion")  risk_score > risk_threshold | `get_risk_severity(risk_score)` | `okta_risk_threshold_exceeded_filter`

[ESCU - Okta Suspicious Activity Reported - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following event is generated when an associate receives an email from Okta asking if a login attempt was suspicious or not. If the associate identifies it as suspicious an event is generated and should be reviewed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following event is generated when an associate receives an email from Okta asking if a login attempt was suspicious or not. If the associate identifies it as suspicious an event is generated and should be reviewed.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested. It also requires that suspicious activity reporting is enabled and associates are trained to submit.
action.escu.known_false_positives = False positives should be limited as this is a high fidelity marker.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Suspicious Activity Reported - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = The following $user$ has reported a suspicious login activity.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Suspicious Activity Reported - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following event is generated when an associate receives an email from Okta asking if a login attempt was suspicious or not. If the associate identifies it as suspicious an event is generated and should be reviewed.
action.notable.param.rule_title = Okta Suspicious Activity Reported
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType=user.account.report_suspicious_activity_by_enduser | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser client.geographicalContext.city client.geographicalContext.country | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_suspicious_activity_reported_filter`

[ESCU - Okta Suspicious Use of a Session Cookie - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic looks for one or more policy evaluation events in which multiple client values (IP, User Agent, etc.) change associated to the same Device Token for a specific user. A detection opportunity arises when an adversary attempts to reuse a stolen web session cookie. \
* Retrieves policy evaluation events from successful authentication events. \
* Aggregates/Groups by Device Token and User, providing the first policy evaluation event in the search window. \
* Evaluates whether there is more than one IP and whether there is more than one OS or browser for each combination of User/Device Token.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1539"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic looks for one or more policy evaluation events in which multiple client values (IP, User Agent, etc.) change associated to the same Device Token for a specific user. A detection opportunity arises when an adversary attempts to reuse a stolen web session cookie. \
* Retrieves policy evaluation events from successful authentication events. \
* Aggregates/Groups by Device Token and User, providing the first policy evaluation event in the search window. \
* Evaluates whether there is more than one IP and whether there is more than one OS or browser for each combination of User/Device Token.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta:im2 logs to be ingested.
action.escu.known_false_positives = False positives may be present based on organization size and configuration of Okta.
action.escu.creation_date = 2023-03-17
action.escu.modification_date = 2023-03-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Suspicious Use of a Session Cookie - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Suspicious Use of a Session Cookie - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1539"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType IN (policy.evaluate_sign_on) outcome.result IN (ALLOW, SUCCESS) | stats earliest(_time) as _time values(client.ipAddress) as src_ip values(client.userAgent.rawUserAgent) as user_agent values(client.userAgent.os) as userAgentOS_list values(client.geographicalContext.city) as city values(client.userAgent.browser) as userAgentBrowser_list values(device.os_platform) as okta_device_os dc(client.userAgent.browser) as dc_userAgentBrowser dc(client.userAgent.os) as dc_userAgentOS dc(client.ipAddress) as dc_src_ip values(outcome.reason) as reason by debugContext.debugData.dtHash actor.alternateId | where dc_src_ip>1 AND (dc_userAgentOS>1 OR dc_userAgentBrowser>1) | `okta_suspicious_use_of_a_session_cookie_filter`

[ESCU - Okta ThreatInsight Login Failure with High Unknown users - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Okta's ThreatInsight to identify "Login failures with high unknown users count" and any included secondary outcome reasons. This event will trigger when a brute force attempt occurs with unknown usernames attempted.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001", "T1110.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Okta's ThreatInsight to identify "Login failures with high unknown users count" and any included secondary outcome reasons. This event will trigger when a brute force attempt occurs with unknown usernames attempted.
action.escu.how_to_implement = This search is specific to Okta and requires Okta logs to be ingested in your Splunk deployment.
action.escu.known_false_positives = Fidelity of this is high as it is Okta ThreatInsight. Filter and modify as needed.
action.escu.creation_date = 2023-03-09
action.escu.modification_date = 2023-03-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta ThreatInsight Login Failure with High Unknown users - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = Okta ThreatInsight has detected or prevented a high number of login failures.
action.risk.param._risk = [{"risk_object_field": "outcome.reason", "risk_object_type": "other", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta ThreatInsight Login Failure with High Unknown users - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 100, "impact": 50, "mitre_attack": ["T1078", "T1078.001", "T1110.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Okta's ThreatInsight to identify "Login failures with high unknown users count" and any included secondary outcome reasons. This event will trigger when a brute force attempt occurs with unknown usernames attempted.
action.notable.param.rule_title = Okta ThreatInsight Login Failure with High Unknown users
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType="security.threat.detected" AND outcome.reason="Login failures with high unknown users count*" | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_threatinsight_login_failure_with_high_unknown_users_filter`

[ESCU - Okta ThreatInsight Suspected PasswordSpray Attack - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Okta's ThreatInsight to identify "PasswordSpray" and any included secondary outcome reasons. This event will trigger when a brute force attempt occurs with unknown usernames attempted.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001", "T1110.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Okta's ThreatInsight to identify "PasswordSpray" and any included secondary outcome reasons. This event will trigger when a brute force attempt occurs with unknown usernames attempted.
action.escu.how_to_implement = This search is specific to Okta and requires Okta logs to be ingested in your Splunk deployment.
action.escu.known_false_positives = Fidelity of this is high as it is Okta ThreatInsight. Filter and modify as needed.
action.escu.creation_date = 2023-03-09
action.escu.modification_date = 2023-03-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta ThreatInsight Suspected PasswordSpray Attack - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = Okta ThreatInsight has detected or prevented a PasswordSpray attack.
action.risk.param._risk = [{"risk_object_field": "outcome.reason", "risk_object_type": "other", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta ThreatInsight Suspected PasswordSpray Attack - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 100, "impact": 60, "mitre_attack": ["T1078", "T1078.001", "T1110.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Okta's ThreatInsight to identify "PasswordSpray" and any included secondary outcome reasons. This event will trigger when a brute force attempt occurs with unknown usernames attempted.
action.notable.param.rule_title = Okta ThreatInsight Suspected PasswordSpray Attack
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType="security.threat.detected" AND outcome.reason="Password Spray" | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_threatinsight_suspected_passwordspray_attack_filter`

[ESCU - Okta ThreatInsight Threat Detected - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following anomaly relies on the Okta ThreatInsight identification of a threat. Use this to drive risk up by src_ip or add additional fields to track. Identification may include password spraying, login failures and Login failures with high unknown users count.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following anomaly relies on the Okta ThreatInsight identification of a threat. Use this to drive risk up by src_ip or add additional fields to track. Identification may include password spraying, login failures and Login failures with high unknown users count.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed.
action.escu.creation_date = 2022-09-21
action.escu.modification_date = 2022-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta ThreatInsight Threat Detected - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = The following $src_ip$ has been conisidered a threat by Okta ThreatInsight.
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta ThreatInsight Threat Detected - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` eventType IN (security.threat.detected, security.internal.threat.detected) | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats values(src_ip) count by signature eventType displayMessage client.device city state country user_agent outcome.reason outcome.result severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_threatinsight_threat_detected_filter`

[ESCU - Okta Two or More Rejected Okta Pushes - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an account that has rejected more than 2 Push notifications in a 10 minute window. Modify this query for your environment by upping the count or time window.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an account that has rejected more than 2 Push notifications in a 10 minute window. Modify this query for your environment by upping the count or time window.
action.escu.how_to_implement = This analytic is specific to Okta and requires Okta logs to be ingested.
action.escu.known_false_positives = False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete.
action.escu.creation_date = 2022-09-27
action.escu.modification_date = 2022-09-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta Two or More Rejected Okta Pushes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity", "Okta MFA Exhaustion"]
action.risk = 1
action.risk.param._risk_message = $user$ account has rejected multiple Okta pushes.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta Two or More Rejected Okta Pushes - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity", "Okta MFA Exhaustion"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an account that has rejected more than 2 Push notifications in a 10 minute window. Modify this query for your environment by upping the count or time window.
action.notable.param.rule_title = Okta Two or More Rejected Okta Pushes
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` outcome.reason="User rejected Okta push verify" OR (debugContext.debugData.factor="OKTA_VERIFY_PUSH" outcome.result=FAILURE legacyEventType="core.user.factor.attempt_fail" "target{}.detailEntry.methodTypeUsed"="Get a push notification") | bin _time as bin_time span=10m | eval user=coalesce(actor.alternateId,user), user=mvindex(split(user, "@"), 0), event_time = _time | stats earliest(event_time) as event_time, min(_time) as firsttime max(_time) as lasttime values(client.ipAddress) as client.ipAddress, values(outcome.reason) as outcome, values(src_ip) AS src_ip, values(client.userAgent.rawUserAgent) as user_agent, values(eventType) as eventType, values(outcome.result) as action, values(legacyEventType) as legacyEventType values(index) as idx, values(sourcetype) as st count by bin_time user host | rename bin_time as timeWindow | convert ctime(*timeWindow) ctime(firsttime) ctime(lasttime) | where count >= 2 | `okta_two_or_more_rejected_okta_pushes_filter`

[ESCU - Okta User Logins From Multiple Cities - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects logins from the same user from different cities in a 24 hour period.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects logins from the same user from different cities in a 24 hour period.
action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
action.escu.known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Okta User Logins From Multiple Cities - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Okta User Logins From Multiple Cities - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1

[ESCU - Path traversal SPL injection - Rule]
action.escu = 0
action.escu.enabled = 1
description = On May 3rd, 2022, Splunk published a security advisory for a Path traversal in search parameter that can potentiall allow SPL injection. An attacker can cause the application to load data from incorrect endpoints, urls leading to outcomes such as running arbitrary SPL queries.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1083"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = On May 3rd, 2022, Splunk published a security advisory for a Path traversal in search parameter that can potentiall allow SPL injection. An attacker can cause the application to load data from incorrect endpoints, urls leading to outcomes such as running arbitrary SPL queries.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index. This search will provide search UI requests with path traversal parameter ("../../../../../../../../../") which shows exploitation attempts. This detection is meant for on premise environments, and if executed on internet facing servers without a WAF may produce a lot of results. This detection will not work against obfuscated path traversal requests.
action.escu.known_false_positives = This search may find additional path traversal exploitation attempts.
action.escu.creation_date = 2022-04-29
action.escu.modification_date = 2022-04-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Path traversal SPL injection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Path traversal exploitation attempt from $clientip$
action.risk.param._risk = [{"risk_object_field": "clientip", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Path traversal SPL injection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2022-26889"], "impact": 50, "mitre_attack": ["T1083"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = On May 3rd, 2022, Splunk published a security advisory for a Path traversal in search parameter that can potentiall allow SPL injection. An attacker can cause the application to load data from incorrect endpoints, urls leading to outcomes such as running arbitrary SPL queries.
action.notable.param.rule_title = Path traversal SPL injection
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `path_traversal_spl_injection` | search "\/..\/..\/..\/..\/..\/..\/..\/..\/..\/"  | stats count by status clientip method uri_path uri_query | `path_traversal_spl_injection_filter`

[ESCU - Splunk Absolute Path Traversal Using runshellscript - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions lower than 8.2.12, 9.0.6, and 9.1.1, an attacker can exploit an absolute path traversal to execute arbitrary code that is located on a separate disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1083"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions lower than 8.2.12, 9.0.6, and 9.1.1, an attacker can exploit an absolute path traversal to execute arbitrary code that is located on a separate disk.
action.escu.how_to_implement = Must have access to internal indexes. Only applies to Splunk on Windows versions.
action.escu.known_false_positives = The command runshellscript can be used for benign purposes. Analyst will have to review the searches and determined maliciousness specially by looking at targeted script.
action.escu.creation_date = 2023-09-05
action.escu.modification_date = 2023-09-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Absolute Path Traversal Using runshellscript - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Absolute Path Traversal Using runshellscript - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2023-40597"], "impact": 50, "mitre_attack": ["T1083"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunk_python` *runshellscript*  | eval log_split=split(_raw, "runshellscript: ") | eval array_raw = mvindex(log_split,1) | eval data_cleaned=replace(replace(replace(array_raw,"\[",""),"\]",""),"'","") | eval array_indices=split(data_cleaned,",") | eval runshellscript_args_count=mvcount(array_indices) | where runshellscript_args_count = 10 | eval interpreter=mvindex(array_indices,0) | eval targetScript=mvindex(array_indices,1) | eval targetScript != "*C:*" | stats count min(_time) as firstTime max(_time) as lastTime by splunk_server interpreter targetScript | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `splunk_absolute_path_traversal_using_runshellscript_filter`

[ESCU - Splunk Account Discovery Drilldown Dashboard Disclosure - Rule]
action.escu = 0
action.escu.enabled = 1
description = Splunk drilldown vulnerability disclosure in Dashboard application that can potentially allow exposure of tokens from privilege users. An attacker can create dashboard and share it to privileged user (admin) and detokenize variables using external urls within dashboards drilldown function.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Splunk drilldown vulnerability disclosure in Dashboard application that can potentially allow exposure of tokens from privilege users. An attacker can create dashboard and share it to privileged user (admin) and detokenize variables using external urls within dashboards drilldown function.
action.escu.how_to_implement = This search uses REST function to query for dashboards with environment variables present in URL options.
action.escu.known_false_positives = This search may reveal non malicious URLs with environment variables used in organizations.
action.escu.creation_date = 2022-08-02
action.escu.modification_date = 2022-08-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Account Discovery Drilldown Dashboard Disclosure - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Potential exposure of environment variables from url embedded in dashboard
action.risk.param._risk = [{"risk_object_field": "author", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Account Discovery Drilldown Dashboard Disclosure - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2022-37438"], "impact": 50, "mitre_attack": ["T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Splunk drilldown vulnerability disclosure in Dashboard application that can potentially allow exposure of tokens from privilege users. An attacker can create dashboard and share it to privileged user (admin) and detokenize variables using external urls within dashboards drilldown function.
action.notable.param.rule_title = Splunk Account Discovery Drilldown Dashboard Disclosure
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | rest splunk_server=local /servicesNS/-/-/data/ui/views | search eai:data="*$env:*" eai:data="*url*" eai:data="*options*" | rename author AS Author eai:acl.sharing AS Permissions eai:appName AS App eai:data AS "Dashboard XML" | fields Author Permissions App "Dashboard XML" | `splunk_account_discovery_drilldown_dashboard_disclosure_filter`

[ESCU - Splunk Code Injection via custom dashboard leading to RCE - Rule]
action.escu = 0
action.escu.enabled = 1
description = This hunting search provides information about a vulnerability in Splunk Enterprise versions below 8.2.9, 8.1.12, 9.0.2, where an authenticated user can execute arbitrary code via the dashboard pdf generation component. Please review events with file=export in the _internal index for the potential targets of exploitation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This hunting search provides information about a vulnerability in Splunk Enterprise versions below 8.2.9, 8.1.12, 9.0.2, where an authenticated user can execute arbitrary code via the dashboard pdf generation component. Please review events with file=export in the _internal index for the potential targets of exploitation.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index.
action.escu.known_false_positives = Not all exports and downloads are malicious, special attention must be put as well on /en-US/splunkd/__raw/services/pdfgen/render in the context of this search.
action.escu.creation_date = 2022-10-11
action.escu.modification_date = 2022-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Code Injection via custom dashboard leading to RCE - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Code Injection via custom dashboard leading to RCE - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2022-43571"], "impact": 50, "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_ui` uri_path=*/data/ui/views/* OR uri_path=*saved/searches/* | dedup uri_path | eval URL=urldecode("uri_path")| rex field=URL "\/saved\/searches\/(?<NAME>[^\/]*)" | rex field=URL "\/data\/ui\/views\/(?<NAME1>[^\/]*)" | eval NAME=NAME."( Saved Search )",NAME1=NAME1."( Dashboard )" | eval NAME=coalesce(NAME,NAME1) | eval STATUS=case(match(status,"2\d+"),"SUCCESS",match(status,"3\d+"),"REDIRECTION",match(status,"4\d+") OR match(status,"5\d+"),"ERROR") | stats list(NAME) as DASHBOARD_TITLE,list(method) as HTTP_METHOD,list(status) as Status_Code,list(STATUS) as STATUS by user | rename user as User | `splunk_code_injection_via_custom_dashboard_leading_to_rce_filter`

[ESCU - Splunk Command and Scripting Interpreter Delete Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of the risky command - Delete - that may be utilized in Splunk to delete some or all data queried for. In order to use Delete in Splunk, one must be assigned the role. This is typically not used and should generate an anomaly if it is used.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Splunk_Audit"]
action.escu.eli5 = The following analytic identifies the use of the risky command - Delete - that may be utilized in Splunk to delete some or all data queried for. In order to use Delete in Splunk, one must be assigned the role. This is typically not used and should generate an anomaly if it is used.
action.escu.how_to_implement = To successfully implement this search acceleration is recommended against the Search_Activity datamodel that runs against the splunk _audit index. In addition, this analytic requires the Common Information Model App which includes the Splunk Audit Datamodel https://splunkbase.splunk.com/app/1621/.
action.escu.known_false_positives = False positives may be present if this command is used as a common practice. Filter as needed.
action.escu.creation_date = 2022-05-27
action.escu.modification_date = 2022-05-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Command and Scripting Interpreter Delete Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = $user$ executed the 'delete' command, if this is unexpected it should be reviewed.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 27}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Command and Scripting Interpreter Delete Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 30, "cve": ["CVE-2022-32154"], "impact": 90, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where Search_Activity.search IN ("*| delete*") Search_Activity.search_type=adhoc Search_Activity.user!=splunk-system-user by Search_Activity.search Search_Activity.info Search_Activity.total_run_time Search_Activity.user Search_Activity.search_type | `drop_dm_object_name(Search_Activity)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_command_and_scripting_interpreter_delete_usage_filter`

[ESCU - Splunk Command and Scripting Interpreter Risky Commands - Rule]
action.escu = 0
action.escu.enabled = 1
description = The Splunk platform contains built-in search processing language (SPL) safeguards to warn you when you are about to unknowingly run a search that contains commands that might be a security risk. This warning appears when you click a link or type a URL that loads a search that contains risky commands. The warning does not appear when you create ad hoc searches. This warning alerts you to the possibility of unauthorized actions by a malicious user. Unauthorized actions include - Copying or transferring data (data exfiltration), Deleting data and Overwriting data. All risky commands may be found here https://docs.splunk.com/Documentation/Splunk/latest/Security/SPLsafeguards#Commands_that_trigger_the_warninga. A possible scenario when this might occur is when a malicious actor creates a search that includes commands that exfiltrate or damage data. The malicious actor then sends an unsuspecting user a link to the search. The URL contains a query string (q) and a search identifier (sid), but the sid is not valid. The malicious actor hopes the user will use the link and the search will run. During analysis, pivot based on user name and filter any user or queries not needed. Queries ran from a dashboard are seen as adhoc queries. When a query runs from a dashboard it will not show in audittrail logs the source dashboard name. The query defaults to adhoc and no Splunk system user activity. In addition, modify this query by removing key commands that generate too much noise, or too little, and create separate queries with higher confidence to alert on.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Splunk_Audit"]
action.escu.eli5 = The Splunk platform contains built-in search processing language (SPL) safeguards to warn you when you are about to unknowingly run a search that contains commands that might be a security risk. This warning appears when you click a link or type a URL that loads a search that contains risky commands. The warning does not appear when you create ad hoc searches. This warning alerts you to the possibility of unauthorized actions by a malicious user. Unauthorized actions include - Copying or transferring data (data exfiltration), Deleting data and Overwriting data. All risky commands may be found here https://docs.splunk.com/Documentation/Splunk/latest/Security/SPLsafeguards#Commands_that_trigger_the_warninga. A possible scenario when this might occur is when a malicious actor creates a search that includes commands that exfiltrate or damage data. The malicious actor then sends an unsuspecting user a link to the search. The URL contains a query string (q) and a search identifier (sid), but the sid is not valid. The malicious actor hopes the user will use the link and the search will run. During analysis, pivot based on user name and filter any user or queries not needed. Queries ran from a dashboard are seen as adhoc queries. When a query runs from a dashboard it will not show in audittrail logs the source dashboard name. The query defaults to adhoc and no Splunk system user activity. In addition, modify this query by removing key commands that generate too much noise, or too little, and create separate queries with higher confidence to alert on.
action.escu.how_to_implement = To successfully implement this search acceleration is recommended against the Search_Activity datamodel that runs against the splunk _audit index.  In addition, this analytic requires the Common Information Model App which includes the Splunk Audit Datamodel https://splunkbase.splunk.com/app/1621/. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = False positives will be present until properly filtered by Username and search name.
action.escu.creation_date = 2022-05-23
action.escu.modification_date = 2022-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Command and Scripting Interpreter Risky Commands - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Command and Scripting Interpreter Risky Commands - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 40, "cve": ["CVE-2022-32154"], "impact": 50, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where Search_Activity.search IN ("*| runshellscript *", "*| collect *","*| delete *", "*| fit *", "*| outputcsv *", "*| outputlookup *", "*| run *", "*| script *", "*| sendalert *", "*| sendemail *", "*| tscolle*") Search_Activity.search_type=adhoc Search_Activity.user!=splunk-system-user by Search_Activity.search Search_Activity.info Search_Activity.total_run_time Search_Activity.user Search_Activity.search_type | `drop_dm_object_name(Search_Activity)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_command_and_scripting_interpreter_risky_commands_filter`

[ESCU - Splunk Command and Scripting Interpreter Risky SPL MLTK - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection utilizes machine learning model named "risky_command_abuse" trained from "Splunk Command and Scripting Interpreter Risky SPL MLTK Baseline". It should be scheduled to run hourly to detect whether a user has run searches containing risky SPL from this list https://docs.splunk.com/Documentation/Splunk/latest/Security/SPLsafeguards#Commands_that_trigger_the_warninga with abnormally long running time in the past one hour, comparing with his/her past seven days history. This search uses the trained baseline to infer whether a search is an outlier (isOutlier ~= 1.0) or not (isOutlier~= 0.0)
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Splunk_Audit"]
action.escu.eli5 = This detection utilizes machine learning model named "risky_command_abuse" trained from "Splunk Command and Scripting Interpreter Risky SPL MLTK Baseline". It should be scheduled to run hourly to detect whether a user has run searches containing risky SPL from this list https://docs.splunk.com/Documentation/Splunk/latest/Security/SPLsafeguards#Commands_that_trigger_the_warninga with abnormally long running time in the past one hour, comparing with his/her past seven days history. This search uses the trained baseline to infer whether a search is an outlier (isOutlier ~= 1.0) or not (isOutlier~= 0.0)
action.escu.how_to_implement = This detection depends on MLTK app which can be found here - https://splunkbase.splunk.com/app/2890/ and the Splunk Audit datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. Baseline model needs to be built using "Splunk Command and Scripting Interpreter Risky SPL MLTK Baseline" before this search can run. Please note that the current search only finds matches exactly one space between separator bar and risky commands.
action.escu.known_false_positives = If the run time of a search exceeds the boundaries of outlier defined by the fitted density function model, false positives can occur, incorrectly labeling a long running search as potentially risky.
action.escu.creation_date = 2022-05-27
action.escu.modification_date = 2022-05-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Command and Scripting Interpreter Risky SPL MLTK - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Abnormally long run time for risk SPL command seen by user $(Search_Activity.user).
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Command and Scripting Interpreter Risky SPL MLTK - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 40, "cve": ["CVE-2022-32154"], "impact": 50, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats sum(Search_Activity.total_run_time) AS run_time, values(Search_Activity.search) as searches, count FROM datamodel=Splunk_Audit.Search_Activity WHERE (Search_Activity.user!="") AND (Search_Activity.total_run_time>1) AND (earliest=-1h@h latest=now) AND (Search_Activity.search IN ("*| runshellscript *", "*| collect *","*| delete *", "*| fit *", "*| outputcsv *", "*| outputlookup *", "*| run *", "*| script *", "*| sendalert *", "*| sendemail *", "*| tscolle*")) AND (Search_Activity.search_type=adhoc) AND (Search_Activity.user!=splunk-system-user) BY _time, Search_Activity.user span=1h | apply risky_command_abuse | fields _time, Search_Activity.user, searches, run_time, IsOutlier(run_time) | rename IsOutlier(run_time) as isOutlier, _time as timestamp | where isOutlier>0.5 | `splunk_command_and_scripting_interpreter_risky_spl_mltk_filter`

[ESCU - Splunk csrf in the ssg kvstore client endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a cross-site request forgery in the Splunk Secure Gateway (SSG) app in the kvstore_client endpoint allows for updating SSG KV store collections via a GET request. SSG is a Splunk Built app included by default with Splunk Enterprise. The vulnerability affects instances with SSG and Splunk Web enabled. This hunting search provides information on affected server specific method and post data that may reveal exploitation of this vulnerability.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a cross-site request forgery in the Splunk Secure Gateway (SSG) app in the kvstore_client endpoint allows for updating SSG KV store collections via a GET request. SSG is a Splunk Built app included by default with Splunk Enterprise. The vulnerability affects instances with SSG and Splunk Web enabled. This hunting search provides information on affected server specific method and post data that may reveal exploitation of this vulnerability.
action.escu.how_to_implement = Requires access to internal index.
action.escu.known_false_positives = This hunting search only applies to the affected versions and setup mentioned in the description of this search, it does not extract payload so it requires manual investigation after executing search. This search will produce false positives.
action.escu.creation_date = 2023-02-14
action.escu.modification_date = 2023-02-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk csrf in the ssg kvstore client endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Potential CSRF exploitation attempt from $splunk_server$
action.risk.param._risk = [{"risk_object_field": "splunk_server", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk csrf in the ssg kvstore client endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-22942"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a cross-site request forgery in the Splunk Secure Gateway (SSG) app in the kvstore_client endpoint allows for updating SSG KV store collections via a GET request. SSG is a Splunk Built app included by default with Splunk Enterprise. The vulnerability affects instances with SSG and Splunk Web enabled. This hunting search provides information on affected server specific method and post data that may reveal exploitation of this vulnerability.
action.notable.param.rule_title = Splunk csrf in the ssg kvstore client endpoint
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkda` uri_path="/en-US/splunkd/__raw/services/ssg/kvstore_client" method="GET" delete_field_value="spacebridge_server" status="200"  | table splunk_server status uri delete_field_value method post_data  | `splunk_csrf_in_the_ssg_kvstore_client_endpoint_filter`

[ESCU - Splunk Data exfiltration from Analytics Workspace using sid query - Rule]
action.escu = 0
action.escu.enabled = 1
description = This hunting search allows operator to discover attempts to exfiltrate data by executing a prepositioned malicious search ID in Analytic Workspace in Splunk Enterprise versions 8.2.9,8.1.12,9.0.2. The attack is browser-based. It requires the attacker to compel a victim to initiate a request within their browser (phishing). The attacker cannot exploit the vulnerability at will.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1567"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This hunting search allows operator to discover attempts to exfiltrate data by executing a prepositioned malicious search ID in Analytic Workspace in Splunk Enterprise versions 8.2.9,8.1.12,9.0.2. The attack is browser-based. It requires the attacker to compel a victim to initiate a request within their browser (phishing). The attacker cannot exploit the vulnerability at will.
action.escu.how_to_implement = The vulnerability affects only instances with Splunk Web Enabled.  After running this search, please run "Splunk Command and Scripting Interpreter Risky SPL MLTK" to gain more insight into potentially risky commands which could lead to data exfiltration.
action.escu.known_false_positives = This search may produce false positives.  This detection does not require you to ingest any new data. The detection does require the ability to search the _audit index. Special attention must be paid to "/en-US/app/search/analytics_workspace?sid=[sid]" which is where the malicious code will be inserted to trigger attack at victim.
action.escu.creation_date = 2022-11-1
action.escu.modification_date = 2022-11-1
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Data exfiltration from Analytics Workspace using sid query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Data exfiltration from Analytics Workspace using sid query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2022-43566"], "impact": 50, "mitre_attack": ["T1567"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `audit_searches` info=granted search NOT ("audit_searches") search NOT ("security_content_summariesonly") AND ((search="*mstats*[*]*" AND provenance="N/A") OR (search="*mstats*\\\"*[*]*\\\"*"))| eval warning=if(match(search,"\\\\\""), "POTENTIAL INJECTION STAGING", "POTENTIAL INJECTION EXECUTION") | table search, user, warning, timestamp | `splunk_data_exfiltration_from_analytics_workspace_using_sid_query_filter`

[ESCU - Splunk Digital Certificates Infrastructure Version - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will check the TLS validation is properly configured on the search head it is run from as well as its search peers after Splunk version 9. Other components such as additional search heads or anything this rest command cannot be distributed to will need to be manually checked.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1587.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will check the TLS validation is properly configured on the search head it is run from as well as its search peers after Splunk version 9. Other components such as additional search heads or anything this rest command cannot be distributed to will need to be manually checked.
action.escu.how_to_implement = The user running this search is required to have a permission allowing them to dispatch REST requests to indexers (the `dispatch_rest_to_indexers` capability) in some architectures. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = No known at this time.
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Digital Certificates Infrastructure Version - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Digital Certificates Infrastructure Version - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2022-32153"], "impact": 50, "mitre_attack": ["T1587.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | rest /services/server/info | table splunk_server version server_roles | join splunk_server [| rest /servicesNS/nobody/search/configs/conf-server/ search="sslConfig"| table splunk_server sslVerifyServerCert sslVerifyServerName serverCert] | fillnull value="Not Set" | rename sslVerifyServerCert as "Server.conf:SslConfig:sslVerifyServerCert", sslVerifyServerName as "Server.conf:SslConfig:sslVerifyServerName", serverCert as "Server.conf:SslConfig:serverCert" | `splunk_digital_certificates_infrastructure_version_filter`

[ESCU - Splunk Digital Certificates Lack of Encryption - Rule]
action.escu = 0
action.escu.enabled = 1
description = On June 14th, 2022, Splunk released a security advisory relating to the authentication that happens between Universal Forwarders and Deployment Servers. In some circumstances, an unauthenticated client can download forwarder bundles from the Deployment Server. In other circumstances, a client may be allowed to publish a forwarder bundle to other clients, which may allow for arbitrary code execution. The fixes for these require upgrading to at least Splunk 9.0 on the forwarder as well. This is a great opportunity to configure TLS across the environment. This search looks for forwarders that are not using TLS and adds risk to those entities.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1587.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = On June 14th, 2022, Splunk released a security advisory relating to the authentication that happens between Universal Forwarders and Deployment Servers. In some circumstances, an unauthenticated client can download forwarder bundles from the Deployment Server. In other circumstances, a client may be allowed to publish a forwarder bundle to other clients, which may allow for arbitrary code execution. The fixes for these require upgrading to at least Splunk 9.0 on the forwarder as well. This is a great opportunity to configure TLS across the environment. This search looks for forwarders that are not using TLS and adds risk to those entities.
action.escu.how_to_implement = This anomaly search looks for forwarder connections that are not currently using TLS. It then presents the source IP, the type of forwarder, and the version of the forwarder. You can also remove the "ssl=false" argument from the initial stanza in order to get a full list of all your forwarders that are sending data, and the version of Splunk software they are running, for audit purposes. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = None at this time
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Digital Certificates Lack of Encryption - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = $hostname$ is not using TLS when forwarding data
action.risk.param._risk = [{"risk_object_field": "hostname", "risk_object_type": "system", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Digital Certificates Lack of Encryption - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2022-32151"], "impact": 25, "mitre_attack": ["T1587.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd` group="tcpin_connections" ssl="false" | stats values(sourceIp) latest(fwdType) latest(version) by hostname | `splunk_digital_certificates_lack_of_encryption_filter`

[ESCU - Splunk DoS Using Malformed SAML Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions lower than 9.0.6, and 8.2.12, an attacker can send a malformed security assertion markup language SAML request to the /saml/acs REST endpoint which can cause a denial of service through a crash or hang of the Splunk daemon.The SAML extensible markup language (XML) parser does not fail SAML signature validation when the attacker modifies the URI in the SAML request. Instead it attempts to access the modified URI, which causes the Splunk daemon to crash or hang.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1498"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions lower than 9.0.6, and 8.2.12, an attacker can send a malformed security assertion markup language SAML request to the /saml/acs REST endpoint which can cause a denial of service through a crash or hang of the Splunk daemon.The SAML extensible markup language (XML) parser does not fail SAML signature validation when the attacker modifies the URI in the SAML request. Instead it attempts to access the modified URI, which causes the Splunk daemon to crash or hang.
action.escu.how_to_implement = To run this search, you must have access to the _internal index.
action.escu.known_false_positives = This search will show false positives. The analyst must look for errors and a pointer indicating a malicious file.
action.escu.creation_date = 2023-09-05
action.escu.modification_date = 2023-09-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk DoS Using Malformed SAML Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk DoS Using Malformed SAML Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 30, "impact": 50, "mitre_attack": ["T1498"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd` event_message=*error* expr=*xpointer* | stats count min(_time) as firstTime max(_time) as lastTime by component expr splunk_server event_message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `splunk_dos_using_malformed_saml_request_filter`

[ESCU - Splunk DOS Via Dump SPL Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 9.0.5, 8.2.11, and 8.1.14, an attacker can exploit a vulnerability in the dump SPL command to cause a Denial of Service by crashing the Splunk daemon.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1499.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 9.0.5, 8.2.11, and 8.1.14, an attacker can exploit a vulnerability in the dump SPL command to cause a Denial of Service by crashing the Splunk daemon.
action.escu.how_to_implement = This search does not require additional ingestion of data. Requires the ability to search _internal index and monitor segmentation faults.
action.escu.known_false_positives = Segmentation faults may occur due to other causes, so this search may produce false positives
action.escu.creation_date = 2023-05-10
action.escu.modification_date = 2023-05-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk DOS Via Dump SPL Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk DOS Via Dump SPL Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1499.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunk_crash_log` "*Segmentation fault*" | stats count by host _time | `splunk_dos_via_dump_spl_command_filter`

[ESCU - Splunk DoS via Malformed S2S Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = On March 24th, 2022, Splunk published a security advisory for a possible Denial of Service stemming from the lack of validation in a specific key-value field in the Splunk-to-Splunk (S2S) protocol. This detection will alert on attempted exploitation in patched versions of Splunk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1498"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = On March 24th, 2022, Splunk published a security advisory for a possible Denial of Service stemming from the lack of validation in a specific key-value field in the Splunk-to-Splunk (S2S) protocol. This detection will alert on attempted exploitation in patched versions of Splunk.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index. This detection will only find attempted exploitation on versions of Splunk already patched for CVE-2021-3422.
action.escu.known_false_positives = None.
action.escu.creation_date = 2022-03-24
action.escu.modification_date = 2022-03-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk DoS via Malformed S2S Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = An attempt to exploit CVE-2021-3422 was detected from $src$ against $host$
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 50}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk DoS via Malformed S2S Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-3422"], "impact": 50, "mitre_attack": ["T1498"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = On March 24th, 2022, Splunk published a security advisory for a possible Denial of Service stemming from the lack of validation in a specific key-value field in the Splunk-to-Splunk (S2S) protocol. This detection will alert on attempted exploitation in patched versions of Splunk.
action.notable.param.rule_title = Splunk DoS via Malformed S2S Request
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd` log_level="ERROR" component="TcpInputProc" thread_name="FwdDataReceiverThread" "Invalid _meta atom" | table host, src | `splunk_dos_via_malformed_s2s_request_filter`

[ESCU - Splunk DOS via printf search function - Rule]
action.escu = 0
action.escu.enabled = 1
description = This hunting search provides information on detecting a vulnerability In Splunk Enterprise versions lower than 8.1.14, 8.2.12, 9.0.6, and 9.1.1, an attacker can use the printf SPL function to perform a denial of service against the Splunk Enterprise instance.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1499.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This hunting search provides information on detecting a vulnerability In Splunk Enterprise versions lower than 8.1.14, 8.2.12, 9.0.6, and 9.1.1, an attacker can use the printf SPL function to perform a denial of service against the Splunk Enterprise instance.
action.escu.how_to_implement = This search requires the ability to search internal indexes.
action.escu.known_false_positives = This search may produces false positives, analyst most focuse in the use of printf conversion function of eval to craft an expression that splunkd cannot interpret correctly causing it to crash.
action.escu.creation_date = 2023-08-30
action.escu.modification_date = 2023-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk DOS via printf search function - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk DOS via printf search function - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2023-40594"], "impact": 100, "mitre_attack": ["T1499.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `audit_searches` "*makeresults * eval * fieldformat *printf*" user!="splunk_system_user" search!="*audit_searches*" | stats count by user splunk_server host search | convert ctime(*time) |`splunk_dos_via_printf_search_function_filter`

[ESCU - Splunk Edit User Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = A low-privilege user who holds a role that has the edit_user capability assigned to it can escalate their privileges to that of the admin user by providing specially crafted web requests.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = A low-privilege user who holds a role that has the edit_user capability assigned to it can escalate their privileges to that of the admin user by providing specially crafted web requests.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _audit index. This detection may assist in efforts to discover abuse of edit_user privilege.
action.escu.known_false_positives = This search may produce false positives as password changing actions may be part of normal behavior. Operator will need to investigate these actions in order to discern exploitation attempts.
action.escu.creation_date = 2023-05-23
action.escu.modification_date = 2023-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Edit User Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Edit User Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2023-32707"], "impact": 80, "mitre_attack": ["T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `audittrail` action IN ("change_own_password","password_change","edit_password") AND info="granted" AND NOT user IN (admin, splunk-system-user) | stats earliest(_time) as event_time values(index) as index values(sourcetype) as sourcetype values(action) as action values(info) as info by user | `splunk_edit_user_privilege_escalation_filter`

[ESCU - Splunk Endpoint Denial of Service DoS Zip Bomb - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search allows operator to identify Splunk search app crashes resulting from specially crafted ZIP file using file monitoring that affects UF versions 8.1.11 and 8.2 versions below 8.2.7.1. It is not possible to detect Zip Bomb attack before crash. This search will provide Universal Forwarder errors from uploaded binary files (zip compression) which are used for this attack. If an analyst sees results from this search we suggest you investigate and triage what zip file was uploaded, zip compressed files may have different extensions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1499"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search allows operator to identify Splunk search app crashes resulting from specially crafted ZIP file using file monitoring that affects UF versions 8.1.11 and 8.2 versions below 8.2.7.1. It is not possible to detect Zip Bomb attack before crash. This search will provide Universal Forwarder errors from uploaded binary files (zip compression) which are used for this attack. If an analyst sees results from this search we suggest you investigate and triage what zip file was uploaded, zip compressed files may have different extensions.
action.escu.how_to_implement = Need to monitor Splunkd data from Universal Forwarders.
action.escu.known_false_positives = This search may reveal non malicious zip files causing errors as well.
action.escu.creation_date = 2022-08-02
action.escu.modification_date = 2022-08-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Endpoint Denial of Service DoS Zip Bomb - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Potential exposure of environment variables from url embedded in dashboard
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 75}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Endpoint Denial of Service DoS Zip Bomb - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 75, "cve": ["CVE-2022-37439"], "impact": 100, "mitre_attack": ["T1499"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search allows operator to identify Splunk search app crashes resulting from specially crafted ZIP file using file monitoring that affects UF versions 8.1.11 and 8.2 versions below 8.2.7.1. It is not possible to detect Zip Bomb attack before crash. This search will provide Universal Forwarder errors from uploaded binary files (zip compression) which are used for this attack. If an analyst sees results from this search we suggest you investigate and triage what zip file was uploaded, zip compressed files may have different extensions.
action.notable.param.rule_title = Splunk Endpoint Denial of Service DoS Zip Bomb
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd` component=FileClassifierManager event_message=*invalid* event_message=*binary* |stats count by host component event_message | `splunk_endpoint_denial_of_service_dos_zip_bomb_filter`

[ESCU - Splunk HTTP Response Splitting Via Rest SPL Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = A low-privileged user, using a specially crafted search command, can trigger an HTTP response splitting vulnerability with the rest SPL command that lets them potentially access other REST endpoints in the system arbitrarily, including accessing restricted content such as password files. This is because the user is able to inject the rest SPL command into the q parameter of an HTTP GET web request. The vulnerability requires the attacker to phish the victim by tricking them into initiating a request within their browser. The attacker cannot exploit the vulnerability at will.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027.006"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = A low-privileged user, using a specially crafted search command, can trigger an HTTP response splitting vulnerability with the rest SPL command that lets them potentially access other REST endpoints in the system arbitrarily, including accessing restricted content such as password files. This is because the user is able to inject the rest SPL command into the q parameter of an HTTP GET web request. The vulnerability requires the attacker to phish the victim by tricking them into initiating a request within their browser. The attacker cannot exploit the vulnerability at will.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _audit index. This search may assist in detecting possible http response splitting exploitation attemptss.
action.escu.known_false_positives = This search may have produce false positives as malformed or erroneous requests made to this endpoint may be executed willingly or erroneously by operators.
action.escu.creation_date = 2023-05-23
action.escu.modification_date = 2023-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk HTTP Response Splitting Via Rest SPL Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk HTTP Response Splitting Via Rest SPL Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1027.006"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `audit_searches` AND search IN ("*|*rest*POST*","*|*rest*PUT*","*|*rest*PATCH*","*|*rest*DELETE*") AND NOT search="*audit_searches*" | table user info has_error_msg search _time  | `splunk_http_response_splitting_via_rest_spl_command_filter`

[ESCU - Splunk Improperly Formatted Parameter Crashes splunkd - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, when the INGEST\\_EVAL parameter is improperly formatted, it crashes splunkd. This hunting search provides the user, timing and number of times the crashing command was executed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1499"], "nist": ["DE.CM"]}
action.escu.data_models = ["Splunk_Audit"]
action.escu.eli5 = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, when the INGEST\\_EVAL parameter is improperly formatted, it crashes splunkd. This hunting search provides the user, timing and number of times the crashing command was executed.
action.escu.how_to_implement = Requires access to audittrail and use of Splunk_Audit.Search_Activity datamodel.
action.escu.known_false_positives = This is a hunting search it should be focused on affected products, otherwise it is likely to produce false positives.
action.escu.creation_date = 2023-02-14
action.escu.modification_date = 2023-02-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Improperly Formatted Parameter Crashes splunkd - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = An attempt to exploit ingest eval parameter was detected from $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Improperly Formatted Parameter Crashes splunkd - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2023-22941"], "impact": 100, "mitre_attack": ["T1499"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, when the INGEST\\_EVAL parameter is improperly formatted, it crashes splunkd. This hunting search provides the user, timing and number of times the crashing command was executed.
action.notable.param.rule_title = Splunk Improperly Formatted Parameter Crashes splunkd
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where (Search_Activity.search="*makeresults*"AND Search_Activity.search="*ingestpreview*transforms*") Search_Activity.search_type=adhoc Search_Activity.search!="*splunk_improperly_formatted_parameter_crashes_splunkd_filter*" Search_Activity.user!=splunk-system-user by Search_Activity.search, Search_Activity.info, Search_Activity.total_run_time, Search_Activity.user, Search_Activity.search_type | `drop_dm_object_name(Search_Activity)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_improperly_formatted_parameter_crashes_splunkd_filter`

[ESCU - Splunk list all nonstandard admin accounts - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will enumerate all Splunk Accounts with administrative rights on this instance.  It deliberately ignores the default admin account since this is assumed to be present.  This search may help in a detection the Cross-Site Scripting Attack listed: In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a View allows for Cross-Site Scripting in an XML View through the 'layoutPanel' attribute in the 'module' tag. The vulnerability affects instances with Splunk Web enabled.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will enumerate all Splunk Accounts with administrative rights on this instance.  It deliberately ignores the default admin account since this is assumed to be present.  This search may help in a detection the Cross-Site Scripting Attack listed: In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a View allows for Cross-Site Scripting in an XML View through the 'layoutPanel' attribute in the 'module' tag. The vulnerability affects instances with Splunk Web enabled.
action.escu.how_to_implement = The user running this search is required to have a permission allowing them to dispatch REST requests to indexers (the `dispatch_rest_to_indexers` capability) in some architectures.  If there have been admin account, in addition to the standard admin account, intentionally created on this server, then edit the filter macro to exclude them.
action.escu.known_false_positives = It is not possible to discern from the user table whether or not users with admin rights have been created intentionally, accidentally, or as a result of exploitation. Each user with these rights should be investigated and, if legitimate, added to the filter macro above.  If a user is not believed to be legitimate, then further investigation should take place.
action.escu.creation_date = 2023-02-07
action.escu.modification_date = 2023-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk list all nonstandard admin accounts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk list all nonstandard admin accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-22933"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | rest splunk_server=local /services/authentication/users |search capabilities=admin* OR imported_capabilities=admin* title!=admin | table title roles capabilities splunk_server | `splunk_list_all_nonstandard_admin_accounts_filter`

[ESCU - Splunk Low Privilege User Can View Hashed Splunk Password - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 9.0.5, 8.2.11, and 8.1.14, a low-privilege user who holds the user role can see the hashed version of the initial user name and password for the Splunk instance by using the rest SPL command against the conf-user-seed REST endpoint. This can lead to a privilege escalation that lets the user take over the admin account on the instance.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1212"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 9.0.5, 8.2.11, and 8.1.14, a low-privilege user who holds the user role can see the hashed version of the initial user name and password for the Splunk instance by using the rest SPL command against the conf-user-seed REST endpoint. This can lead to a privilege escalation that lets the user take over the admin account on the instance.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _audit index. This detection may assist in efforts to discover attempts to access con-user-seed file content.
action.escu.known_false_positives = This search may produce false positives as accounts with high privileges may access this file. Operator will need to investigate these actions in order to discern exploitation attempts.
action.escu.creation_date = 2023-05-09
action.escu.modification_date = 2023-05-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Low Privilege User Can View Hashed Splunk Password - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Low Privilege User Can View Hashed Splunk Password - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1212"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_web` uri="*/servicesNS/nobody/system/configs/conf-user-seed*" | stats earliest(_time) as event_time values(method) as method values(status) as status values(clientip) as clientip values(useragent) as useragent values(file) as file by user | convert ctime(*time) | `splunk_low_privilege_user_can_view_hashed_splunk_password_filter`

[ESCU - Splunk Path Traversal In Splunk App For Lookup File Edit - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 9.0.5, 8.2.11, and 8.1.14, a low-privilege user with access to the Splunk App for Lookup File Editing can, with a specially crafted web request, trigger a path traversal exploit that can then be used to read and write to restricted areas of the Splunk installation directory, including but not limited to the password hash file for the instance.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1083"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 9.0.5, 8.2.11, and 8.1.14, a low-privilege user with access to the Splunk App for Lookup File Editing can, with a specially crafted web request, trigger a path traversal exploit that can then be used to read and write to restricted areas of the Splunk installation directory, including but not limited to the password hash file for the instance.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index. This detection is meant for on premise environments, and if executed on internet facing servers without a WAF may produce a lot of results. This detection will not work against obfuscated path traversal requests.
action.escu.known_false_positives = This search may find additional path traversal exploitation attempts or malformed requests.
action.escu.creation_date = 2023-05-11
action.escu.modification_date = 2023-05-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Path Traversal In Splunk App For Lookup File Edit - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Path Traversal In Splunk App For Lookup File Edit - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1083"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkda` uri_query=*lookup_file* | table clientip uri_query lookup_file owner namespace  version | stats count by clientip namespace lookup_file uri_query | `splunk_path_traversal_in_splunk_app_for_lookup_file_edit_filter`

[ESCU - Persistent XSS in RapidDiag through User Interface Views - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise 9.0 versions before 9.0.4, a View allows for Cross-Site Scripting through the error message in a Base64-encoded image. The vulnerability affects instances with Splunk Web enabled. It does not affect Splunk Enterprise versions below 9.0. This search provides information on what user may have potentially added a malicious payload and what users were exposed to it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise 9.0 versions before 9.0.4, a View allows for Cross-Site Scripting through the error message in a Base64-encoded image. The vulnerability affects instances with Splunk Web enabled. It does not affect Splunk Enterprise versions below 9.0. This search provides information on what user may have potentially added a malicious payload and what users were exposed to it.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index
action.escu.known_false_positives = This is a hunting search, it will not deobfuscate base64 payload, it provides however it will provide what user added the view artifact and what user opened it. It will require further investigation based on the information presented by this hunting search.
action.escu.creation_date = 2023-02-14
action.escu.modification_date = 2023-02-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Persistent XSS in RapidDiag through User Interface Views - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = A potential XSS attempt has been detected from $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Persistent XSS in RapidDiag through User Interface Views - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-22932"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = In Splunk Enterprise 9.0 versions before 9.0.4, a View allows for Cross-Site Scripting through the error message in a Base64-encoded image. The vulnerability affects instances with Splunk Web enabled. It does not affect Splunk Enterprise versions below 9.0. This search provides information on what user may have potentially added a malicious payload and what users were exposed to it.
action.notable.param.rule_title = Persistent XSS in RapidDiag through User Interface Views
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `audit_searches` path=/opt/splunk/etc/users/*/search/local/data/ui/views/* action=* |table user action roles info roles path | dedup user action | `persistent_xss_in_rapiddiag_through_user_interface_views_filter`

[ESCU - Splunk Persistent XSS Via URL Validation Bypass W Dashboard - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 9.0.4, 8.2.10, and 8.1.13, a low-privileged user can bypass URL validation to perform a path traversal and access restricted and confidential information by targeting other users on the instance, including the admin user.  The only affected version of bootstrap which shipped with Splunk was version 2.3.1, so the search is targeted at that version alone.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 9.0.4, 8.2.10, and 8.1.13, a low-privileged user can bypass URL validation to perform a path traversal and access restricted and confidential information by targeting other users on the instance, including the admin user.  The only affected version of bootstrap which shipped with Splunk was version 2.3.1, so the search is targeted at that version alone.
action.escu.how_to_implement = This search does not require additional data to be ingested. This search requires ability to search _internal index. This search helps discover access to vulnerable bootstrap versions.
action.escu.known_false_positives = This search will produce numerous false positives as it shows ANY accesses to vulnerable bootstrap Javascript files.  Accesses to these files occur during normal Splunk usage. To reduce or eliminate false positives, update the a version of Splunk which has addressed the vulnerability.
action.escu.creation_date = 2023-05-09
action.escu.modification_date = 2023-05-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Persistent XSS Via URL Validation Bypass W Dashboard - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Persistent XSS Via URL Validation Bypass W Dashboard - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 20, "cve": ["CVE-2019-8331"], "impact": 80, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_web` method=GET uri_path="*bootstrap-2.3.1*" file="*.js" | table _time clientip uri_path file status | `splunk_persistent_xss_via_url_validation_bypass_w_dashboard_filter`

[ESCU - Splunk Process Injection Forwarder Bundle Downloads - Rule]
action.escu = 0
action.escu.enabled = 1
description = On June 14th, 2022, Splunk released a security advisory relating to the authentication that happens between Universal Forwarders and Deployment Servers. In some circumstances, an unauthenticated client can download forwarder bundles from the Deployment Server. This hunting search pulls a full list of forwarder bundle downloads where the peer column is the forwarder, the host column is the Deployment Server, and then you have a list of the apps downloaded and the serverclasses in which the peer is a member of. You should look for apps or clients that you do not recognize as being part of your environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = On June 14th, 2022, Splunk released a security advisory relating to the authentication that happens between Universal Forwarders and Deployment Servers. In some circumstances, an unauthenticated client can download forwarder bundles from the Deployment Server. This hunting search pulls a full list of forwarder bundle downloads where the peer column is the forwarder, the host column is the Deployment Server, and then you have a list of the apps downloaded and the serverclasses in which the peer is a member of. You should look for apps or clients that you do not recognize as being part of your environment.
action.escu.how_to_implement = This hunting search uses native logs produced when a deployment server is within your environment. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = None at this time.
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Process Injection Forwarder Bundle Downloads - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Process Injection Forwarder Bundle Downloads - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2022-32157"], "impact": 50, "mitre_attack": ["T1055"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd` component="PackageDownloadRestHandler" | stats values(app) values(serverclass) by peer, host | `splunk_process_injection_forwarder_bundle_downloads_filter`

[ESCU - Splunk Protocol Impersonation Weak Encryption Configuration - Rule]
action.escu = 0
action.escu.enabled = 1
description = On June 14th, 2022, Splunk released a security advisory relating to TLS validation occuring within the httplib and urllib python libraries shipped with Splunk. In addition to upgrading to Splunk Enterprise 9.0 or later, several configuration settings need to be set. This search will check those configurations on the search head it is run from as well as its search peers. In addition to these settings, the PYTHONHTTPSVERIFY setting in $SPLUNK_HOME/etc/splunk-launch.conf needs to be enabled as well. Other components such as additional search heads or anything this rest command cannot be distributed to will need to be manually checked.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1001.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = On June 14th, 2022, Splunk released a security advisory relating to TLS validation occuring within the httplib and urllib python libraries shipped with Splunk. In addition to upgrading to Splunk Enterprise 9.0 or later, several configuration settings need to be set. This search will check those configurations on the search head it is run from as well as its search peers. In addition to these settings, the PYTHONHTTPSVERIFY setting in $SPLUNK_HOME/etc/splunk-launch.conf needs to be enabled as well. Other components such as additional search heads or anything this rest command cannot be distributed to will need to be manually checked.
action.escu.how_to_implement = The user running this search is required to have a permission allowing them to dispatch REST requests to indexers (The `dispatch_rest_to_indexers` capability). Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = While all of the settings on each device returned by this search may appear to be hardened, you will still need to verify the value of PYTHONHTTPSVERIFY in $SPLUNK_HOME/etc/splunk-launch.conf on each device in order to harden the python configuration.
action.escu.creation_date = 2022-05-25
action.escu.modification_date = 2022-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Protocol Impersonation Weak Encryption Configuration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Protocol Impersonation Weak Encryption Configuration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2022-32151"], "impact": 50, "mitre_attack": ["T1001.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | rest /services/server/info | table splunk_server version server_roles | join splunk_server [| rest /servicesNS/nobody/search/configs/conf-server/ search="PythonSslClientConfig" | table splunk_server sslVerifyServerCert sslVerifyServerName] | join splunk_server [| rest /servicesNS/nobody/search/configs/conf-web/settings |  table splunk_server serverCert sslVersions] | rename sslVerifyServerCert as "Server.conf:PythonSSLClientConfig:sslVerifyServerCert", sslVerifyServerName as "Server.conf:PythonSSLClientConfig:sslVerifyServerName", serverCert as "Web.conf:Settings:serverCert", sslVersions as "Web.conf:Settings:sslVersions" | `splunk_protocol_impersonation_weak_encryption_configuration_filter`

[ESCU - Splunk protocol impersonation weak encryption selfsigned - Rule]
action.escu = 0
action.escu.enabled = 1
description = On June 14th 2022, Splunk released vulnerability advisory addresing Python TLS validation which was not set before Splunk version 9. This search displays events showing WARNING of using Splunk issued default selfsigned certificates.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1588.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = On June 14th 2022, Splunk released vulnerability advisory addresing Python TLS validation which was not set before Splunk version 9. This search displays events showing WARNING of using Splunk issued default selfsigned certificates.
action.escu.how_to_implement = Must upgrade to Splunk version 9 and Configure TLS in order to apply this search. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = This searches finds self signed certificates issued by Splunk which are not recommended from Splunk version 9 forward.
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk protocol impersonation weak encryption selfsigned - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk protocol impersonation weak encryption selfsigned - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2022-32152"], "impact": 50, "mitre_attack": ["T1588.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd` certificate event_message="X509 certificate* should not be used*" | stats count by host CN component log_level | `splunk_protocol_impersonation_weak_encryption_selfsigned_filter`

[ESCU - Splunk protocol impersonation weak encryption simplerequest - Rule]
action.escu = 0
action.escu.enabled = 1
description = On Splunk version 9 on Python3 client libraries verify server certificates by default and use CA certificate store. This search warns a user about a failure to validate a certificate using python3 request.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1588.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = On Splunk version 9 on Python3 client libraries verify server certificates by default and use CA certificate store. This search warns a user about a failure to validate a certificate using python3 request.
action.escu.how_to_implement = Must upgrade to Splunk version 9 and Configure TLS host name validation for Splunk Python modules in order to apply this search. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = This search tries to address validation of server and client certificates within Splunk infrastructure, it might produce results from accidental or unintended requests to port 8089.
action.escu.creation_date = 2022-05-24
action.escu.modification_date = 2022-05-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk protocol impersonation weak encryption simplerequest - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk protocol impersonation weak encryption simplerequest - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2022-32152"], "impact": 50, "mitre_attack": ["T1588.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunk_python` "simpleRequest SSL certificate validation is enabled without hostname verification" | stats count by host path | `splunk_protocol_impersonation_weak_encryption_simplerequest_filter`

[ESCU - Splunk RBAC Bypass On Indexing Preview REST Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = An unauthorized user can use the /services/indexing/preview REST endpoint to overwrite search results if they know the search ID (SID) of an existing search job.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = An unauthorized user can use the /services/indexing/preview REST endpoint to overwrite search results if they know the search ID (SID) of an existing search job.
action.escu.how_to_implement = This search does not require additional data ingestion. It requires the ability to search _internal index.
action.escu.known_false_positives = This is a hunting search which provides verbose results against this endpoint. Operator must consider things such as IP address, useragent and user(specially low privelege) and host to investigate possible attack.
action.escu.creation_date = 2023-05-10
action.escu.modification_date = 2023-05-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk RBAC Bypass On Indexing Preview REST Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk RBAC Bypass On Indexing Preview REST Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1134"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkda` method="POST" uri="*/services/indexing/preview*" | table host clientip status useragent user uri_path | `splunk_rbac_bypass_on_indexing_preview_rest_endpoint_filter`

[ESCU - Splunk RCE via Serialized Session Payload - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions lower than 8.2.12, 9.0.6, and 9.1.1, an attacker can execute a specially crafted query that they can then use to serialize untrusted data. The attacker can use the query to execute arbitrary code. The exploit requires the use of the 'collect' SPL command which writes a file within the Splunk Enterprise installation.  The attacker can then use this file to submit a serialized payload that can result in execution of code within the payload. Please refer to the following URL for additional information on these disclosures - https://advisory.splunk.com
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions lower than 8.2.12, 9.0.6, and 9.1.1, an attacker can execute a specially crafted query that they can then use to serialize untrusted data. The attacker can use the query to execute arbitrary code. The exploit requires the use of the 'collect' SPL command which writes a file within the Splunk Enterprise installation.  The attacker can then use this file to submit a serialized payload that can result in execution of code within the payload. Please refer to the following URL for additional information on these disclosures - https://advisory.splunk.com
action.escu.how_to_implement = Requires access to the _audit index.
action.escu.known_false_positives = There are numerous many uses of the 'makeresults' and 'collect' SPL commands. Please evaluate the results of this search for potential abuse.
action.escu.creation_date = 2023-10-02
action.escu.modification_date = 2023-10-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk RCE via Serialized Session Payload - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk RCE via Serialized Session Payload - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-40595"], "impact": 50, "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `audit_searches` file=* (search="*makeresults*" AND search="*collect*") | stats count min(_time) as firstTime max(_time) as lastTime by action file user splunk_server search | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_rce_via_serialized_session_payload_filter`

[ESCU - Splunk RCE via Splunk Secure Gateway  Splunk Mobile alerts feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = This hunting search provides information on possible exploitation attempts against Splunk Secure Gateway App Mobile Alerts feature in Splunk versions 9.0, 8.2.x, 8.1.x. An authenticated user can run arbitrary operating system commands remotely through the use of specially crafted requests to the mobile alerts feature in the Splunk Secure Gateway app.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This hunting search provides information on possible exploitation attempts against Splunk Secure Gateway App Mobile Alerts feature in Splunk versions 9.0, 8.2.x, 8.1.x. An authenticated user can run arbitrary operating system commands remotely through the use of specially crafted requests to the mobile alerts feature in the Splunk Secure Gateway app.
action.escu.how_to_implement = This search only applies if Splunk Mobile Gateway is deployed in the vulnerable Splunk versions.
action.escu.known_false_positives = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index. Focus of this search is "uri_path=/servicesNS/nobody/splunk_secure_gateway/storage/collections/data/mobile_alerts*" which is the injection point.
action.escu.creation_date = 2022-10-11
action.escu.modification_date = 2022-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk RCE via Splunk Secure Gateway  Splunk Mobile alerts feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk RCE via Splunk Secure Gateway  Splunk Mobile alerts feature - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2022-43567"], "impact": 90, "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkda` uri_path="/servicesNS/nobody/splunk_secure_gateway/storage/collections/data/mobile_alerts*" sort="notification.created_at:-1" | table  clientip file host method uri_query sort | `splunk_rce_via_splunk_secure_gateway__splunk_mobile_alerts_feature_filter`

[ESCU - Splunk Reflected XSS in the templates lists radio - Rule]
action.escu = 0
action.escu.enabled = 1
description = Splunk versions below 8.1.12,8.2.9 and 9.0.2 are vulnerable to reflected cross site scripting (XSS). A View allows for a Reflected Cross Site scripting via JavaScript Object Notation (JSON) in a query parameter when ouput_mode=radio.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = Splunk versions below 8.1.12,8.2.9 and 9.0.2 are vulnerable to reflected cross site scripting (XSS). A View allows for a Reflected Cross Site scripting via JavaScript Object Notation (JSON) in a query parameter when ouput_mode=radio.
action.escu.how_to_implement = This vulnerability only affects instances with Splunk Web enabled. This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index.
action.escu.known_false_positives = This search may produce false positives as it is difficult to pinpoint all possible XSS injection characters in a single search string. Special attention is required to "en-US/list/entities/x/ui/views" which is the vulnerable injection point.
action.escu.creation_date = 2022-10-11
action.escu.modification_date = 2022-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Reflected XSS in the templates lists radio - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Reflected XSS in the templates lists radio - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2022-43568"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_webx` user=admin status=200 uri=*/lists/entities/x/ui/views* uri_query!=null | stats count earliest(_time) as event_time values(status) as status values(clientip) as clientip by index, sourcetype, _time, host, user, uri | `splunk_reflected_xss_in_the_templates_lists_radio_filter`

[ESCU - Splunk Reflected XSS on App Search Table Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 9.1.1, 9.0.6, and 8.2.12, an attacker can craft a special web request that can result in reflected cross-site scripting XSS on the app search table web endpoint, which presents as the Create Table View page in Splunk Web. Exploitation of this vulnerability can lead to the execution of arbitrary commands on the Splunk platform instance. A JavaScript file within this web endpoint does not properly validate input which lets an attacker insert a payload into a function.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 9.1.1, 9.0.6, and 8.2.12, an attacker can craft a special web request that can result in reflected cross-site scripting XSS on the app search table web endpoint, which presents as the Create Table View page in Splunk Web. Exploitation of this vulnerability can lead to the execution of arbitrary commands on the Splunk platform instance. A JavaScript file within this web endpoint does not properly validate input which lets an attacker insert a payload into a function.
action.escu.how_to_implement = Need access to the internal indexes.
action.escu.known_false_positives = This search will produce false positives. It is necessary to also look at uri_query parameter to determine the possible malicious intention of inserting makeresults within the uri string.
action.escu.creation_date = 2023-09-05
action.escu.modification_date = 2023-09-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Reflected XSS on App Search Table Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Reflected XSS on App Search Table Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 40, "impact": 30, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_web` (dataset_commands="*makeresults*" AND dataset_commands="*count*" AND dataset_commands="*eval*" AND dataset_commands="*baseSPL*") | stats count min(_time) as firstTime max(_time) as lastTime by clientip status user view root uri_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `splunk_reflected_xss_on_app_search_table_endpoint_filter`

[ESCU - Splunk risky Command Abuse disclosed february 2023 - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for a variety of high-risk commands throughout a number of different Splunk Vulnerability Disclosures.  Please refer to the following URL for additional information on these disclosures - https://advisory.splunk.com
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548", "T1202"], "nist": ["DE.AE"]}
action.escu.data_models = ["Splunk_Audit"]
action.escu.eli5 = This search looks for a variety of high-risk commands throughout a number of different Splunk Vulnerability Disclosures.  Please refer to the following URL for additional information on these disclosures - https://advisory.splunk.com
action.escu.how_to_implement = Requires implementation of Splunk_Audit.Search_Activity datamodel.
action.escu.known_false_positives = This search encompasses many commands.
action.escu.creation_date = 2023-08-30
action.escu.modification_date = 2023-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk risky Command Abuse disclosed february 2023 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk risky Command Abuse disclosed february 2023 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-22931", "CVE-2023-22934", "CVE-2023-22935", "CVE-2023-22936", "CVE-2023-22939", "CVE-2023-22940", "CVE-2023-40598", "CVE-2023-40598"], "impact": 50, "mitre_attack": ["T1548", "T1202"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats fillnull_value="N/A" count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where Search_Activity.search_type=adhoc Search_Activity.user!=splunk-system-user by Search_Activity.search Search_Activity.info Search_Activity.total_run_time Search_Activity.user Search_Activity.search_type | `drop_dm_object_name(Search_Activity)` | lookup splunk_risky_command splunk_risky_command as search output splunk_risky_command description vulnerable_versions CVE other_metadata | where splunk_risky_command != "false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_risky_command_abuse_disclosed_february_2023_filter`

[ESCU - Splunk Stored XSS via Data Model objectName field - Rule]
action.escu = 0
action.escu.enabled = 1
description = Splunk Enterprise versions 8.1.12, 8.2.9, 9.0.2 are vulnerable to persistent cross site scripting via Data Model object name. An authenticated user can inject and store arbitrary scripts that can lead to persistent cross-site scripting (XSS) in the object name Data Model.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = Splunk Enterprise versions 8.1.12, 8.2.9, 9.0.2 are vulnerable to persistent cross site scripting via Data Model object name. An authenticated user can inject and store arbitrary scripts that can lead to persistent cross-site scripting (XSS) in the object name Data Model.
action.escu.how_to_implement = This vulnerability only affects Splunk Web enabled instances. This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index.
action.escu.known_false_positives = This search may produce false positives and does not cover exploitation attempts via code obfuscation, focus of search is suspicious requests against "/en-US/splunkd/__raw/servicesNS/*/launcher/datamodel/model" which is the injection point.
action.escu.creation_date = 2022-10-11
action.escu.modification_date = 2022-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Stored XSS via Data Model objectName field - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Stored XSS via Data Model objectName field - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2022-43569"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_webx` uri=/en-US/splunkd/__raw/servicesNS/*/launcher/datamodel/model* uri_query!=null | stats count by _time host status clientip user uri | `splunk_stored_xss_via_data_model_objectname_field_filter`

[ESCU - Splunk Unauthenticated Log Injection Web Service Log - Rule]
action.escu = 0
action.escu.enabled = 1
description = An attacker can use a specially crafted web URL in their browser to cause log file injection, in which the attack inserts American National Standards Institute (ANSI) escape codes into specific files using a terminal program that supports those escape codes. The attack requires a terminal program that supports the translation of ANSI escape codes and requires additional user interaction to successfully execute. This following analytic detects potential log injection attempts into the Splunk server.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = An attacker can use a specially crafted web URL in their browser to cause log file injection, in which the attack inserts American National Standards Institute (ANSI) escape codes into specific files using a terminal program that supports those escape codes. The attack requires a terminal program that supports the translation of ANSI escape codes and requires additional user interaction to successfully execute. This following analytic detects potential log injection attempts into the Splunk server.
action.escu.how_to_implement = This only affects web enabled Splunk instances. The detection does require the ability to search the _internal index.
action.escu.known_false_positives = This hunting search will produce false positives if ANSI escape characters are included in URLs either voluntarily or by accident. This search will not detect obfuscated ANSI characters.
action.escu.creation_date = 2023-07-13
action.escu.modification_date = 2023-07-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Unauthenticated Log Injection Web Service Log - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Unauthenticated Log Injection Web Service Log - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 30, "cve": ["CVE-2023-32712"], "impact": 30, "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_webx`  uri_path IN ("*\x1B*", "*\u001b*", "*\033*", "*\0x9*", "*\0x8*") | stats count by uri_path method host status clientip | `splunk_unauthenticated_log_injection_web_service_log_filter`

[ESCU - Splunk unnecessary file extensions allowed by lookup table uploads - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, the lookup table uploads let a user upload lookup tables with unnecessary filename extensions. Lookup table file extensions may now only be one of .csv, .csv.gz, .kmz, .kml, .mmdb, or .mmdb.gz. This search provides user activity focus on uploads which aims to help hunt for malicious file uploads.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, the lookup table uploads let a user upload lookup tables with unnecessary filename extensions. Lookup table file extensions may now only be one of .csv, .csv.gz, .kmz, .kml, .mmdb, or .mmdb.gz. This search provides user activity focus on uploads which aims to help hunt for malicious file uploads.
action.escu.how_to_implement = Requires access to internal splunkd_access.
action.escu.known_false_positives = This is a hunting search, the search provides information on upload, edit, and delete activity on Lookup Tables. Manual investigation is necessary after executing search. This search will produce false positives as payload cannot be directly discerned.
action.escu.creation_date = 2023-02-14
action.escu.modification_date = 2023-02-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk unnecessary file extensions allowed by lookup table uploads - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Potential lookup template injection attempt from $user$ on lookup table at path $uri_path$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "uri_path", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk unnecessary file extensions allowed by lookup table uploads - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-22937"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, the lookup table uploads let a user upload lookup tables with unnecessary filename extensions. Lookup table file extensions may now only be one of .csv, .csv.gz, .kmz, .kml, .mmdb, or .mmdb.gz. This search provides user activity focus on uploads which aims to help hunt for malicious file uploads.
action.notable.param.rule_title = Splunk unnecessary file extensions allowed by lookup table uploads
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkda` method IN ("POST", "DELETE") uri_path=/servicesNS/*/ui/views/* | eval activity = case( method=="POST" AND like( uri_path , "%/acl" ) , "Permissions Update", method=="POST" AND NOT like( uri_path , "%/acl" ) , "Edited" , method=="DELETE" , "Deleted" ) | rex field=uri_path "(?<user_and_app>.*?)\/ui\/views/(?<dashboard_encoded>.*)" | eval dashboard = urldecode( dashboard_encoded ) | table _time, uri_path, user, dashboard, activity, uri_path | `splunk_unnecessary_file_extensions_allowed_by_lookup_table_uploads_filter`

[ESCU - Splunk User Enumeration Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = On May 3rd, 2022, Splunk published a security advisory for  username enumeration stemming from verbose login failure messages present on some REST endpoints. This detection will alert on attempted exploitation in patched versions of Splunk as well as actual exploitation in unpatched version of Splunk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = On May 3rd, 2022, Splunk published a security advisory for  username enumeration stemming from verbose login failure messages present on some REST endpoints. This detection will alert on attempted exploitation in patched versions of Splunk as well as actual exploitation in unpatched version of Splunk.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _audit index. This detection may assist in efforts to find password spraying or brute force authorization attempts in addition to someone enumerating usernames.
action.escu.known_false_positives = Automation executing authentication attempts against your Splunk infrastructure with outdated credentials may cause false positives.
action.escu.creation_date = 2022-04-29
action.escu.modification_date = 2022-04-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk User Enumeration Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = $TotalFailedAuths$ failed authentication events to Splunk from $src$ detected.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk User Enumeration Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2021-33845"], "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = On May 3rd, 2022, Splunk published a security advisory for  username enumeration stemming from verbose login failure messages present on some REST endpoints. This detection will alert on attempted exploitation in patched versions of Splunk as well as actual exploitation in unpatched version of Splunk.
action.notable.param.rule_title = Splunk User Enumeration Attempt
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `splunkd_failed_auths` | stats count(user) as auths by user, src | where auths>5 | stats values(user) as "Users", sum(auths) as TotalFailedAuths by src | `splunk_user_enumeration_attempt_filter`

[ESCU - Splunk XSS in Monitoring Console - Rule]
action.escu = 0
action.escu.enabled = 1
description = On May 3rd, 2022, Splunk published a security advisory for a reflective Cross-Site Scripting (XSS) vulnerability stemming from the lack of input validation in the Distributed Monitoring Console app. This detection will alert on attempted exploitation in patched versions of Splunk as well as actual exploitation in unpatched version of Splunk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = On May 3rd, 2022, Splunk published a security advisory for a reflective Cross-Site Scripting (XSS) vulnerability stemming from the lack of input validation in the Distributed Monitoring Console app. This detection will alert on attempted exploitation in patched versions of Splunk as well as actual exploitation in unpatched version of Splunk.
action.escu.how_to_implement = This detection does not require you to ingest any new data. The detection does require the ability to search the _internal index. This detection will find attempted exploitation of CVE-2022-27183.
action.escu.known_false_positives = Use of the monitoring console where the less-than sign (<) is the first character in the description field.
action.escu.creation_date = 2022-04-27
action.escu.modification_date = 2022-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk XSS in Monitoring Console - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = A potential XSS attempt has been detected from $user$
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk XSS in Monitoring Console - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2022-27183"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = On May 3rd, 2022, Splunk published a security advisory for a reflective Cross-Site Scripting (XSS) vulnerability stemming from the lack of input validation in the Distributed Monitoring Console app. This detection will alert on attempted exploitation in patched versions of Splunk as well as actual exploitation in unpatched version of Splunk.
action.notable.param.rule_title = Splunk XSS in Monitoring Console
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `splunkd_web` method="GET" uri_query="description=%3C*" | table _time host status clientip user uri | `splunk_xss_in_monitoring_console_filter`

[ESCU - Splunk XSS in Save table dialog header in search page - Rule]
action.escu = 0
action.escu.enabled = 1
description = This is a hunting search to find persistent cross-site scripting XSS code that was included while inputing data in 'Save Table' dialog in Splunk Enterprise (8.1.12,8.2.9,9.0.2). A remote user with "power" Splunk role can store this code that can lead to persistent cross site scripting.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This is a hunting search to find persistent cross-site scripting XSS code that was included while inputing data in 'Save Table' dialog in Splunk Enterprise (8.1.12,8.2.9,9.0.2). A remote user with "power" Splunk role can store this code that can lead to persistent cross site scripting.
action.escu.how_to_implement = Watch for POST requests combined with XSS script strings or obfuscation against the injection point /en-US/splunkd/__raw/servicesNS/nobody/search/datamodel/model.
action.escu.known_false_positives = If host is vulnerable and XSS script strings are inputted they will show up in search. Not all Post requests are malicious as they will show when users create and save dashboards. This search may produce several results with non malicious POST requests. Only affects Splunk Web enabled instances.
action.escu.creation_date = 2022-10-11
action.escu.modification_date = 2022-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk XSS in Save table dialog header in search page - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk XSS in Save table dialog header in search page - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2022-43561"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `splunkd_webx` method=POST  uri=/en-US/splunkd/__raw/servicesNS/nobody/search/datamodel/model | table _time host status clientip user uri | `splunk_xss_in_save_table_dialog_header_in_search_page_filter`

[ESCU - Splunk XSS via View - Rule]
action.escu = 0
action.escu.enabled = 1
description = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a View allows for Cross-Site Scripting in an XML View through the 'layoutPanel' attribute in the 'module' tag. The vulnerability affects instances with Splunk Web enabled. This hunting search shows users action, application and role used for creating views related to this vulnerability.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, a View allows for Cross-Site Scripting in an XML View through the 'layoutPanel' attribute in the 'module' tag. The vulnerability affects instances with Splunk Web enabled. This hunting search shows users action, application and role used for creating views related to this vulnerability.
action.escu.how_to_implement = This data is collected by default in Splunk.  Upon first enabling this rule, a number of errors may be observed. Those that are due to improperly formatted, but non-nefarious, XML views should be be remedied in the corresponding view. Please take care investigating potential XSS as accessing an affected page could retrigger the exploit.
action.escu.known_false_positives = The error detected above can be generated for a wide variety of improperly formatted XML views.  There will be false positives as the search cannot extract the malicious payload and the view should be manually investigated.
action.escu.creation_date = 2023-02-07
action.escu.modification_date = 2023-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk XSS via View - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk XSS via View - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-22933"], "impact": 50, "mitre_attack": ["T1189"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = index = _internal sourcetype IN ("splunk_web_service", "splunk_python") message="*loadParams*" | `security_content_ctime(_time)` |  table _time message fileName | `splunk_xss_via_view_filter`

[ESCU - Suspicious Email Attachment Extensions - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects emails that contain attachments with suspicious file extensions. Detecting and responding to emails with suspicious attachments can mitigate the risks associated with phishing and malware attacks, thereby protecting the organization's data and systems from potential harm. The detection is made by using a Splunk query that searches for emails in the datamodel=Email where the filename of the attachment is not empty. The analytic uses the tstats command to summarize the count, first time, and last time of the emails that meet the criteria. It groups the results by the source user, file name, and message ID of the email. The detection is important because it indicates potential phishing or malware delivery attempts in which an attacker attempts to deliver malicious content through email attachments, which can lead to data breaches, malware infections, or unauthorized access to sensitive information. Next steps include reviewing the identified emails and attachments and analyzing the source user, file name, and message ID to determine if they are legitimate or malicious. Additionally, you must inspect any relevant on-disk artifacts associated with the attachments and investigate any concurrent processes to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = The following analytic detects emails that contain attachments with suspicious file extensions. Detecting and responding to emails with suspicious attachments can mitigate the risks associated with phishing and malware attacks, thereby protecting the organization's data and systems from potential harm. The detection is made by using a Splunk query that searches for emails in the datamodel=Email where the filename of the attachment is not empty. The analytic uses the tstats command to summarize the count, first time, and last time of the emails that meet the criteria. It groups the results by the source user, file name, and message ID of the email. The detection is important because it indicates potential phishing or malware delivery attempts in which an attacker attempts to deliver malicious content through email attachments, which can lead to data breaches, malware infections, or unauthorized access to sensitive information. Next steps include reviewing the identified emails and attachments and analyzing the source user, file name, and message ID to determine if they are legitimate or malicious. Additionally, you must inspect any relevant on-disk artifacts associated with the attachments and investigate any concurrent processes to identify the source of the attack.
action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \
 **Splunk Phantom Playbook Integration**\
If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Email Attachment Extensions - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "Emotet Malware  DHS Report TA18-201A ", "Hermetic Wiper", "Suspicious Emails"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Email Attachment Extensions - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Emotet Malware  DHS Report TA18-201A ", "Hermetic Wiper", "Suspicious Emails"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter` 

[ESCU - Suspicious Java Classes - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.
action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro.
action.escu.known_false_positives = There are no known false positives.
action.escu.creation_date = 2018-12-06
action.escu.modification_date = 2018-12-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Java Classes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Apache Struts Vulnerability"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Java Classes - Rule
action.correlationsearch.annotations = {"analytic_story": ["Apache Struts Vulnerability"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter`

[ESCU - Web Servers Executing Suspicious Processes - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects suspicious processes on systems labeled as web servers. This detection is made by a Splunk query that searches for specific process names that might indicate malicious activity. These suspicious processes include "whoami", "ping", "iptables", "wget", "service", and "curl". Uses the Splunk data model "Endpoint.Processes" and filters the results to only include systems categorized as web servers. This detection is important because it indicates unauthorized or malicious activity on web servers since these processes are commonly used by attackers to perform reconnaissance, establish persistence, or exfiltrate data from compromised systems. The impact of such an attack can be significant, ranging from data theft to the deployment of additional malicious payloads, potentially leading to ransomware or other damaging outcomes. False positives might occur since the legitimate use of these processes on web servers can trigger the analytic. Next steps include triaging and investigating to determine the legitimacy of the activity. Also, review the source and command of the suspicious process. You must also examine any relevant on-disk artifacts and look for concurrent processes to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects suspicious processes on systems labeled as web servers. This detection is made by a Splunk query that searches for specific process names that might indicate malicious activity. These suspicious processes include "whoami", "ping", "iptables", "wget", "service", and "curl". Uses the Splunk data model "Endpoint.Processes" and filters the results to only include systems categorized as web servers. This detection is important because it indicates unauthorized or malicious activity on web servers since these processes are commonly used by attackers to perform reconnaissance, establish persistence, or exfiltrate data from compromised systems. The impact of such an attack can be significant, ranging from data theft to the deployment of additional malicious payloads, potentially leading to ransomware or other damaging outcomes. False positives might occur since the legitimate use of these processes on web servers can trigger the analytic. Next steps include triaging and investigating to determine the legitimacy of the activity. Also, review the source and command of the suspicious process. You must also examine any relevant on-disk artifacts and look for concurrent processes to identify the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks.
action.escu.creation_date = 2019-04-01
action.escu.modification_date = 2019-04-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web Servers Executing Suspicious Processes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Apache Struts Vulnerability"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Web Servers Executing Suspicious Processes - Rule
action.correlationsearch.annotations = {"analytic_story": ["Apache Struts Vulnerability"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects suspicious processes on systems labeled as web servers. This detection is made by a Splunk query that searches for specific process names that might indicate malicious activity. These suspicious processes include "whoami", "ping", "iptables", "wget", "service", and "curl". Uses the Splunk data model "Endpoint.Processes" and filters the results to only include systems categorized as web servers. This detection is important because it indicates unauthorized or malicious activity on web servers since these processes are commonly used by attackers to perform reconnaissance, establish persistence, or exfiltrate data from compromised systems. The impact of such an attack can be significant, ranging from data theft to the deployment of additional malicious payloads, potentially leading to ransomware or other damaging outcomes. False positives might occur since the legitimate use of these processes on web servers can trigger the analytic. Next steps include triaging and investigating to determine the legitimacy of the activity. Also, review the source and command of the suspicious process. You must also examine any relevant on-disk artifacts and look for concurrent processes to identify the source of the attack.
action.notable.param.rule_title = Web Servers Executing Suspicious Processes
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter`

[ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function.
action.escu.known_false_positives = 
action.escu.creation_date = 2020-09-07
action.escu.modification_date = 2020-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud User Activities", "Compromised User Account"]
action.risk = 1
action.risk.param._risk_message = user $user$ has made $api_calls$ api calls, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities", "Compromised User Account"], "cis20": ["CIS 13"], "confidence": 50, "impact": 30, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`

[ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function.
action.escu.known_false_positives = Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
action.escu.creation_date = 2020-08-21
action.escu.modification_date = 2020-08-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Instance Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter`

[ESCU - Abnormally High Number Of Cloud Instances Launched - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function.
action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
action.escu.creation_date = 2020-08-21
action.escu.modification_date = 2020-08-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High Number Of Cloud Instances Launched - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious Cloud Instance Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Abnormally High Number Of Cloud Instances Launched - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Cryptomining", "Suspicious Cloud Instance Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter`

[ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model.
action.escu.known_false_positives = 
action.escu.creation_date = 2020-09-07
action.escu.modification_date = 2020-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.risk = 1
action.risk.param._risk_message = user $user$ has made $api_calls$ api calls related to security groups, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 30, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter`

[ESCU - Amazon EKS Kubernetes cluster scan detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs.
action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context.
action.escu.creation_date = 2020-04-15
action.escu.modification_date = 2020-04-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Amazon EKS Kubernetes cluster scan detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Amazon EKS Kubernetes cluster scan detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` 

[ESCU - Amazon EKS Kubernetes Pod scan detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects unauthenticated requests made against the Kubernetes' Pods API through proactive monitoring to protect the Kubernetes environment from unauthorized access and potential security breaches. The detection is made by using the Splunk query `aws_cloudwatchlogs_eks` with specific filters to identify these requests. Identifies events where the `user.username` is set to "system:anonymous", the `verb` is set to "list", and the `objectRef.resource` is set to "pods". Additionally, the search checks if the `requestURI` is equal to "/api/v1/pods". Analyzing these events helps you to identify any unauthorized access attempts to the Kubernetes' Pods API. Unauthenticated requests can indicate potential security breaches or unauthorized access to sensitive resources within the Kubernetes environment. The detection is important because unauthorized access to Kubernetes' Pods API can lead to the compromise of sensitive data, unauthorized execution of commands, or even the potential for lateral movement within the Kubernetes cluster. False positives might occur since there might be legitimate use cases for unauthenticated requests in certain scenarios. Therefore, you must review and validate any detected events before taking any action.  Next steps include investigating the incident to mitigate any ongoing threats, and strengthening the security measures to prevent future unauthorized access attempts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects unauthenticated requests made against the Kubernetes' Pods API through proactive monitoring to protect the Kubernetes environment from unauthorized access and potential security breaches. The detection is made by using the Splunk query `aws_cloudwatchlogs_eks` with specific filters to identify these requests. Identifies events where the `user.username` is set to "system:anonymous", the `verb` is set to "list", and the `objectRef.resource` is set to "pods". Additionally, the search checks if the `requestURI` is equal to "/api/v1/pods". Analyzing these events helps you to identify any unauthorized access attempts to the Kubernetes' Pods API. Unauthenticated requests can indicate potential security breaches or unauthorized access to sensitive resources within the Kubernetes environment. The detection is important because unauthorized access to Kubernetes' Pods API can lead to the compromise of sensitive data, unauthorized execution of commands, or even the potential for lateral movement within the Kubernetes cluster. False positives might occur since there might be legitimate use cases for unauthenticated requests in certain scenarios. Therefore, you must review and validate any detected events before taking any action.  Next steps include investigating the incident to mitigate any ongoing threats, and strengthening the security measures to prevent future unauthorized access attempts.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives.
action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context.
action.escu.creation_date = 2020-04-15
action.escu.modification_date = 2020-04-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Amazon EKS Kubernetes Pod scan detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Amazon EKS Kubernetes Pod scan detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` 

[ESCU - ASL AWS Concurrent Sessions From Different Ips - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an AWS IAM account with concurrent sessions coming from more than one unique IP address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. When a user navigates the AWS Console after authentication, the API call with the event name `DescribeEventAggregates` is registered in the AWS CloudTrail logs. The Splunk Threat Research team leveraged this event name to identify 2 concurrent sessions. The presence of this event occurring from two different IP addresses is highly unlikely. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1185"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an AWS IAM account with concurrent sessions coming from more than one unique IP address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. When a user navigates the AWS Console after authentication, the API call with the event name `DescribeEventAggregates` is registered in the AWS CloudTrail logs. The Splunk Threat Research team leveraged this event name to identify 2 concurrent sessions. The presence of this event occurring from two different IP addresses is highly unlikely. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
action.escu.creation_date = 2023-05-23
action.escu.modification_date = 2023-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Concurrent Sessions From Different Ips - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Compromised User Account", "AWS Identity and Access Management Account"]
action.risk = 1
action.risk.param._risk_message = User $identity.user.name$ has concurrent sessions from more than one unique IP address $src_endpoint.ip$ in the span of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "identity.user.credential_uid", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "src_endpoint.ip", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Concurrent Sessions From Different Ips - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "AWS Identity and Access Management Account"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1185"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `amazon_security_lake` api.operation=DescribeEventAggregates src_endpoint.domain!="AWS Internal" | bin span=5m _time | stats values(src_endpoint.ip) as src_endpoint.ip dc(src_endpoint.ip) as distinct_ip_count by _time identity.user.credential_uid identity.user.name | where distinct_ip_count > 1 | `aws_concurrent_sessions_from_different_ips_filter`

[ESCU - ASL AWS CreateAccessKey - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection rule monitors for the creation of AWS Identity and Access Management (IAM) access keys. An IAM access key consists of an access key ID and secret access key, which are used to sign programmatic requests to AWS services. While IAM access keys can be legitimately used by developers and administrators for API access, their creation can also be indicative of malicious activity. Attackers who have gained unauthorized access to an AWS environment might create access keys as a means to establish persistence or to exfiltrate data through the APIs. Moreover, because access keys can be used to authenticate with AWS services without the need for further interaction, they can be particularly appealing for bad actors looking to operate under the radar. Consequently, it's important to vigilantly monitor and scrutinize access key creation events, especially if they are associated with unusual activity or are created by users who don't typically perform these actions. This hunting query identifies when a potentially compromised user creates a IAM access key for another user who may have higher privilleges, which can be a sign for privilege escalation. Hunting queries are designed to be executed manual during threat hunting.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This detection rule monitors for the creation of AWS Identity and Access Management (IAM) access keys. An IAM access key consists of an access key ID and secret access key, which are used to sign programmatic requests to AWS services. While IAM access keys can be legitimately used by developers and administrators for API access, their creation can also be indicative of malicious activity. Attackers who have gained unauthorized access to an AWS environment might create access keys as a means to establish persistence or to exfiltrate data through the APIs. Moreover, because access keys can be used to authenticate with AWS services without the need for further interaction, they can be particularly appealing for bad actors looking to operate under the radar. Consequently, it's important to vigilantly monitor and scrutinize access key creation events, especially if they are associated with unusual activity or are created by users who don't typically perform these actions. This hunting query identifies when a potentially compromised user creates a IAM access key for another user who may have higher privilleges, which can be a sign for privilege escalation. Hunting queries are designed to be executed manual during threat hunting.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.
action.escu.creation_date = 2022-05-23
action.escu.modification_date = 2022-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS CreateAccessKey - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS CreateAccessKey - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` api.operation=CreateAccessKey http_request.user_agent!=console.amazonaws.com api.response.error=null | rename unmapped{}.key as unmapped_key , unmapped{}.value as unmapped_value | eval keyjoin=mvzip(unmapped_key,unmapped_value) | mvexpand keyjoin | rex field=keyjoin "^(?<key>[^,]+),(?<value>.*)$" | eval {key} = value | search responseElements.accessKey.userName = * | rename identity.user.name as identity_user_name, responseElements.accessKey.userName as responseElements_accessKey_userName | eval match=if(identity_user_name=responseElements_accessKey_userName,1,0) | search match=0 | rename identity_user_name as identity.user.name , responseElements_accessKey_userName as responseElements.accessKey.userName | stats count min(_time) as firstTime max(_time) as lastTime by responseElements.accessKey.userName api.operation api.service.name identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`asl_aws_createaccesskey_filter`

[ESCU - ASL AWS Defense Evasion Delete Cloudtrail - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies AWS `DeleteTrail` events within CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their malicious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may delete the the entire cloudtrail that is logging activities in the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies AWS `DeleteTrail` events within CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their malicious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may delete the the entire cloudtrail that is logging activities in the environment.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has stopped cloudTrail logging. Please investigate this activity.
action.escu.creation_date = 2023-05-31
action.escu.modification_date = 2023-05-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Defense Evasion Delete Cloudtrail - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Defense Evasion"]
action.risk = 1
action.risk.param._risk_message = User $identity.user.name$ has delete a CloudTrail logging for account id $identity.user.account_uid$
action.risk.param._risk = [{"risk_object_field": "src_endpoint.ip", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "identity.user.name", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Defense Evasion Delete Cloudtrail - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies AWS `DeleteTrail` events within CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their malicious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may delete the the entire cloudtrail that is logging activities in the environment.
action.notable.param.rule_title = ASL AWS Defense Evasion Delete Cloudtrail
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` api.operation=DeleteTrail | stats count min(_time) as firstTime max(_time) as lastTime by identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `asl_aws_defense_evasion_delete_cloudtrail_filter`

[ESCU - ASL AWS Defense Evasion Delete CloudWatch Log Group - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies AWS `DeleteLogGroup` events in CloudTrail logs. Attackers may evade the logging capability by deleting the log group in CloudWatch. This will stop sending the logs and metrics to CloudWatch. When the adversary has the right type of permissions within the compromised AWS environment, they may delete the CloudWatch log group that is logging activities in the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1562.008"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies AWS `DeleteLogGroup` events in CloudTrail logs. Attackers may evade the logging capability by deleting the log group in CloudWatch. This will stop sending the logs and metrics to CloudWatch. When the adversary has the right type of permissions within the compromised AWS environment, they may delete the CloudWatch log group that is logging activities in the environment.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has deleted CloudWatch logging. Please investigate this activity.
action.escu.creation_date = 2023-05-31
action.escu.modification_date = 2023-05-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Defense Evasion Delete CloudWatch Log Group - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Defense Evasion"]
action.risk = 1
action.risk.param._risk_message = User $identity.user.name$ has deleted a CloudWatch logging group for account id $identity.user.account_uid$
action.risk.param._risk = [{"risk_object_field": "src_endpoint.ip", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "identity.user.name", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Defense Evasion Delete CloudWatch Log Group - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562", "T1562.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies AWS `DeleteLogGroup` events in CloudTrail logs. Attackers may evade the logging capability by deleting the log group in CloudWatch. This will stop sending the logs and metrics to CloudWatch. When the adversary has the right type of permissions within the compromised AWS environment, they may delete the CloudWatch log group that is logging activities in the environment.
action.notable.param.rule_title = ASL AWS Defense Evasion Delete CloudWatch Log Group
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` api.operation=DeleteLogGroup | stats count min(_time) as firstTime max(_time) as lastTime by identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `asl_aws_defense_evasion_delete_cloudwatch_log_group_filter`

[ESCU - ASL AWS Defense Evasion Impair Security Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for several delete specific API calls made to AWS Security Services like CloudWatch, GuardDuty and Web Application Firewalls. These API calls are often leveraged by adversaries to weaken existing security defenses by deleting logging configurations in the CloudWatch alarm, delete a set of detectors from your Guardduty environment or simply delete a bunch of CloudWatch alarms to remain stealthy and avoid detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This analytic looks for several delete specific API calls made to AWS Security Services like CloudWatch, GuardDuty and Web Application Firewalls. These API calls are often leveraged by adversaries to weaken existing security defenses by deleting logging configurations in the CloudWatch alarm, delete a set of detectors from your Guardduty environment or simply delete a bunch of CloudWatch alarms to remain stealthy and avoid detection.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names.
action.escu.creation_date = 2023-06-01
action.escu.modification_date = 2023-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Defense Evasion Impair Security Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Defense Evasion"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Defense Evasion Impair Security Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` api.operation IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms") | stats count min(_time) as firstTime max(_time) as lastTime by api.operation identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|  `asl_aws_defense_evasion_impair_security_services_filter`

[ESCU - ASL AWS Excessive Security Scanning - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = While this search has no known false positives.
action.escu.creation_date = 2023-06-01
action.escu.modification_date = 2023-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Excessive Security Scanning - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = user $identity.user.name$ has excessive number of api calls.
action.risk.param._risk = [{"risk_object_field": "src_endpoint.ip", "risk_object_type": "system", "risk_score": 18}, {"risk_object_field": "identity.user.name", "risk_object_type": "user", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Excessive Security Scanning - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 13"], "confidence": 60, "impact": 30, "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` api.operation=Describe* OR api.operation=List* OR api.operation=Get* | stats dc(api.operation) as dc_api_operations min(_time) as firstTime max(_time) as lastTime values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_endpoint.ip values(cloud.region) as cloud.region values(identity.user.account_uid) as identity.user.account_uid by identity.user.name | where dc_api_operations > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`asl_aws_excessive_security_scanning_filter`

[ESCU - ASL AWS IAM Delete Policy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
action.escu.creation_date = 2023-06-02
action.escu.modification_date = 2023-06-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS IAM Delete Policy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS IAM Delete Policy - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1098"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` api.operation=DeletePolicy | stats count min(_time) as firstTime max(_time) as lastTime by api.operation api.service.name identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_delete_policy_filter`

[ESCU - ASL AWS Multi-Factor Authentication Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an attempt to disable multi-factor authentication for an AWS IAM user. An adversary who has obtained access to an AWS tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1621", "T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an attempt to disable multi-factor authentication for an AWS IAM user. An adversary who has obtained access to an AWS tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = AWS Administrators may disable MFA but it is highly unlikely for this event to occur without prior notice to the company
action.escu.creation_date = 2023-06-02
action.escu.modification_date = 2023-06-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Multi-Factor Authentication Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user_name$ has disabled Multi-Factor authentication for AWS account $aws_account_id$
action.risk.param._risk = [{"risk_object_field": "identity.user.account_uid", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "identity.user.name", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_endpoint.ip", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Multi-Factor Authentication Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1586", "T1586.003", "T1621", "T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an attempt to disable multi-factor authentication for an AWS IAM user. An adversary who has obtained access to an AWS tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.notable.param.rule_title = ASL AWS Multi-Factor Authentication Disabled
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` (api.operation=DeleteVirtualMFADevice OR api.operation=DeactivateMFADevice) | stats count min(_time) as firstTime max(_time) as lastTime by api.operation api.service.name identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_multi_factor_authentication_disabled_filter`

[ESCU - ASL AWS New MFA Method Registered For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the registration of a new Multi Factor authentication method for an AWS account logged through Amazon Secruity Lake (ASL). Adversaries who have obtained unauthorized access to an AWS account may register a new MFA method to maintain persistence.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the registration of a new Multi Factor authentication method for an AWS account logged through Amazon Secruity Lake (ASL). Adversaries who have obtained unauthorized access to an AWS account may register a new MFA method to maintain persistence.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
action.escu.creation_date = 2023-05-22
action.escu.modification_date = 2023-05-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS New MFA Method Registered For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = A new virtual device is added to user $identity.user.name$
action.risk.param._risk = [{"risk_object_field": "identity.user.name", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_endpoint.ip", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS New MFA Method Registered For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the registration of a new Multi Factor authentication method for an AWS account logged through Amazon Secruity Lake (ASL). Adversaries who have obtained unauthorized access to an AWS account may register a new MFA method to maintain persistence.
action.notable.param.rule_title = ASL AWS New MFA Method Registered For User
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `amazon_security_lake` api.operation=CreateVirtualMFADevice | stats count min(_time) as firstTime max(_time) as lastTime by api.operation api.service.name identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_new_mfa_method_registered_for_user_filter`

[ESCU - ASL AWS Password Policy Changes - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events from Amazon Security Lake where a user is making successful API calls to view/update/delete the existing password policy in an AWS organization. It is unlikely for a regular user to conduct this operation. These events may potentially be malicious, adversaries often use this information to gain more understanding of the password defenses in place and exploit them to increase their attack surface when a user account is compromised.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events from Amazon Security Lake where a user is making successful API calls to view/update/delete the existing password policy in an AWS organization. It is unlikely for a regular user to conduct this operation. These events may potentially be malicious, adversaries often use this information to gain more understanding of the password defenses in place and exploit them to increase their attack surface when a user account is compromised.
action.escu.how_to_implement = You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately triggered an AWS audit tool activity which may trigger this event.
action.escu.creation_date = 2023-05-22
action.escu.modification_date = 2023-05-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - ASL AWS Password Policy Changes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS IAM Privilege Escalation", "Compromised User Account"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ASL AWS Password Policy Changes - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `amazon_security_lake` "api.service.name"="iam.amazonaws.com" "api.operation" IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy") "api.response.error"=null | stats count min(_time) as firstTime max(_time) as lastTime by identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_password_policy_changes_filter`

[ESCU - AWS AMI Atttribute Modification for Exfiltration - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for suspicious AWS AMI attribute modifications, such as sharing it with another AWS account or making the full AMI image public. Adversaries are known to abuse these APIs to exfiltrate sensitive organization information stored in the AWS Resources, there by its very important to monitor these seemingly benign API activity in Cloudtrail logs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for suspicious AWS AMI attribute modifications, such as sharing it with another AWS account or making the full AMI image public. Adversaries are known to abuse these APIs to exfiltrate sensitive organization information stored in the AWS Resources, there by its very important to monitor these seemingly benign API activity in Cloudtrail logs.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that an AWS admin has legitimately shared a snapshot with others for  a specific purpose.
action.escu.creation_date = 2023-03-31
action.escu.modification_date = 2023-03-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS AMI Atttribute Modification for Exfiltration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious Cloud Instance Activities", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = AWS AMI from account $aws_account_id$ is shared externally with $accounts_added$ from $src_ip$ or AMI made is made Public.
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS AMI Atttribute Modification for Exfiltration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for suspicious AWS AMI attribute modifications, such as sharing it with another AWS account or making the full AMI image public. Adversaries are known to abuse these APIs to exfiltrate sensitive organization information stored in the AWS Resources, there by its very important to monitor these seemingly benign API activity in Cloudtrail logs.
action.notable.param.rule_title = AWS AMI Atttribute Modification for Exfiltration
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ModifyImageAttribute (requestParameters.launchPermission.add.items{}.userId = * OR requestParameters.launchPermission.add.items{}.group = all) |  rename requestParameters.launchPermission.add.items{}.group as group_added |  rename requestParameters.launchPermission.add.items{}.userId as accounts_added | eval ami_status=if(match(group_added,"all") ,"Public AMI", "Not Public")  | stats count min(_time) as firstTime max(_time) as lastTime  values(group_added) values(accounts_added) as accounts_added values(ami_status) by  src_ip region eventName userAgent user_arn aws_account_id userIdentity.principalId |  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_ami_atttribute_modification_for_exfiltration_filter`

[ESCU - AWS Concurrent Sessions From Different Ips - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an AWS IAM account with concurrent sessions coming from more than one unique IP address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. When a user navigates the AWS Console after authentication, the API call with the event name `DescribeEventAggregates` is registered in the AWS CloudTrail logs. The Splunk Threat Research team leveraged this event name to identify 2 concurrent sessions. The presence of this event occurring from two different IP addresses is highly unlikely. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1185"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an AWS IAM account with concurrent sessions coming from more than one unique IP address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. When a user navigates the AWS Console after authentication, the API call with the event name `DescribeEventAggregates` is registered in the AWS CloudTrail logs. The Splunk Threat Research team leveraged this event name to identify 2 concurrent sessions. The presence of this event occurring from two different IP addresses is highly unlikely. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.how_to_implement = You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
action.escu.creation_date = 2023-02-01
action.escu.modification_date = 2023-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Concurrent Sessions From Different Ips - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Compromised User Account", "AWS Identity and Access Management Account"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has concurrent sessions from more than one unique IP address $src_ip$ in the span of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Concurrent Sessions From Different Ips - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "AWS Identity and Access Management Account"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1185"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an AWS IAM account with concurrent sessions coming from more than one unique IP address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. When a user navigates the AWS Console after authentication, the API call with the event name `DescribeEventAggregates` is registered in the AWS CloudTrail logs. The Splunk Threat Research team leveraged this event name to identify 2 concurrent sessions. The presence of this event occurring from two different IP addresses is highly unlikely. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.notable.param.rule_title = AWS Concurrent Sessions From Different Ips
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `cloudtrail` eventName = DescribeEventAggregates src_ip!="AWS Internal" | bin span=5m _time | stats values(userAgent) values(eventName) values(src_ip) as src_ip  dc(src_ip) as distinct_ip_count by _time user_arn | where distinct_ip_count > 1 | `aws_concurrent_sessions_from_different_ips_filter`

[ESCU - AWS Console Login Failed During MFA Challenge - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an authentication attempt event against an AWS Console that fails during the Multi Factor Authentication challenge. AWS Cloudtrail logs provide a a very useful field called `additionalEventData` that logs information regarding usage of MFA. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1621"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an authentication attempt event against an AWS Console that fails during the Multi Factor Authentication challenge. AWS Cloudtrail logs provide a a very useful field called `additionalEventData` that logs information regarding usage of MFA. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled.
action.escu.how_to_implement = The Splunk AWS Add-on is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake.
action.escu.creation_date = 2022-10-03
action.escu.modification_date = 2022-10-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Console Login Failed During MFA Challenge - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover", "Compromised User Account"]
action.risk = 1
action.risk.param._risk_message = User $user_name$ failed to pass MFA challenge while logging into console from $src$
action.risk.param._risk = [{"risk_object_field": "user_name", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Console Login Failed During MFA Challenge - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1586", "T1586.003", "T1621"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an authentication attempt event against an AWS Console that fails during the Multi Factor Authentication challenge. AWS Cloudtrail logs provide a a very useful field called `additionalEventData` that logs information regarding usage of MFA. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled.
action.notable.param.rule_title = AWS Console Login Failed During MFA Challenge
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName= ConsoleLogin errorMessage="Failed authentication" additionalEventData.MFAUsed = "Yes" | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion user_name userIdentity.arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_console_login_failed_during_mfa_challenge_filter`

[ESCU - AWS Create Policy Version to allow all resources - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user created a policy version that allows them to access any resource in their account.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user created a policy version that allows them to access any resource in their account.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources and you must verify this activity.
action.escu.creation_date = 2022-05-17
action.escu.modification_date = 2022-05-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Create Policy Version to allow all resources - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user$ created a policy version that allows them to access any resource in their account.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Create Policy Version to allow all resources - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events where a user created a policy version that allows them to access any resource in their account.
action.notable.param.rule_title = AWS Create Policy Version to allow all resources
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success | spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | where key_policy_action_1 = "*" | stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID awsRegion user user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_create_policy_version_to_allow_all_resources_filter`

[ESCU - AWS CreateAccessKey - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection rule monitors for the creation of AWS Identity and Access Management (IAM) access keys. An IAM access key consists of an access key ID and secret access key, which are used to sign programmatic requests to AWS services. While IAM access keys can be legitimately used by developers and administrators for API access, their creation can also be indicative of malicious activity. Attackers who have gained unauthorized access to an AWS environment might create access keys as a means to establish persistence or to exfiltrate data through the APIs. Moreover, because access keys can be used to authenticate with AWS services without the need for further interaction, they can be particularly appealing for bad actors looking to operate under the radar. Consequently, it's important to vigilantly monitor and scrutinize access key creation events, especially if they are associated with unusual activity or are created by users who don't typically perform these actions. This hunting query identifies when a potentially compromised user creates a IAM access key for another user who may have higher privilleges, which can be a sign for privilege escalation. Hunting queries are designed to be executed manual during threat hunting.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This detection rule monitors for the creation of AWS Identity and Access Management (IAM) access keys. An IAM access key consists of an access key ID and secret access key, which are used to sign programmatic requests to AWS services. While IAM access keys can be legitimately used by developers and administrators for API access, their creation can also be indicative of malicious activity. Attackers who have gained unauthorized access to an AWS environment might create access keys as a means to establish persistence or to exfiltrate data through the APIs. Moreover, because access keys can be used to authenticate with AWS services without the need for further interaction, they can be particularly appealing for bad actors looking to operate under the radar. Consequently, it's important to vigilantly monitor and scrutinize access key creation events, especially if they are associated with unusual activity or are created by users who don't typically perform these actions. This hunting query identifies when a potentially compromised user creates a IAM access key for another user who may have higher privilleges, which can be a sign for privilege escalation. Hunting queries are designed to be executed manual during threat hunting.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.
action.escu.creation_date = 2022-03-03
action.escu.modification_date = 2022-03-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS CreateAccessKey - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS CreateAccessKey - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 13"], "confidence": 90, "impact": 70, "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName),1,0) | search match=0 | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`aws_createaccesskey_filter`

[ESCU - AWS CreateLoginProfile - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user A(victim A) creates a login profile for user B, followed by a AWS Console login event from user B from the same src_ip as user B. This correlated event can be indicative of privilege escalation since both events happened from the same src_ip
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user A(victim A) creates a login profile for user B, followed by a AWS Console login event from user B from the same src_ip as user B. This correlated event can be indicative of privilege escalation since both events happened from the same src_ip
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a login profile for another user.
action.escu.creation_date = 2021-07-19
action.escu.modification_date = 2021-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS CreateLoginProfile - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ is attempting to create a login profile for $requestParameters.userName$ and did a console login from this IP $src_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS CreateLoginProfile - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 13"], "confidence": 80, "impact": 90, "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events where a user A(victim A) creates a login profile for user B, followed by a AWS Console login event from user B from the same src_ip as user B. This correlated event can be indicative of privilege escalation since both events happened from the same src_ip
action.notable.param.rule_title = AWS CreateLoginProfile
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = CreateLoginProfile | rename requestParameters.userName as new_login_profile | table src_ip eventName new_login_profile userIdentity.userName  | join new_login_profile src_ip [| search `cloudtrail` eventName = ConsoleLogin | rename userIdentity.userName  as new_login_profile | stats count values(eventName) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn new_login_profile src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`] | `aws_createloginprofile_filter`

[ESCU - AWS Credential Access Failed Login - Rule]
action.escu = 0
action.escu.enabled = 1
description = It shows that there have been an unsuccessful attempt to log in using the user identity to the AWS management console. Since the user identity has access to AWS account services and resources, an attacker might try to brute force the password for that identity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = It shows that there have been an unsuccessful attempt to log in using the user identity to the AWS management console. Since the user identity has access to AWS account services and resources, an attacker might try to brute force the password for that identity.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = Users may genuinely mistype or forget the password.
action.escu.creation_date = 2022-08-07
action.escu.modification_date = 2022-08-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Credential Access Failed Login - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user$ has a login failure from IP $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Credential Access Failed Login - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = It shows that there have been an unsuccessful attempt to log in using the user identity to the AWS management console. Since the user identity has access to AWS account services and resources, an attacker might try to brute force the password for that identity.
action.notable.param.rule_title = AWS Credential Access Failed Login
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Authentication where Authentication.action = failure Authentication.app=AwsConsoleSignIn Authentication.signature=ConsoleLogin BY Authentication.app Authentication.signature Authentication.dest  Authentication.user Authentication.action Authentication.user_id Authentication.src | `drop_dm_object_name(Authentication)`  | `security_content_ctime(firstTime)`|  `security_content_ctime(lastTime)` | `aws_credential_access_failed_login_filter`

[ESCU - AWS Credential Access GetPasswordData - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection analytic identifies more than 10 GetPasswordData API calls made to your AWS account with a time window of 5 minutes. Attackers can retrieve the encrypted administrator password for a running Windows instance.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This detection analytic identifies more than 10 GetPasswordData API calls made to your AWS account with a time window of 5 minutes. Attackers can retrieve the encrypted administrator password for a running Windows instance.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. We encourage the users to adjust the values of `distinct_instance_ids` and tweak the `span` value according to their environment.
action.escu.known_false_positives = Administrator tooling or automated scripts may make these calls but it is highly unlikely to make several calls in a short period of time.
action.escu.creation_date = 2022-08-10
action.escu.modification_date = 2022-08-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Credential Access GetPasswordData - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ is seen to make mulitple `GetPasswordData` API calls to instance ids $instance_ids$ from IP $src_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Credential Access GetPasswordData - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=GetPasswordData eventSource = ec2.amazonaws.com |  bin _time span=5m |  stats count values(errorCode) as errorCode dc(requestParameters.instanceId) as distinct_instance_ids values(requestParameters.instanceId) as instance_ids by aws_account_id src_ip user_arn userAgent eventName _time |  where distinct_instance_ids > 10 | `aws_credential_access_getpassworddata_filter`

[ESCU - AWS Credential Access RDS Password reset - Rule]
action.escu = 0
action.escu.enabled = 1
description = The master user password for Amazon RDS DB instance can be reset using the Amazon RDS console. Using this technique, the attacker can get access to the sensitive data from the DB. Usually, the production databases may have sensitive data like Credit card information, PII, Health care Data. This event should be investigated further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The master user password for Amazon RDS DB instance can be reset using the Amazon RDS console. Using this technique, the attacker can get access to the sensitive data from the DB. Usually, the production databases may have sensitive data like Credit card information, PII, Health care Data. This event should be investigated further.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = Users may genuinely reset the RDS password.
action.escu.creation_date = 2022-08-07
action.escu.modification_date = 2022-08-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Credential Access RDS Password reset - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = $DB$ password has been reset from IP $sourceIPAddress$
action.risk.param._risk = [{"risk_object_field": "sourceIPAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Credential Access RDS Password reset - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1586", "T1586.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The master user password for Amazon RDS DB instance can be reset using the Amazon RDS console. Using this technique, the attacker can get access to the sensitive data from the DB. Usually, the production databases may have sensitive data like Credit card information, PII, Health care Data. This event should be investigated further.
action.notable.param.rule_title = AWS Credential Access RDS Password reset
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource="rds.amazonaws.com" eventName=ModifyDBInstance "requestParameters.masterUserPassword"=* | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.dBInstanceIdentifier) as DB by sourceIPAddress awsRegion eventName userAgent| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_credential_access_rds_password_reset_filter`

[ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro.
action.escu.known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request.
action.escu.creation_date = 2020-05-28
action.escu.modification_date = 2020-05-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"]
action.risk = 1
action.risk.param._risk_message = AWS account $requestingAccountId$ is trying to access resource from some other account $requestedAccountId$, for the first time.
action.risk.param._risk = [{"threat_object_field": "requestingAccountId", "threat_object_type": "other"}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Authentication Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 30, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") |  where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter`

[ESCU - AWS Defense Evasion Delete Cloudtrail - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies AWS `DeleteTrail` events within CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their malicious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may delete the the entire cloudtrail that is logging activities in the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies AWS `DeleteTrail` events within CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their malicious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may delete the the entire cloudtrail that is logging activities in the environment.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has stopped cloudTrail logging. Please investigate this activity.
action.escu.creation_date = 2022-07-13
action.escu.modification_date = 2022-07-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Defense Evasion Delete Cloudtrail - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Defense Evasion"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has delete a CloudTrail logging for account id $aws_account_id$ from IP $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Defense Evasion Delete Cloudtrail - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies AWS `DeleteTrail` events within CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their malicious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may delete the the entire cloudtrail that is logging activities in the environment.
action.notable.param.rule_title = AWS Defense Evasion Delete Cloudtrail
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = DeleteTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.name) as deleted_cloudtrail_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudtrail_filter`

[ESCU - AWS Defense Evasion Delete CloudWatch Log Group - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies AWS `DeleteLogGroup` events in CloudTrail logs. Attackers may evade the logging capability by deleting the log group in CloudWatch. This will stop sending the logs and metrics to CloudWatch. When the adversary has the right type of permissions within the compromised AWS environment, they may delete the CloudWatch log group that is logging activities in the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1562.008"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies AWS `DeleteLogGroup` events in CloudTrail logs. Attackers may evade the logging capability by deleting the log group in CloudWatch. This will stop sending the logs and metrics to CloudWatch. When the adversary has the right type of permissions within the compromised AWS environment, they may delete the CloudWatch log group that is logging activities in the environment.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has deleted CloudWatch logging. Please investigate this activity.
action.escu.creation_date = 2022-07-17
action.escu.modification_date = 2022-07-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Defense Evasion Delete CloudWatch Log Group - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Defense Evasion"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has deleted a CloudWatch logging group for account id $aws_account_id$ from IP $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Defense Evasion Delete CloudWatch Log Group - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562", "T1562.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies AWS `DeleteLogGroup` events in CloudTrail logs. Attackers may evade the logging capability by deleting the log group in CloudWatch. This will stop sending the logs and metrics to CloudWatch. When the adversary has the right type of permissions within the compromised AWS environment, they may delete the CloudWatch log group that is logging activities in the environment.
action.notable.param.rule_title = AWS Defense Evasion Delete CloudWatch Log Group
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = DeleteLogGroup eventSource = logs.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.logGroupName) as log_group_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudwatch_log_group_filter`

[ESCU - AWS Defense Evasion Impair Security Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for several delete specific API calls made to AWS Security Services like CloudWatch, GuardDuty and Web Application Firewalls. These API calls are often leveraged by adversaries to weaken existing security defenses by deleting logging configurations in the CloudWatch alarm, delete a set of detectors from your Guardduty environment or simply delete a bunch of CloudWatch alarms to remain stealthy and avoid detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This analytic looks for several delete specific API calls made to AWS Security Services like CloudWatch, GuardDuty and Web Application Firewalls. These API calls are often leveraged by adversaries to weaken existing security defenses by deleting logging configurations in the CloudWatch alarm, delete a set of detectors from your Guardduty environment or simply delete a bunch of CloudWatch alarms to remain stealthy and avoid detection.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
action.escu.known_false_positives = While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names.
action.escu.creation_date = 2022-07-26
action.escu.modification_date = 2022-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Defense Evasion Impair Security Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Defense Evasion"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Defense Evasion Impair Security Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms") | stats count min(_time) as firstTime max(_time) as lastTime values(eventName)  as eventName values(eventSource) as eventSource values(requestParameters.*) as * by src region user_arn aws_account_id user_type user_agent errorCode| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|  `aws_defense_evasion_impair_security_services_filter`

[ESCU - AWS Defense Evasion PutBucketLifecycle - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies `PutBucketLifecycle` events in CloudTrail logs where a user has created a new lifecycle rule for an S3 bucket with a short expiration period. Attackers may use this API call to impair the CloudTrail logging by removing logs from the S3 bucket by changing the object expiration day to 1 day, in which case the CloudTrail logs will be deleted.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies `PutBucketLifecycle` events in CloudTrail logs where a user has created a new lifecycle rule for an S3 bucket with a short expiration period. Attackers may use this API call to impair the CloudTrail logging by removing logs from the S3 bucket by changing the object expiration day to 1 day, in which case the CloudTrail logs will be deleted.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. We recommend our users to set the expiration days value according to your company's log retention policies.
action.escu.known_false_positives = While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names.
action.escu.creation_date = 2022-07-25
action.escu.modification_date = 2022-07-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Defense Evasion PutBucketLifecycle - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Defense Evasion"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Defense Evasion PutBucketLifecycle - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 40, "impact": 50, "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=PutBucketLifecycle user_type=IAMUser errorCode=success |  spath path=requestParameters{}.LifecycleConfiguration{}.Rule{}.Expiration{}.Days output=expiration_days |  spath path=requestParameters{}.bucketName output=bucket_name | stats count min(_time) as firstTime max(_time) as lastTime  by src region eventName userAgent user_arn aws_account_id expiration_days  bucket_name user_type| `security_content_ctime(firstTime)` |  `security_content_ctime(lastTime)` | where expiration_days < 3 | `aws_defense_evasion_putbucketlifecycle_filter`

[ESCU - AWS Defense Evasion Stop Logging Cloudtrail - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies `StopLogging` events in CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their macliious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may easily stop logging.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies `StopLogging` events in CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their macliious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may easily stop logging.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable Cloudtrail logs in your AWS Environment.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has stopped cloudtrail logging. Please investigate this activity.
action.escu.creation_date = 2022-07-12
action.escu.modification_date = 2022-07-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Defense Evasion Stop Logging Cloudtrail - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Defense Evasion"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has stopped Cloudtrail logging for account id $aws_account_id$ from IP $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Defense Evasion Stop Logging Cloudtrail - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562.008", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies `StopLogging` events in CloudTrail logs. Adversaries often try to impair their target's defenses by stopping their macliious activity from being logged, so that they may operate with stealth and avoid detection. When the adversary has the right type of permissions in the compromised AWS environment, they may easily stop logging.
action.notable.param.rule_title = AWS Defense Evasion Stop Logging Cloudtrail
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = StopLogging eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.name) as stopped_cloudtrail_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_defense_evasion_stop_logging_cloudtrail_filter`

[ESCU - AWS Defense Evasion Update Cloudtrail - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies `UpdateTrail` events in CloudTrail logs. Attackers may evade the logging capability by updating the settings and impairing them with wrong parameters. For example, Attackers may change the multi-regional log into a single region logs, which evades the logging for other regions. When the adversary has the right type of permissions in the compromised AWS environment, they may update the CloudTrail settings that is logging activities in your environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1562.008"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies `UpdateTrail` events in CloudTrail logs. Attackers may evade the logging capability by updating the settings and impairing them with wrong parameters. For example, Attackers may change the multi-regional log into a single region logs, which evades the logging for other regions. When the adversary has the right type of permissions in the compromised AWS environment, they may update the CloudTrail settings that is logging activities in your environment.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has updated cloudtrail logging. Please investigate this activity.
action.escu.creation_date = 2022-07-17
action.escu.modification_date = 2022-07-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Defense Evasion Update Cloudtrail - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Defense Evasion"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has updated a cloudtrail logging for account id $aws_account_id$ from IP $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Defense Evasion Update Cloudtrail - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Defense Evasion"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562", "T1562.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies `UpdateTrail` events in CloudTrail logs. Attackers may evade the logging capability by updating the settings and impairing them with wrong parameters. For example, Attackers may change the multi-regional log into a single region logs, which evades the logging for other regions. When the adversary has the right type of permissions in the compromised AWS environment, they may update the CloudTrail settings that is logging activities in your environment.
action.notable.param.rule_title = AWS Defense Evasion Update Cloudtrail
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = UpdateTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.name) as cloudtrail_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_update_cloudtrail_filter`

[ESCU - aws detect attach to role policy - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges.
action.escu.how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
action.escu.known_false_positives = Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies.
action.escu.creation_date = 2020-07-27
action.escu.modification_date = 2020-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - aws detect attach to role policy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - aws detect attach to role policy - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate  | `aws_detect_attach_to_role_policy_filter`

[ESCU - aws detect permanent key creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
action.escu.known_false_positives = Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context.
action.escu.creation_date = 2020-07-27
action.escu.modification_date = 2020-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - aws detect permanent key creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - aws detect permanent key creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter`

[ESCU - aws detect role creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges.
action.escu.how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
action.escu.known_false_positives = CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases.
action.escu.creation_date = 2020-07-27
action.escu.modification_date = 2020-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - aws detect role creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - aws detect role creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate | `aws_detect_role_creation_filter`

[ESCU - aws detect sts assume role abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
action.escu.known_false_positives = Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse.
action.escu.creation_date = 2020-07-27
action.escu.modification_date = 2020-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - aws detect sts assume role abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - aws detect sts assume role abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role | table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate | `aws_detect_sts_assume_role_abuse_filter`

[ESCU - aws detect sts get session token abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
action.escu.how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
action.escu.known_false_positives = Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used.
action.escu.creation_date = 2020-07-27
action.escu.modification_date = 2020-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - aws detect sts get session token abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - aws detect sts get session token abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1550"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser| spath eventName | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter`

[ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of KMS keys where action kms:Encrypt is accessible for everyone (also outside of your organization). This is an indicator that your account is compromised and the attacker uses the encryption key to compromise another company.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of KMS keys where action kms:Encrypt is accessible for everyone (also outside of your organization). This is an indicator that your account is compromised and the attacker uses the encryption key to compromise another company.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-01-11
action.escu.modification_date = 2021-01-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Ransomware Cloud"]
action.risk = 1
action.risk.param._risk_message = AWS account is potentially compromised and user $userIdentity.principalId$ is trying to compromise other accounts.
action.risk.param._risk = [{"risk_object_field": "userIdentity.principalId", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware Cloud"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1486"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search provides detection of KMS keys where action kms:Encrypt is accessible for everyone (also outside of your organization). This is an indicator that your account is compromised and the attacker uses the encryption key to compromise another company.
action.notable.param.rule_title = AWS Detect Users creating keys with encrypt policy without MFA
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`

[ESCU - AWS Detect Users with KMS keys performing encryption S3 - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of users with KMS keys performing encryption specifically against S3 buckets.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of users with KMS keys performing encryption specifically against S3 buckets.
action.escu.how_to_implement = You must install Splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
action.escu.known_false_positives = There maybe buckets provisioned with S3 encryption
action.escu.creation_date = 2022-11-11
action.escu.modification_date = 2022-11-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Detect Users with KMS keys performing encryption S3 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Ransomware Cloud"]
action.risk = 1
action.risk.param._risk_message = User $user$ with KMS keys is performing encryption, against S3 buckets on these files $dest_file$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}, {"risk_object_field": "dest_file", "risk_object_type": "other", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Detect Users with KMS keys performing encryption S3 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware Cloud"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucketName, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(bucketName) as bucketName values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`

[ESCU - AWS Disable Bucket Versioning - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects AWS cloudtrail events where bucket versioning is suspended by a user. Versioning allows the AWS Administrators to maintain different version of the S3 bucket which can be used to recover deleted data. Adversaries have leveraged this technique in the wild during a ransomware incident to disable versioning so the client cannot recover the data.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects AWS cloudtrail events where bucket versioning is suspended by a user. Versioning allows the AWS Administrators to maintain different version of the S3 bucket which can be used to recover deleted data. Adversaries have leveraged this technique in the wild during a ransomware incident to disable versioning so the client cannot recover the data.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that an AWS Administrator has legitimately disabled versioning on certain buckets to avoid costs.
action.escu.creation_date = 2023-05-01
action.escu.modification_date = 2023-05-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Disable Bucket Versioning - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = Bucket Versioning is suspended for S3 buckets- $bucket_name$ by user $user_arn$ from IP address $src_ip$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Disable Bucket Versioning - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1490"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName= PutBucketVersioning "requestParameters.VersioningConfiguration.Status"=Suspended |  stats count values(requestParameters.bucketName) as bucket_name values(resources{}.ARN) as resource_arn by src_ip aws_account_id awsRegion eventName userAgent user_arn userIdentity.principalId  errorCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_disable_bucket_versioning_filter`

[ESCU - AWS EC2 Snapshot Shared Externally - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes AWS CloudTrail events to identify when an EC2 snapshot permissions are modified to be shared with a different AWS account. This method is used by adversaries to exfiltrate the EC2 snapshot.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes AWS CloudTrail events to identify when an EC2 snapshot permissions are modified to be shared with a different AWS account. This method is used by adversaries to exfiltrate the EC2 snapshot.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that an AWS admin has legitimately shared a snapshot with others for  a specific purpose.
action.escu.creation_date = 2023-03-20
action.escu.modification_date = 2023-03-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS EC2 Snapshot Shared Externally - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious Cloud Instance Activities", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = AWS EC2 snapshot from account $aws_account_id$ is shared with $requested_account_id$ by user $user_arn$ from $src_ip$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS EC2 Snapshot Shared Externally - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes AWS CloudTrail events to identify when an EC2 snapshot permissions are modified to be shared with a different AWS account. This method is used by adversaries to exfiltrate the EC2 snapshot.
action.notable.param.rule_title = AWS EC2 Snapshot Shared Externally
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ModifySnapshotAttribute | rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id | search requested_account_id != NULL | eval match=if(requested_account_id==aws_account_id,"Match","No Match") | table _time user_arn src_ip requestParameters.attributeType requested_account_id aws_account_id match vendor_region user_agent userIdentity.principalId | where match = "No Match" | `aws_ec2_snapshot_shared_externally_filter` 

[ESCU - AWS ECR Container Scanning Findings High - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-06-21
action.escu.modification_date = 2022-06-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS ECR Container Scanning Findings High - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Vulnerabilities with severity high found in image $image$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS ECR Container Scanning Findings High - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 100, "impact": 70, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.notable.param.rule_title = AWS ECR Container Scanning Findings High
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand findings | spath input=findings| search severity=HIGH | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as image | eval finding = finding_name.", ".finding_description | eval phase="release" | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, image, userName, src_ip, finding, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_high_filter`

[ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Dev Sec Ops"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 10, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand findings | spath input=findings| search severity IN ("LOW", "INFORMATIONAL", "UNKNOWN") | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repositoryName | eval finding = finding_name.", ".finding_description | eval phase="release" | eval severity="low" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repositoryName, userName, src_ip, finding, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_low_informational_unknown_filter`

[ESCU - AWS ECR Container Scanning Findings Medium - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-17
action.escu.modification_date = 2021-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS ECR Container Scanning Findings Medium - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Vulnerabilities with severity high found in image $image$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 21}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS ECR Container Scanning Findings Medium - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 30, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand findings | spath input=findings| search severity=MEDIUM | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as image | eval finding = finding_name.", ".finding_description | eval phase="release" | eval severity="medium" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, image, userName, src_ip, finding, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_medium_filter`

[ESCU - AWS ECR Container Upload Outside Business Hours - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). A upload of a new container is normally done during business hours. When done outside business hours, we want to take a look into it.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). A upload of a new container is normally done during business hours. When done outside business hours, we want to take a look into it.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = When your development is spreaded in different time zones, applying this rule can be difficult.
action.escu.creation_date = 2021-08-19
action.escu.modification_date = 2021-08-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS ECR Container Upload Outside Business Hours - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Container uploaded outside business hours from $user$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS ECR Container Upload Outside Business Hours - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage date_hour>=20 OR date_hour<8 OR date_wday=saturday OR date_wday=sunday | rename requestParameters.* as * | rename repositoryName AS image | eval phase="release" | eval severity="medium" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, user, userName, src_ip, imageTag, registryId, image, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_upload_outside_business_hours_filter`

[ESCU - AWS ECR Container Upload Unknown User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). A upload of a new container is normally done from only a few known users. When the user was never seen before, we should have a closer look into the event.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). A upload of a new container is normally done from only a few known users. When the user was never seen before, we should have a closer look into the event.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-19
action.escu.modification_date = 2021-08-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS ECR Container Upload Unknown User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Container uploaded from unknown user $user$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS ECR Container Upload Unknown User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage NOT `aws_ecr_users` | rename requestParameters.* as * | rename repositoryName AS image | eval phase="release" | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, user, userName, src_ip, imageTag, registryId, image, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_upload_unknown_user_filter`

[ESCU - AWS Excessive Security Scanning - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1526"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives.
action.escu.creation_date = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Excessive Security Scanning - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = User $user$ has excessive number of api calls $dc_events$ from these IP addresses $src$, violating the threshold of 50, using the following commands $command$.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 18}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Excessive Security Scanning - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 13"], "confidence": 60, "impact": 30, "mitre_attack": ["T1526"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
action.notable.param.rule_title = AWS Excessive Security Scanning
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get*  | stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as command values(src) as src values(userAgent) as userAgent by user userIdentity.arn | where dc_events > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter`

[ESCU - AWS Exfiltration via Anomalous GetObject API Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search uses built in  Splunk command `| anomalydetection` to detect anomalies with respect to users making high number of GetObject API calls to download objects from S3 in a 10 minute time window. The field `probable_cause` is the name of the field that best explains why the event is anomalous. This command identifies anomalous events by computing a probability for each GetObject event by "count" "user_type" "user_arn" and detects anomaly based on the frequencies.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1119"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search uses built in  Splunk command `| anomalydetection` to detect anomalies with respect to users making high number of GetObject API calls to download objects from S3 in a 10 minute time window. The field `probable_cause` is the name of the field that best explains why the event is anomalous. This command identifies anomalous events by computing a probability for each GetObject event by "count" "user_type" "user_arn" and detects anomaly based on the frequencies.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that a user downloaded these files to use them locally and there are AWS services in configured that perform these activities for a legitimate reason. Filter is needed.
action.escu.creation_date = 2023-04-10
action.escu.modification_date = 2023-04-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Exfiltration via Anomalous GetObject API Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = Anomalous S3 activities detected by user $user_arn$ from $src_ip$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Exfiltration via Anomalous GetObject API Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1119"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=GetObject | bin _time span=10m |  stats count values(requestParameters.bucketName) as bucketName by _time src_ip aws_account_id user_type user_arn userIdentity.principalId | anomalydetection "count" "user_type" "user_arn" action=annotate | search probable_cause=* |`aws_exfiltration_via_anomalous_getobject_api_activity_filter`

[ESCU - AWS Exfiltration via Batch Service - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for events where AWS Batch Service is used for creating a job that could potentially abuse the AWS Bucket Replication feature on S3 buckets. This AWS service can used to transfer data between different AWS S3 buckets and an attacker can leverage this to exfiltrate data by creating a malicious batch job.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1119"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for events where AWS Batch Service is used for creating a job that could potentially abuse the AWS Bucket Replication feature on S3 buckets. This AWS service can used to transfer data between different AWS S3 buckets and an attacker can leverage this to exfiltrate data by creating a malicious batch job.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that an AWS Administrator or a user has legitimately created this job for some tasks.
action.escu.creation_date = 2023-04-24
action.escu.modification_date = 2023-04-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Exfiltration via Batch Service - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = AWS Batch Job is created on account id - $aws_account_id$ from src_ip $src_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Exfiltration via Batch Service - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1119"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for events where AWS Batch Service is used for creating a job that could potentially abuse the AWS Bucket Replication feature on S3 buckets. This AWS service can used to transfer data between different AWS S3 buckets and an attacker can leverage this to exfiltrate data by creating a malicious batch job.
action.notable.param.rule_title = AWS Exfiltration via Batch Service
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = JobCreated |  stats count min(_time) as firstTime max(_time) as lastTime values(serviceEventDetails.jobArn) as job_arn values(serviceEventDetails.status) as status by src_ip  aws_account_id eventName errorCode userAgent| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_datasync_task_filter`

[ESCU - AWS Exfiltration via Bucket Replication - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects API calls made to an S3 bucket when bucket replication services are enabled. S3 bucket replication is a feature offered by Amazon Web Services (AWS) that allows you to automatically and asynchronously copy data from one S3 bucket to another in the same or different region.\
S3 bucket replication can also be used for cross-account replication, where data is replicated from a source bucket owned by one AWS account to a destination bucket owned by a different AWS account.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects API calls made to an S3 bucket when bucket replication services are enabled. S3 bucket replication is a feature offered by Amazon Web Services (AWS) that allows you to automatically and asynchronously copy data from one S3 bucket to another in the same or different region.\
S3 bucket replication can also be used for cross-account replication, where data is replicated from a source bucket owned by one AWS account to a destination bucket owned by a different AWS account.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that an AWS admin has legitimately implemented data replication to ensure data availability and improve data protection/backup strategies.
action.escu.creation_date = 2023-04-28
action.escu.modification_date = 2023-04-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Exfiltration via Bucket Replication - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = AWS Bucket Replication rule $rule$ added on $source_bucket$ to $destination_bucket$ by user $user_arn$ from IP Address - $src_ip$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Exfiltration via Bucket Replication - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects API calls made to an S3 bucket when bucket replication services are enabled. S3 bucket replication is a feature offered by Amazon Web Services (AWS) that allows you to automatically and asynchronously copy data from one S3 bucket to another in the same or different region.\
S3 bucket replication can also be used for cross-account replication, where data is replicated from a source bucket owned by one AWS account to a destination bucket owned by a different AWS account.
action.notable.param.rule_title = AWS Exfiltration via Bucket Replication
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail`  eventName = PutBucketReplication eventSource = s3.amazonaws.com |  rename requestParameters.* as * | stats count values(bucketName) as source_bucket values(ReplicationConfiguration.Rule.ID) as rule_id values(ReplicationConfiguration.Rule.Destination.Bucket) as destination_bucket by _time user_arn userName user_type src_ip aws_account_id userIdentity.principalId user_agent | `aws_exfiltration_via_ec2_snapshot_filter`

[ESCU - AWS Exfiltration via DataSync Task - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for potential misuse of an AWS service known as DataSync. This AWS service is used to transfer data between different AWS cloud storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server. Attackers can create a task in AWS to periodically copy data from a private AWS location to a public location resulting in the compromise of the data.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1119"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for potential misuse of an AWS service known as DataSync. This AWS service is used to transfer data between different AWS cloud storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server. Attackers can create a task in AWS to periodically copy data from a private AWS location to a public location resulting in the compromise of the data.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = It is possible that an AWS Administrator has legitimately created this task for creating backup. Please check the `sourceLocationArn` and `destinationLocationArn` of this task
action.escu.creation_date = 2023-04-10
action.escu.modification_date = 2023-04-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Exfiltration via DataSync Task - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = DataSync task created on account id - $aws_account_id$ by user $user_arn$ from src_ip $src_ip$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Exfiltration via DataSync Task - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1119"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for potential misuse of an AWS service known as DataSync. This AWS service is used to transfer data between different AWS cloud storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server. Attackers can create a task in AWS to periodically copy data from a private AWS location to a public location resulting in the compromise of the data.
action.notable.param.rule_title = AWS Exfiltration via DataSync Task
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName = CreateTask eventSource="datasync.amazonaws.com" | rename  requestParameters.*  as *  | stats count min(_time) as firstTime max(_time) as lastTime by src_ip aws_account_id awsRegion eventName destinationLocationArn sourceLocationArn userAgent user_arn userIdentity.principalId  errorCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_datasync_task_filter`

[ESCU - AWS Exfiltration via EC2 Snapshot - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects a series of AWS API calls, made in a short time window, related to EC2 snapshots that can detect a potential exfiltration via EC2 Snapshot modifications. In this attack, the attacker typically proceeds by listing and creating EC2 snapshots of the available EC2 instances followed by modifying snapshot attributes such that it can be shared externally. Once this is done, the attacker can then load that EC2 snapshot and access all the sensitive information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects a series of AWS API calls, made in a short time window, related to EC2 snapshots that can detect a potential exfiltration via EC2 Snapshot modifications. In this attack, the attacker typically proceeds by listing and creating EC2 snapshots of the available EC2 instances followed by modifying snapshot attributes such that it can be shared externally. Once this is done, the attacker can then load that EC2 snapshot and access all the sensitive information.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. We have intentionally removed `guardduty.amazonaws.com` from src_ip to remove false positives caused by guard duty. We recommend you adjust the time window as per your environment.
action.escu.known_false_positives = It is possible that an AWS admin has legitimately shared a snapshot with an other account for a specific purpose. Please check any recent change requests filed in your organization.
action.escu.creation_date = 2023-03-22
action.escu.modification_date = 2023-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Exfiltration via EC2 Snapshot - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious Cloud Instance Activities", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = Potential AWS EC2 Exfiltration detected on account id - $aws_account_id$ by user $userName$ from src_ip $src_ip$
action.risk.param._risk = [{"risk_object_field": "userName", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Exfiltration via EC2 Snapshot - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1537"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects a series of AWS API calls, made in a short time window, related to EC2 snapshots that can detect a potential exfiltration via EC2 Snapshot modifications. In this attack, the attacker typically proceeds by listing and creating EC2 snapshots of the available EC2 instances followed by modifying snapshot attributes such that it can be shared externally. Once this is done, the attacker can then load that EC2 snapshot and access all the sensitive information.
action.notable.param.rule_title = AWS Exfiltration via EC2 Snapshot
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName IN ("CreateSnapshot", "DescribeSnapshotAttribute", "ModifySnapshotAttribute", "DeleteSnapshot") src_ip !="guardduty.amazonaws.com" |  bin _time span=5m |  stats count dc(eventName) as distinct_api_calls values(eventName)  values(requestParameters.attributeType) as attributeType values(requestParameters.createVolumePermission.add.items{}.userId) as aws_account_id_added values(userAgent) as userAgent by _time userName src_ip aws_account_id | where distinct_api_calls >= 2 | `aws_exfiltration_via_ec2_snapshot_filter`

[ESCU - AWS High Number Of Failed Authentications For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an AWS account with more than 20 failed authentication events in the span of 5 minutes. This behavior could represent a brute force attack against the account. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an AWS account with more than 20 failed authentication events in the span of 5 minutes. This behavior could represent a brute force attack against the account. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.how_to_implement = You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = A user with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
action.escu.creation_date = 2023-01-27
action.escu.modification_date = 2023-01-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS High Number Of Failed Authentications For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Compromised User Account", "AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user_name$ failed to authenticate more than 20 times in the span of 5 minutes for AWS Account $aws_account_id$
action.risk.param._risk = [{"risk_object_field": "user_name", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS High Number Of Failed Authentications For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time | stats dc(_raw) AS failed_attempts values(src_ip) as src_ip values(user_agent) by _time, user_name, eventName, eventSource aws_account_id | where failed_attempts > 20 | `aws_high_number_of_failed_authentications_for_user_filter`

[ESCU - AWS High Number Of Failed Authentications From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an IP address failing to authenticate 20 or more times to the AWS Web Console in the span of 5 minutes. This behavior could represent a brute force attack against an AWS tenant to obtain initial access or elevate privileges. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an IP address failing to authenticate 20 or more times to the AWS Web Console in the span of 5 minutes. This behavior could represent a brute force attack against an AWS tenant to obtain initial access or elevate privileges. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.how_to_implement = You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the tried account threshold to tune this search according to their environment.
action.escu.known_false_positives = An Ip address with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
action.escu.creation_date = 2023-01-30
action.escu.modification_date = 2023-01-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS High Number Of Failed Authentications From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover", "Compromised User Account"]
action.risk = 1
action.risk.param._risk_message = Multiple failed console login attempts against users $tried_accounts$ seen from $src_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 54}, {"risk_object_field": "tried_accounts", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS High Number Of Failed Authentications From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin action=failure | bucket span=5m _time | stats dc(_raw) AS failed_attempts values(user_name) as tried_accounts values(user_agent) by _time, src_ip, eventName, eventSource aws_account_id | where failed_attempts > 20 | `aws_high_number_of_failed_authentications_from_ip_filter`

[ESCU - AWS IAM AccessDenied Discovery Events - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1580"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS IAM AccessDenied Discovery Events - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.risk = 1
action.risk.param._risk_message = User $userIdentity.arn$ is seen to perform excessive number of discovery related api calls- $failures$, within an hour where the access was denied.
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "userIdentity.arn", "risk_object_type": "user", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS IAM AccessDenied Discovery Events - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1580"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) | bucket _time span=1h | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources by src_ip, userIdentity.arn, _time | where failures >= 5 and methods >= 1 and sources >= 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_accessdenied_discovery_events_filter`

[ESCU - AWS IAM Assume Role Policy Brute Force - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`.  Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1580", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`.  Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.
action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.
action.escu.creation_date = 2021-04-01
action.escu.modification_date = 2021-04-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS IAM Assume Role Policy Brute Force - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has caused multiple failures with errorCode $errorCode$, which potentially means adversary is attempting to identify a role name.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 28}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 28}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS IAM Assume Role Policy Brute Force - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1580", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`.  Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
action.notable.param.rule_title = AWS IAM Assume Role Policy Brute Force
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn | where count >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter`

[ESCU - AWS IAM Delete Policy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
action.escu.creation_date = 2021-04-01
action.escu.modification_date = 2021-04-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS IAM Delete Policy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS IAM Delete Policy - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1098"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src user_arn eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_delete_policy_filter`

[ESCU - AWS IAM Failure Group Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.
action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
action.escu.creation_date = 2021-04-01
action.escu.modification_date = 2021-04-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS IAM Failure Group Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has had mulitple failures while attempting to delete groups from $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 5}, {"risk_object_field": "group_name", "risk_object_type": "user", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS IAM Failure Group Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1098"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_failure_group_deletion_filter`

[ESCU - AWS IAM Successful Group Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069.003", "T1098", "T1069"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.
action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS IAM Successful Group Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS IAM Successful Group Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1069.003", "T1098", "T1069"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_deleted by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_successful_group_deletion_filter`

[ESCU - AWS Lambda UpdateFunctionCode - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to detect IAM users attempting to update/modify AWS lambda code via the AWS CLI to gain persistence, futher access into your AWS environment and to facilitate planting backdoors. In this instance, an attacker may upload malicious code/binary to a lambda function which will be executed automatically when the funnction is triggered.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is designed to detect IAM users attempting to update/modify AWS lambda code via the AWS CLI to gain persistence, futher access into your AWS environment and to facilitate planting backdoors. In this instance, an attacker may upload malicious code/binary to a lambda function which will be executed automatically when the funnction is triggered.
action.escu.how_to_implement = You must install Splunk AWS Add on and enable Cloudtrail logs in your AWS Environment.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin or an autorized IAM user has updated the lambda fuction code legitimately.
action.escu.creation_date = 2022-02-24
action.escu.modification_date = 2022-02-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Lambda UpdateFunctionCode - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Lambda UpdateFunctionCode - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1204"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=lambda.amazonaws.com eventName=UpdateFunctionCode*  errorCode = success  user_type=IAMUser | stats  count min(_time) as firstTime max(_time) as lastTime  values(requestParameters.functionName) as function_updated by src_ip user_arn user_agent user_type eventName aws_account_id |`aws_lambda_updatefunctioncode_filter`

[ESCU - AWS Multi-Factor Authentication Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an attempt to disable multi-factor authentication for an AWS IAM user. An adversary who has obtained access to an AWS tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1621", "T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an attempt to disable multi-factor authentication for an AWS IAM user. An adversary who has obtained access to an AWS tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.how_to_implement = The Splunk AWS Add-on is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = AWS Administrators may disable MFA but it is highly unlikely for this event to occur without prior notice to the company
action.escu.creation_date = 2022-10-04
action.escu.modification_date = 2022-10-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Multi-Factor Authentication Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user_name$ has disabled Multi-Factor authentication for AWS account $aws_account_id$
action.risk.param._risk = [{"risk_object_field": "aws_account_id", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "user_name", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Multi-Factor Authentication Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1586", "T1586.003", "T1621", "T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an attempt to disable multi-factor authentication for an AWS IAM user. An adversary who has obtained access to an AWS tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.notable.param.rule_title = AWS Multi-Factor Authentication Disabled
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (eventName= DeleteVirtualMFADevice OR eventName=DeactivateMFADevice) | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource aws_account_id userAgent eventID awsRegion user_name userIdentity.arn status | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_multi_factor_authentication_disabled_filter`

[ESCU - AWS Multiple Failed MFA Requests For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies multiple failed multi-factor authentication requests to an AWS Console for a single user. AWS Cloudtrail logs provide a a very useful field called `additionalEventData` that logs information regarding usage of MFA. Specifically, the analytic triggers when more than 10 MFA user prompts fail within 10 minutes. AWS Environments can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1621"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies multiple failed multi-factor authentication requests to an AWS Console for a single user. AWS Cloudtrail logs provide a a very useful field called `additionalEventData` that logs information regarding usage of MFA. Specifically, the analytic triggers when more than 10 MFA user prompts fail within 10 minutes. AWS Environments can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.escu.how_to_implement = The Splunk AWS Add-on is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
action.escu.creation_date = 2022-10-03
action.escu.modification_date = 2022-10-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Multiple Failed MFA Requests For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user_name$ is seen to have high number of MFA prompt failures within a short period of time.
action.risk.param._risk = [{"risk_object_field": "user_name", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Multiple Failed MFA Requests For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1586", "T1586.003", "T1621"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName= ConsoleLogin "additionalEventData.MFAUsed"=Yes errorMessage="Failed authentication" | bucket span=5m _time |  stats dc(_raw) as mfa_prompts values(userAgent) as userAgent values(src) as src by _time user_name user_arn aws_account_id eventName errorMessage | where mfa_prompts > 10| `aws_multiple_failed_mfa_requests_for_user_filter`

[ESCU - AWS Multiple Users Failing To Authenticate From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source Ip failing to authenticate into the AWS Console with 30 unique valid users within 10 minutes. This behavior could represent an adversary performing a Password Spraying attack against an AWS environment tenant to obtain initial access or elevate privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source Ip failing to authenticate into the AWS Console with 30 unique valid users within 10 minutes. This behavior could represent an adversary performing a Password Spraying attack against an AWS environment tenant to obtain initial access or elevate privileges.
action.escu.how_to_implement = You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the tried account threshold to tune this search according to their environment.
action.escu.known_false_positives = No known false postives for this detection. Please review this alert
action.escu.creation_date = 2022-09-27
action.escu.modification_date = 2022-09-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Multiple Users Failing To Authenticate From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover", "Compromised User Account"]
action.risk = 1
action.risk.param._risk_message = Multiple failed console login attempts against users $tried_accounts$ seen from $src_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 54}, {"risk_object_field": "tried_accounts", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Multiple Users Failing To Authenticate From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time | stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts by _time, src_ip, eventName, action, user_agent |  where unique_accounts > 30 |`aws_unusual_number_of_failed_authentications_from_ip_filter`

[ESCU - AWS Network Access Control List Created with All Open Ports - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for AWS CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The search looks for AWS CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail inputs.
action.escu.known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment.
action.escu.creation_date = 2021-01-11
action.escu.modification_date = 2021-01-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Network Access Control List Created with All Open Ports - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has created network ACLs with all the ports open to a specified CIDR $requestParameters.cidrBlock$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Network Access Control List Created with All Open Ports - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Network ACL Activity"], "cis20": ["CIS 13"], "confidence": 80, "impact": 60, "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for AWS CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR.
action.notable.param.rule_title = AWS Network Access Control List Created with All Open Ports
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName user_arn userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter`

[ESCU - AWS Network Access Control List Deleted - Rule]
action.escu = 0
action.escu.enabled = 1
description = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the AWS CloudTrail logs to detect users deleting network ACLs.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the AWS CloudTrail logs to detect users deleting network ACLs.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs.
action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL.
action.escu.creation_date = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ from $src$ has sucessfully deleted network ACLs entry (eventName= $eventName$), such that the instance is accessible from anywhere
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Network Access Control List Deleted - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Network ACL Activity"], "cis20": ["CIS 13"], "confidence": 50, "impact": 10, "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter`

[ESCU - AWS New MFA Method Registered For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the registration of a new Multi Factor authentication method for an AWS account. Adversaries who have obtained unauthorized access to an AWS account may register a new MFA method to maintain persistence.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the registration of a new Multi Factor authentication method for an AWS account. Adversaries who have obtained unauthorized access to an AWS account may register a new MFA method to maintain persistence.
action.escu.how_to_implement = You must install Splunk AWS add on and Splunk App for AWS. This search works when AWS CloudTrail logs.
action.escu.known_false_positives = Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
action.escu.creation_date = 2023-01-31
action.escu.modification_date = 2023-01-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS New MFA Method Registered For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = A new virtual device $virtualMFADeviceName$ is added to user $user_arn$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS New MFA Method Registered For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the registration of a new Multi Factor authentication method for an AWS account. Adversaries who have obtained unauthorized access to an AWS account may register a new MFA method to maintain persistence.
action.notable.param.rule_title = AWS New MFA Method Registered For User
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `cloudtrail` eventName=CreateVirtualMFADevice | stats count values(requestParameters.virtualMFADeviceName) as virtualMFADeviceName min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_new_mfa_method_registered_for_user_filter`

[ESCU - AWS Password Policy Changes - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user is making successful API calls to view/update/delete the existing password policy in an AWS organization. It is unlikely for a regular user to conduct this operation. These events may potentially be malicious, adversaries often use this information to gain more understanding of the password defenses in place and exploit them to increase their attack surface when a user account is compromised.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user is making successful API calls to view/update/delete the existing password policy in an AWS organization. It is unlikely for a regular user to conduct this operation. These events may potentially be malicious, adversaries often use this information to gain more understanding of the password defenses in place and exploit them to increase their attack surface when a user account is compromised.
action.escu.how_to_implement = You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately triggered an AWS audit tool activity which may trigger this event.
action.escu.creation_date = 2023-01-26
action.escu.modification_date = 2023-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Password Policy Changes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation", "Compromised User Account"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Password Policy Changes - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy") errorCode=success | stats count values(eventName) as eventName values(userAgent) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode  awsRegion userIdentity.principalId user_arn src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  | `aws_password_policy_changes_filter`

[ESCU - AWS S3 Exfiltration Behavior Identified - Rule]
action.escu = 0
action.escu.enabled = 1
description = This correlation search looks at the risk events created by the detection analytics related Collection and Exfiltration techniques used by adversaries. The rule is designed to identify instances where 2 or more analytics unique AWS analytics and 2 or more distinct mitre IDs has triggered for a particular risk object. This alert when triggered may indicate a potential exfiltration in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1537"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = This correlation search looks at the risk events created by the detection analytics related Collection and Exfiltration techniques used by adversaries. The rule is designed to identify instances where 2 or more analytics unique AWS analytics and 2 or more distinct mitre IDs has triggered for a particular risk object. This alert when triggered may indicate a potential exfiltration in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.
action.escu.how_to_implement = You must enable all the detection searches in the Data Exfiltration Analytic story to create risk events in Enterprise Security.
action.escu.known_false_positives = alse positives may be present based on automated tooling or system administrators. Filter as needed.
action.escu.creation_date = 2023-05-04
action.escu.modification_date = 2023-05-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS S3 Exfiltration Behavior Identified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Instance Activities", "Data Exfiltration"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - AWS S3 Exfiltration Behavior Identified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1537"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This correlation search looks at the risk events created by the detection analytics related Collection and Exfiltration techniques used by adversaries. The rule is designed to identify instances where 2 or more analytics unique AWS analytics and 2 or more distinct mitre IDs has triggered for a particular risk object. This alert when triggered may indicate a potential exfiltration in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.
action.notable.param.rule_title = RBA: AWS S3 Exfiltration Behavior Identified
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count values(All_Risk.risk_message) as risk_message  from datamodel=Risk.All_Risk where All_Risk.annotations.mitre_attack.mitre_tactic = "collection" OR All_Risk.annotations.mitre_attack.mitre_tactic = "exfiltration" source = *AWS*  by All_Risk.risk_object | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 2 and mitre_tactic_id_count>=2 | `aws_s3_exfiltration_behavior_identified_filter`

[ESCU - AWS SAML Access by Provider User and Principal - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
action.escu.known_false_positives = Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks.
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS SAML Access by Provider User and Principal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = From IP address $sourceIPAddress$, user agent $userAgent$ has trigged an event $eventName$ for account ID $recipientAccountId$
action.risk.param._risk = [{"risk_object_field": "sourceIPAddress", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "recipientAccountId", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS SAML Access by Provider User and Principal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter`

[ESCU - AWS SAML Update identity provider - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker.
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored.
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS SAML Update identity provider - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $userIdentity.principalId$ from IP address $sourceIPAddress$ has trigged an event $eventName$ to update the SAML provider to $requestParameters.sAMLProviderArn$
action.risk.param._risk = [{"risk_object_field": "sourceIPAddress", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "userIdentity.principalId", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS SAML Update identity provider - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1078"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker.
action.notable.param.rule_title = AWS SAML Update identity provider
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter`

[ESCU - AWS SetDefaultPolicyVersion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user has set a default policy versions. Attackers have been know to use this technique for Privilege Escalation in case the previous versions of the policy had permissions to access more resources than the current version of the policy
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user has set a default policy versions. Attackers have been know to use this technique for Privilege Escalation in case the previous versions of the policy had permissions to access more resources than the current version of the policy
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately set a default policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources
action.escu.creation_date = 2021-03-02
action.escu.modification_date = 2021-03-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS SetDefaultPolicyVersion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = From IP address $src$, user $user_arn$ has trigged an event $eventName$ for updating the the default policy version
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS SetDefaultPolicyVersion - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events where a user has set a default policy versions. Attackers have been know to use this technique for Privilege Escalation in case the previous versions of the policy had permissions to access more resources than the current version of the policy
action.notable.param.rule_title = AWS SetDefaultPolicyVersion
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policy_arn by src requestParameters.versionId eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_setdefaultpolicyversion_filter`

[ESCU - AWS Successful Console Authentication From Multiple IPs - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an AWS account successfully authenticating from more than one unique Ip address in the span of 5 minutes. This behavior could represent an adversary who has stolen credentials via a phishing attack or some other method and using them to access corporate online resources around the same time as a legitimate user. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1535"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an AWS account successfully authenticating from more than one unique Ip address in the span of 5 minutes. This behavior could represent an adversary who has stolen credentials via a phishing attack or some other method and using them to access corporate online resources around the same time as a legitimate user. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.how_to_implement = You must install Splunk AWS add on and Splunk App for AWS. This search works when AWS CloudTrail events are normalized use the Authentication datamodel.
action.escu.known_false_positives = A user with successful authentication events from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
action.escu.creation_date = 2023-01-19
action.escu.modification_date = 2023-01-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Successful Console Authentication From Multiple IPs - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Compromised User Account"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has successfully logged into the AWS Console from different IP addresses $src$ within 5 mins
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Successful Console Authentication From Multiple IPs - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1586", "T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `cloudtrail` eventName = ConsoleLogin | bin span=5m _time | stats values(userAgent) values(eventName) values(src_ip) dc(src_ip) as distinct_ip_count by _time user_arn | where distinct_ip_count>1 | `aws_successful_console_authentication_from_multiple_ips_filter`

[ESCU - AWS Successful Single-Factor Authentication - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a successful Console Login authentication event against an AWS IAM user for an account without Multi-Factor Authentication enabled. This could be evidence of a misconfiguration, a policy violation or an account take over attempt that should be investigated
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a successful Console Login authentication event against an AWS IAM user for an account without Multi-Factor Authentication enabled. This could be evidence of a misconfiguration, a policy violation or an account take over attempt that should be investigated
action.escu.how_to_implement = The Splunk AWS Add-on is required to utilize this data. The search requires AWS Cloudtrail logs.
action.escu.known_false_positives = It is possible that some accounts do not have MFA enabled for the AWS account however its agaisnt the best practices of securing AWS.
action.escu.creation_date = 2022-10-04
action.escu.modification_date = 2022-10-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Successful Single-Factor Authentication - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user_name$ has successfully logged into an AWS Console without Multi-Factor Authentication from $src$
action.risk.param._risk = [{"risk_object_field": "user_name", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Successful Single-Factor Authentication - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a successful Console Login authentication event against an AWS IAM user for an account without Multi-Factor Authentication enabled. This could be evidence of a misconfiguration, a policy violation or an account take over attempt that should be investigated
action.notable.param.rule_title = AWS Successful Single-Factor Authentication
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName= ConsoleLogin errorCode=success "additionalEventData.MFAUsed"=No | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource aws_account_id errorCode additionalEventData.MFAUsed userAgent eventID awsRegion user_name userIdentity.arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_successful_single_factor_authentication_filter`

[ESCU - AWS Unusual Number of Failed Authentications From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source IP failing to authenticate into the AWS Console with multiple valid users. This behavior could represent an adversary performing a Password Spraying attack against an AWS environment to obtain initial access or elevate privileges. The detection calculates the standard deviation for source IP and leverages the 3-sigma statistical rule to identify an unusual number of failed authentication attempts. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises. While looking for anomalies using statistical methods like the standard deviation can have benefits, we also recommend using threshold-based detections to complement coverage. A similar analytic following the threshold model is `AWS Multiple Users Failing To Authenticate From Ip`.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source IP failing to authenticate into the AWS Console with multiple valid users. This behavior could represent an adversary performing a Password Spraying attack against an AWS environment to obtain initial access or elevate privileges. The detection calculates the standard deviation for source IP and leverages the 3-sigma statistical rule to identify an unusual number of failed authentication attempts. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises. While looking for anomalies using statistical methods like the standard deviation can have benefits, we also recommend using threshold-based detections to complement coverage. A similar analytic following the threshold model is `AWS Multiple Users Failing To Authenticate From Ip`.
action.escu.how_to_implement = You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the calculation of the upperBound field to tune this search according to their environment
action.escu.known_false_positives = No known false postives for this detection. Please review this alert
action.escu.creation_date = 2022-09-26
action.escu.modification_date = 2022-09-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Unusual Number of Failed Authentications From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Identity and Access Management Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Unusual number of failed console login attempts against users $tried_accounts$ seen from $src_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 54}, {"risk_object_field": "tried_accounts", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS Unusual Number of Failed Authentications From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time | stats  dc(_raw) AS unique_accounts values(user_name) as tried_accounts by _time, src_ip | eventstats  avg(unique_accounts) as ip_avg , stdev(unique_accounts) as ip_std by _time | eval  upperBound=(ip_avg+ip_std*3) | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | where isOutlier = 1 |`aws_unusual_number_of_failed_authentications_from_ip_filter`

[ESCU - AWS UpdateLoginProfile - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user A who has already permission to update login profile, makes an API call to update login profile for another user B . Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user A who has already permission to update login profile, makes an API call to update login profile for another user B . Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.
action.escu.creation_date = 2022-03-03
action.escu.modification_date = 2022-03-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS UpdateLoginProfile - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = From IP address $src$, user agent $userAgent$ has trigged an event $eventName$ for updating the existing login profile, potentially giving user $user_arn$ more access privilleges
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AWS UpdateLoginProfile - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events where a user A who has already permission to update login profile, makes an API call to update login profile for another user B . Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)
action.notable.param.rule_title = AWS UpdateLoginProfile
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName), 1,0) | search match=0 | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.userName user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_updateloginprofile_filter`

[ESCU - Azure Active Directory High Risk Sign-in - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic triggers on a high risk sign-in against Azure Active Directory identified by Azure Identity Protection. Identity Protection monitors sign-in events using heuristics and machine learning to identify potentially malicious events and categorizes them in three categories high, medium and low.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following analytic triggers on a high risk sign-in against Azure Active Directory identified by Azure Identity Protection. Identity Protection monitors sign-in events using heuristics and machine learning to identify potentially malicious events and categorizes them in three categories high, medium and low.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the RiskyUsers and UserRiskEvents log category.
action.escu.known_false_positives = Details for the risk calculation algorithm used by Identity Protection are unknown and may be prone to false positives.
action.escu.creation_date = 2022-07-11
action.escu.modification_date = 2022-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure Active Directory High Risk Sign-in - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = A high risk event was identified by Identify Protection for user $userPrincipalName$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 54}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure Active Directory High Risk Sign-in - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic triggers on a high risk sign-in against Azure Active Directory identified by Azure Identity Protection. Identity Protection monitors sign-in events using heuristics and machine learning to identify potentially malicious events and categorizes them in three categories high, medium and low.
action.notable.param.rule_title = Azure Active Directory High Risk Sign-in
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=UserRiskEvents properties.riskLevel=high | rename properties.* as * |  stats values(userPrincipalName) as userPrincipalName by _time, ipAddress, activity, riskLevel, riskEventType, additionalInfo | `azure_active_directory_high_risk_sign_in_filter`

[ESCU - Azure AD Application Administrator Role Assigned - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the assignment of the Application Administrator role to an Azure AD user. Users in this role can create and manage all aspects of enterprise applications, application registrations, and application proxy settings. This role also grants the ability to manage application credentials. Users assigned this role can add credentials to an application, and use those credentials to impersonate the applications identity. If the applications identity has been granted access to a resource, such as the ability to create or update User or other objects, then a user assigned to this role could perform those actions while impersonating the application. This ability to impersonate the applications identity may be an elevation of privilege over what the user can do via their role assignments. Red teams and adversaries alike may abuse this role to escalate their privileges in an Azure AD tenant.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the assignment of the Application Administrator role to an Azure AD user. Users in this role can create and manage all aspects of enterprise applications, application registrations, and application proxy settings. This role also grants the ability to manage application credentials. Users assigned this role can add credentials to an application, and use those credentials to impersonate the applications identity. If the applications identity has been granted access to a resource, such as the ability to create or update User or other objects, then a user assigned to this role could perform those actions while impersonating the application. This ability to impersonate the applications identity may be an elevation of privilege over what the user can do via their role assignments. Red teams and adversaries alike may abuse this role to escalate their privileges in an Azure AD tenant.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrators may legitimately assign the Application Administrator role to a user. Filter as needed.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Application Administrator Role Assigned - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = The privileged Azure AD role Application Administrator was assigned for User $userPrincipalName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Application Administrator Role Assigned - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the assignment of the Application Administrator role to an Azure AD user. Users in this role can create and manage all aspects of enterprise applications, application registrations, and application proxy settings. This role also grants the ability to manage application credentials. Users assigned this role can add credentials to an application, and use those credentials to impersonate the applications identity. If the applications identity has been granted access to a resource, such as the ability to create or update User or other objects, then a user assigned to this role could perform those actions while impersonating the application. This ability to impersonate the applications identity may be an elevation of privilege over what the user can do via their role assignments. Red teams and adversaries alike may abuse this role to escalate their privileges in an Azure AD tenant.
action.notable.param.rule_title = Azure AD Application Administrator Role Assigned
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` "operationName"="Add member to role"  "properties.targetResources{}.modifiedProperties{}.newValue"="\"Application Administrator\"" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | stats values(userPrincipalName) as userPrincipalName by _time, initiatedBy, result, operationName | `azure_ad_application_administrator_role_assigned_filter`

[ESCU - Azure AD Authentication Failed During MFA Challenge - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an authentication attempt event against an Azure AD tenant that fails during the Multi Factor Authentication challenge. Error Code 500121 represents a failed attempt to authenticate using a second factor. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004", "T1621"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an authentication attempt event against an Azure AD tenant that fails during the Multi Factor Authentication challenge. Error Code 500121 represents a failed attempt to authenticate using a second factor. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled. 
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake.
action.escu.creation_date = 2022-07-14
action.escu.modification_date = 2022-07-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Authentication Failed During MFA Challenge - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $userPrincipalName$ failed to pass MFA challenge
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 54}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Authentication Failed During MFA Challenge - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004", "T1621"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an authentication attempt event against an Azure AD tenant that fails during the Multi Factor Authentication challenge. Error Code 500121 represents a failed attempt to authenticate using a second factor. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled. 
action.notable.param.rule_title = Azure AD Authentication Failed During MFA Challenge
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=SignInLogs properties.status.errorCode=500121 | rename properties.* as * | stats values(userPrincipalName) as userPrincipalName by _time, ipAddress, status.additionalDetails, appDisplayName, userAgent | `azure_ad_authentication_failed_during_mfa_challenge_filter`

[ESCU - Azure AD Concurrent Sessions From Different Ips - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an Azure AD account with concurrent sessions coming from more than one unique Ip address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1185"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an Azure AD account with concurrent sessions coming from more than one unique Ip address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
action.escu.creation_date = 2023-01-24
action.escu.modification_date = 2023-01-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Concurrent Sessions From Different Ips - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Compromised User Account", "Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $userPrincipalName$ has concurrent sessions from more than one unique IP address in the span of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Concurrent Sessions From Different Ips - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1185"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an Azure AD account with concurrent sessions coming from more than one unique Ip address within the span of 5 minutes. This behavior could represent a session hijacking attack whereby an adversary has extracted cookies from a victims browser and is using them from a different location to access corporate online resources. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.notable.param.rule_title = Azure AD Concurrent Sessions From Different Ips
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` properties.authenticationDetails{}.succeeded=true category=NonInteractiveUserSignInLogs | rename properties.* as * | bucket span=5m _time | stats  dc(ipAddress) AS unique_ips values(ipAddress) as ipAddress values(appDisplayName) by _time, userPrincipalName | where unique_ips > 1 | `azure_ad_concurrent_sessions_from_different_ips_filter`

[ESCU - Azure AD External Guest User Invited - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the invitation of an external guest user within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the invitation of an external guest user within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrator may legitimately invite external guest users. Filter as needed.
action.escu.creation_date = 2022-08-18
action.escu.modification_date = 2022-08-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD External Guest User Invited - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = External Guest User $userPrincipalName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD External Guest User Invited - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1136.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the invitation of an external guest user within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`
action.notable.param.rule_title = Azure AD External Guest User Invited
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `azuread` "operationName"="Invite external user" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.type as type | stats values(userPrincipalName) as userPrincipalName by _time, type, initiatedBy, result, operationName | `azure_ad_external_guest_user_invited_filter`

[ESCU - Azure AD Global Administrator Role Assigned - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the assignment of the Azure AD Global Administrator role to an Azure AD user. The Global Administrator role is the most powerful administrator role in Azure AD and provides almost unlimited access to data, resources and settings. It is equivalent to the Domain Administrator group in an Active Directory environment. While Azure AD roles do not grant access to Azure services and resources, it is possible for a Global Administrator account to gain control of Azure resources. Adversaries and red teams alike may assign this role to a compromised account to establish Persistence or escalate their privileges in an Azure AD environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the assignment of the Azure AD Global Administrator role to an Azure AD user. The Global Administrator role is the most powerful administrator role in Azure AD and provides almost unlimited access to data, resources and settings. It is equivalent to the Domain Administrator group in an Active Directory environment. While Azure AD roles do not grant access to Azure services and resources, it is possible for a Global Administrator account to gain control of Azure resources. Adversaries and red teams alike may assign this role to a compromised account to establish Persistence or escalate their privileges in an Azure AD environment.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrators may legitimately assign the Global Administrator role to a user. Filter as needed.
action.escu.creation_date = 2022-08-17
action.escu.modification_date = 2022-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Global Administrator Role Assigned - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence", "Azure Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = Global Administrator Role assigned for User $userPrincipalName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Global Administrator Role Assigned - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence", "Azure Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1098.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the assignment of the Azure AD Global Administrator role to an Azure AD user. The Global Administrator role is the most powerful administrator role in Azure AD and provides almost unlimited access to data, resources and settings. It is equivalent to the Domain Administrator group in an Active Directory environment. While Azure AD roles do not grant access to Azure services and resources, it is possible for a Global Administrator account to gain control of Azure resources. Adversaries and red teams alike may assign this role to a compromised account to establish Persistence or escalate their privileges in an Azure AD environment.
action.notable.param.rule_title = Azure AD Global Administrator Role Assigned
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `azuread` operationName="Add member to role"  properties.targetResources{}.modifiedProperties{}.newValue="\"Global Administrator\"" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | stats values(userPrincipalName) as userPrincipalName by _time, initiatedBy, result, operationName | `azure_ad_global_administrator_role_assigned_filter`

[ESCU - Azure AD High Number Of Failed Authentications For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an Azure AD account with more than 20 failed authentication events in the span of 5 minutes. This behavior could represent a brute force attack against the account. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110", "T1110.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an Azure AD account with more than 20 failed authentication events in the span of 5 minutes. This behavior could represent a brute force attack against the account. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = A user with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
action.escu.creation_date = 2023-01-23
action.escu.modification_date = 2023-01-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD High Number Of Failed Authentications For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Compromised User Account", "Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $userPrincipalName$ failed to authenticate more than 20 times in the span of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD High Number Of Failed Authentications For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1110", "T1110.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an Azure AD account with more than 20 failed authentication events in the span of 5 minutes. This behavior could represent a brute force attack against the account. As environments differ across organizations, security teams should customize the threshold of this detection.
action.notable.param.rule_title = Azure AD High Number Of Failed Authentications For User
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category= SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | stats  dc(_raw) AS failed_attempts  values(ipAddress) by userPrincipalName, _time | where failed_attempts > 20 | `azure_ad_high_number_of_failed_authentications_for_user_filter`

[ESCU - Azure AD High Number Of Failed Authentications From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an Ip address failing to authenticate 20 or more times to an Azure AD tenant in the span of 5 minutes. This behavior could represent a brute force attack againstan Azure AD to obtain initial access or elevate privileges. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110", "T1110.001", "T1110.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an Ip address failing to authenticate 20 or more times to an Azure AD tenant in the span of 5 minutes. This behavior could represent a brute force attack againstan Azure AD to obtain initial access or elevate privileges. As environments differ across organizations, security teams should customize the threshold of this detection.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = An Ip address with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
action.escu.creation_date = 2023-01-23
action.escu.modification_date = 2023-01-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD High Number Of Failed Authentications From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Compromised User Account", "Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = $ipAddress$ failed to authenticate more than 20 times in the span of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD High Number Of Failed Authentications From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1110", "T1110.001", "T1110.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an Ip address failing to authenticate 20 or more times to an Azure AD tenant in the span of 5 minutes. This behavior could represent a brute force attack againstan Azure AD to obtain initial access or elevate privileges. As environments differ across organizations, security teams should customize the threshold of this detection.
action.notable.param.rule_title = Azure AD High Number Of Failed Authentications From Ip
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category= SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | stats  dc(_raw) AS failed_attempts values(userPrincipalName) as userPrincipalName by ipAddress, _time | where failed_attempts > 20 | `azure_ad_high_number_of_failed_authentications_from_ip_filter`

[ESCU - Azure AD Multi-Factor Authentication Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an attempt to disable multi-factor authentication for an Azure AD user. An adversary who has obtained access to an Azure AD tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = The following analytic identifies an attempt to disable multi-factor authentication for an Azure AD user. An adversary who has obtained access to an Azure AD tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Legitimate use case may require for users to disable MFA. Filter as needed.
action.escu.creation_date = 2022-08-10
action.escu.modification_date = 2022-08-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Multi-Factor Authentication Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = MFA disabled for User $userPrincipalName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Multi-Factor Authentication Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1586", "T1586.003", "T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an attempt to disable multi-factor authentication for an Azure AD user. An adversary who has obtained access to an Azure AD tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.notable.param.rule_title = Azure AD Multi-Factor Authentication Disabled
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `azuread` category=AuditLogs operationName="Disable Strong Authentication" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename targetResources{}.type as type | rename initiatedBy.user.userPrincipalName as initiatedBy | stats values(userPrincipalName) as userPrincipalName by _time, type, operationName, initiatedBy, result | `azure_ad_multi_factor_authentication_disabled_filter`

[ESCU - Azure AD Multiple Failed MFA Requests For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies multiple failed multi-factor authentication requests for a single user within an Azure AD tenant. Error Code 500121 represents a failed attempt to authenticate using a second factor. Specifically, the analytic triggers when more than 10 MFA user prompts fail within 10 minutes. Azure AD tenants can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1621", "T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies multiple failed multi-factor authentication requests for a single user within an Azure AD tenant. Error Code 500121 represents a failed attempt to authenticate using a second factor. Specifically, the analytic triggers when more than 10 MFA user prompts fail within 10 minutes. Azure AD tenants can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Multiple Failed MFA Requests For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Multiple Failed MFA requests for user $userPrincipalName$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Multiple Failed MFA Requests For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1621", "T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies multiple failed multi-factor authentication requests for a single user within an Azure AD tenant. Error Code 500121 represents a failed attempt to authenticate using a second factor. Specifically, the analytic triggers when more than 10 MFA user prompts fail within 10 minutes. Azure AD tenants can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.notable.param.rule_title = Azure AD Multiple Failed MFA Requests For User
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=SignInLogs properties.status.errorCode=500121 | rename properties.* as * | bucket span=10m _time | stats dc(_raw) AS mfa_prompts values(ipAddress) as ipAddress by userPrincipalName, status.additionalDetails, appDisplayName, userAgent, _time | where mfa_prompts > 10 | `azure_ad_multiple_failed_mfa_requests_for_user_filter`

[ESCU - Azure AD Multiple Users Failing To Authenticate From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source Ip failing to authenticate with 30 unique valid users within 5 minutes. This behavior could represent an adversary performing a Password Spraying attack against an Azure Active Directory tenant to obtain initial access or elevate privileges. Error Code 50126 represents an invalid password. This logic can be used for real time security monitoring as well as threat hunting exercises.\
Azure AD tenants can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold if needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source Ip failing to authenticate with 30 unique valid users within 5 minutes. This behavior could represent an adversary performing a Password Spraying attack against an Azure Active Directory tenant to obtain initial access or elevate privileges. Error Code 50126 represents an invalid password. This logic can be used for real time security monitoring as well as threat hunting exercises.\
Azure AD tenants can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold if needed.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = A source Ip failing to authenticate with multiple users is not a common for legitimate behavior.
action.escu.creation_date = 2022-07-12
action.escu.modification_date = 2022-07-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Multiple Users Failing To Authenticate From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Source Ip $ipAddress$ failed to authenticate with 30 users within 5 minutes.
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 63}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Multiple Users Failing To Authenticate From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | stats  dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName by _time, ipAddress | where unique_accounts > 30 | `azure_ad_multiple_users_failing_to_authenticate_from_ip_filter`

[ESCU - Azure AD New Custom Domain Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the addition of a new custom domain within an Azure Active Directory tenant. Adding a custom domain is a step required to set up the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the addition of a new custom domain within an Azure Active Directory tenant. Adding a custom domain is a step required to set up the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = In most organizations, new customm domains will be updated infrequently. Filter as needed.
action.escu.creation_date = 2022-09-02
action.escu.modification_date = 2022-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD New Custom Domain Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A new custom domain, $domain$ , was added by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD New Custom Domain Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1484", "T1484.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the addition of a new custom domain within an Azure Active Directory tenant. Adding a custom domain is a step required to set up the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.notable.param.rule_title = Azure AD New Custom Domain Added
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Add unverified domain" "properties.result"=success | rename properties.* as * | rename callerIpAddress as callerIpAddress | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.displayName as domain | stats values(domain) by _time, initiatedBy, result, operationName, callerIpAddress | `azure_ad_new_custom_domain_added_filter`

[ESCU - Azure AD New Federated Domain Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the addition of a new federated domain within an Azure Active Directory tenant. This event could represent the execution of the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the addition of a new federated domain within an Azure Active Directory tenant. This event could represent the execution of the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = In most organizations, domain federation settings will be updated infrequently. Filter as needed.
action.escu.creation_date = 2022-09-02
action.escu.modification_date = 2022-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD New Federated Domain Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A new federated domain, $domain$ , was added by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD New Federated Domain Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1484", "T1484.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the addition of a new federated domain within an Azure Active Directory tenant. This event could represent the execution of the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.notable.param.rule_title = Azure AD New Federated Domain Added
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Set domain authentication" "properties.result"=success | rename properties.* as * | rename callerIpAddress as callerIpAddress | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.displayName as domain | stats values(domain) by _time, initiatedBy, result, operationName, callerIpAddress | `azure_ad_new_federated_domain_added_filter`

[ESCU - Azure AD New MFA Method Registered For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the registration of a new Multi Factor authentication method for an Azure AD account. Adversaries who have obtained unauthorized access to an Azure AD account may register a new MFA method to maintain persistence.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the registration of a new Multi Factor authentication method for an Azure AD account. Adversaries who have obtained unauthorized access to an Azure AD account may register a new MFA method to maintain persistence.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
action.escu.creation_date = 2023-01-30
action.escu.modification_date = 2023-01-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD New MFA Method Registered For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Compromised User Account", "Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = UPDATE message
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "callerIpAddress", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD New MFA Method Registered For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the registration of a new Multi Factor authentication method for an Azure AD account. Adversaries who have obtained unauthorized access to an Azure AD account may register a new MFA method to maintain persistence.
action.notable.param.rule_title = Azure AD New MFA Method Registered For User
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=AuditLogs operationName="User registered security info" properties.operationType=Add | rename properties.* as * | rename targetResources{}.* as * | stats  values(userPrincipalName) as userPrincipalName by _time, resultDescription, result, callerIpAddress | `azure_ad_new_mfa_method_registered_for_user_filter`

[ESCU - Azure AD PIM Role Assigned - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the assignment of the Azure AD PIM role. Privileged Identity Management (PIM) is a service within Azure Azure AD that enables administrators to manage, control, and monitor access to sensitive resources. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions on resources. Once a user has been made eligible for an administrative role, she must activate this role assignment to perform the privileged actions. When a role is activated, Azure AD PIM temporarily adds active assignment for the role. While PIM can be leveraged as a powerful security control, it may also abused by adversaries to obtain privileged access. Security teams should monitor for the assignment and activation of PIM roles and validate their legitimacy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the assignment of the Azure AD PIM role. Privileged Identity Management (PIM) is a service within Azure Azure AD that enables administrators to manage, control, and monitor access to sensitive resources. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions on resources. Once a user has been made eligible for an administrative role, she must activate this role assignment to perform the privileged actions. When a role is activated, Azure AD PIM temporarily adds active assignment for the role. While PIM can be leveraged as a powerful security control, it may also abused by adversaries to obtain privileged access. Security teams should monitor for the assignment and activation of PIM roles and validate their legitimacy.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = As part of legitimate administrative behavior, users may be assigned PIM roles. Filter as needed
action.escu.creation_date = 2023-04-26
action.escu.modification_date = 2023-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD PIM Role Assigned - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Privilege Escalation", "Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = An Azure AD PIM role assignment was assiged to $userPrincipalName$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD PIM Role Assigned - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Privilege Escalation", "Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the assignment of the Azure AD PIM role. Privileged Identity Management (PIM) is a service within Azure Azure AD that enables administrators to manage, control, and monitor access to sensitive resources. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions on resources. Once a user has been made eligible for an administrative role, she must activate this role assignment to perform the privileged actions. When a role is activated, Azure AD PIM temporarily adds active assignment for the role. While PIM can be leveraged as a powerful security control, it may also abused by adversaries to obtain privileged access. Security teams should monitor for the assignment and activation of PIM roles and validate their legitimacy.
action.notable.param.rule_title = Azure AD PIM Role Assigned
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Add eligible member to role in PIM completed*" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | stats values(userPrincipalName) values(targetResources{}.displayName) by _time, result, operationName, initiatedBy.user.displayName | `azure_ad_pim_role_assigned_filter`

[ESCU - Azure AD PIM Role Assignment Activated - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the assignment of the Azure AD PIM role. Privileged Identity Management (PIM) is a service within Azure Azure AD that enables administrators to manage, control, and monitor access to sensitive resources. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions on resources. Once a user has been made eligible for an administrative role, she must activate this role assignment to perform the privileged actions. When a role is activated, Azure AD PIM temporarily adds active assignment for the role. While PIM can be leveraged as a powerful security control, it may also abused by adversaries to obtain privileged access. Security teams should monitor for the assignment and activation of PIM roles and validate their legitimacy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the assignment of the Azure AD PIM role. Privileged Identity Management (PIM) is a service within Azure Azure AD that enables administrators to manage, control, and monitor access to sensitive resources. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions on resources. Once a user has been made eligible for an administrative role, she must activate this role assignment to perform the privileged actions. When a role is activated, Azure AD PIM temporarily adds active assignment for the role. While PIM can be leveraged as a powerful security control, it may also abused by adversaries to obtain privileged access. Security teams should monitor for the assignment and activation of PIM roles and validate their legitimacy.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = As part of legitimate administrative behavior, users may activate PIM roles. Filter as needed
action.escu.creation_date = 2023-04-26
action.escu.modification_date = 2023-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD PIM Role Assignment Activated - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Privilege Escalation", "Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = An Azure AD PIM role assignment was activated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD PIM Role Assignment Activated - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Privilege Escalation", "Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the assignment of the Azure AD PIM role. Privileged Identity Management (PIM) is a service within Azure Azure AD that enables administrators to manage, control, and monitor access to sensitive resources. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions on resources. Once a user has been made eligible for an administrative role, she must activate this role assignment to perform the privileged actions. When a role is activated, Azure AD PIM temporarily adds active assignment for the role. While PIM can be leveraged as a powerful security control, it may also abused by adversaries to obtain privileged access. Security teams should monitor for the assignment and activation of PIM roles and validate their legitimacy.
action.notable.param.rule_title = Azure AD PIM Role Assignment Activated
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Add member to role completed (PIM activation)" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | stats values(userPrincipalName) values(targetResources{}.displayName) by _time, initiatedBy, result, operationName, | `azure_ad_pim_role_assignment_activated_filter`

[ESCU - Azure AD Privileged Authentication Administrator Role Assigned - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the assignment of the Privileged Authentication Administrato role to an Azure AD user. Users in this role can set or reset authentication methods for any user in Azure Active Directory, including privileged roles like Global Administrators. Users with this role can change credentials for people who may have access to sensitive or private information or critical configuration inside and outside of Azure Active Directory. Changing the credentials of a user may mean the ability to assume that users identity and permissions. Red teams and adversaries alike may abuse this role to escalate their privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = The following analytic identifies the assignment of the Privileged Authentication Administrato role to an Azure AD user. Users in this role can set or reset authentication methods for any user in Azure Active Directory, including privileged roles like Global Administrators. Users with this role can change credentials for people who may have access to sensitive or private information or critical configuration inside and outside of Azure Active Directory. Changing the credentials of a user may mean the ability to assume that users identity and permissions. Red teams and adversaries alike may abuse this role to escalate their privileges.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrators may legitimately assign the Privileged Authentication Administrator role as part of administrative tasks. Filter as needed.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Privileged Authentication Administrator Role Assigned - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = The privileged Azure AD role Privileged Authentication Administrator was assigned for User $userPrincipalName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 50}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Privileged Authentication Administrator Role Assigned - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 100, "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the assignment of the Privileged Authentication Administrato role to an Azure AD user. Users in this role can set or reset authentication methods for any user in Azure Active Directory, including privileged roles like Global Administrators. Users with this role can change credentials for people who may have access to sensitive or private information or critical configuration inside and outside of Azure Active Directory. Changing the credentials of a user may mean the ability to assume that users identity and permissions. Red teams and adversaries alike may abuse this role to escalate their privileges.
action.notable.param.rule_title = Azure AD Privileged Authentication Administrator Role Assigned
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` "operationName"="Add member to role"  "properties.targetResources{}.modifiedProperties{}.newValue"="\"Privileged Authentication Administrator\"" | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | stats values(userPrincipalName) as userPrincipalName by _time, initiatedBy, result, operationName | `azure_ad_privileged_authentication_administrator_role_assigned_filter`

[ESCU - Azure AD Privileged Role Assigned - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the assignment of sensitive and privileged Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike may assign these roles to a compromised account to establish Persistence in an Azure AD environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the assignment of sensitive and privileged Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike may assign these roles to a compromised account to establish Persistence in an Azure AD environment.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrators will legitimately assign the privileged roles users as part of administrative tasks. Filter as needed.
action.escu.creation_date = 2022-08-29
action.escu.modification_date = 2022-08-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Privileged Role Assigned - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A privileged Azure AD role was assigned for User $userPrincipalName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 63}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Privileged Role Assigned - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the assignment of sensitive and privileged Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike may assign these roles to a compromised account to establish Persistence in an Azure AD environment.
action.notable.param.rule_title = Azure AD Privileged Role Assigned
action.notable.param.security_domain = audit
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` "operationName"="Add member to role" | rename properties.*  as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue  as roles | eval role=mvindex(roles,1) | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description | search isprvilegedadrole = True | stats values(userPrincipalName) as userPrincipalName by _time, initiatedBy, result, operationName, role, description | `azure_ad_privileged_role_assigned_filter`

[ESCU - Azure AD Privileged Role Assigned to Service Principal - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential privilege escalation threats in Azure Active Directory (AD). The detection is made by running a specific search within the ingested Azure Active Directory events to leverage the AuditLogs log category. This detection is important because it identifies instances where privileged roles that hold elevated permissions are assigned to service principals. This prevents unauthorized access or malicious activities, which occur when these non-human entities access Azure resources to exploit them. False positives might occur since administrators can legitimately assign privileged roles to service principals.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects potential privilege escalation threats in Azure Active Directory (AD). The detection is made by running a specific search within the ingested Azure Active Directory events to leverage the AuditLogs log category. This detection is important because it identifies instances where privileged roles that hold elevated permissions are assigned to service principals. This prevents unauthorized access or malicious activities, which occur when these non-human entities access Azure resources to exploit them. False positives might occur since administrators can legitimately assign privileged roles to service principals.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrators may legitimately assign the privileged roles to Service Principals as part of administrative tasks. Filter as needed.
action.escu.creation_date = 2023-04-28
action.escu.modification_date = 2023-04-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Privileged Role Assigned to Service Principal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = A privileged Azure AD role was assigned to the Service Principal $displayName$ initiated by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Privileged Role Assigned to Service Principal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1098", "T1098.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects potential privilege escalation threats in Azure Active Directory (AD). The detection is made by running a specific search within the ingested Azure Active Directory events to leverage the AuditLogs log category. This detection is important because it identifies instances where privileged roles that hold elevated permissions are assigned to service principals. This prevents unauthorized access or malicious activities, which occur when these non-human entities access Azure resources to exploit them. False positives might occur since administrators can legitimately assign privileged roles to service principals.
action.notable.param.rule_title = Azure AD Privileged Role Assigned to Service Principal
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Add member to role" | rename properties.* as * | search "targetResources{}.type"=ServicePrincipal | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue  as roles | eval role=mvindex(roles,1) | rename targetResources{}.displayName as apps | eval displayName=mvindex(apps,0) | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description | search isprvilegedadrole = True | stats values(displayName) by _time, initiatedBy, result, operationName, role | `azure_ad_privileged_role_assigned_to_service_principal_filter`

[ESCU - Azure AD Service Principal Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the creation of a Service Principal in an Azure AD environment. An Azure Service Principal is an identity designed to be used with applications, services, and automated tools to access resources. It is similar to a service account within an Active Directory environment. Service Principal authentication does not support multi-factor authentication nor conditional access policies. Adversaries and red teams alike who have obtained administrative access may create a Service Principal to establish Persistence and obtain single-factor access to an Azure AD environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the creation of a Service Principal in an Azure AD environment. An Azure Service Principal is an identity designed to be used with applications, services, and automated tools to access resources. It is similar to a service account within an Active Directory environment. Service Principal authentication does not support multi-factor authentication nor conditional access policies. Adversaries and red teams alike who have obtained administrative access may create a Service Principal to establish Persistence and obtain single-factor access to an Azure AD environment.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment thorough an EventHub. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrator may legitimately create Service Principal. Filter as needed.
action.escu.creation_date = 2022-08-17
action.escu.modification_date = 2022-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Service Principal Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = Service Principal named $displayName$ created by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "displayName", "risk_object_type": "other", "risk_score": 45}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Service Principal Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1136.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the creation of a Service Principal in an Azure AD environment. An Azure Service Principal is an identity designed to be used with applications, services, and automated tools to access resources. It is similar to a service account within an Active Directory environment. Service Principal authentication does not support multi-factor authentication nor conditional access policies. Adversaries and red teams alike who have obtained administrative access may create a Service Principal to establish Persistence and obtain single-factor access to an Azure AD environment.
action.notable.param.rule_title = Azure AD Service Principal Created
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `azuread` operationName="Add service principal" properties.initiatedBy.user.id=* | rename properties.* as * | rename targetResources{}.displayName as displayName | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.type as type | stats values(displayName) as displayName by _time, type, initiatedBy, result, operationName | `azure_ad_service_principal_created_filter`

[ESCU - Azure AD Service Principal New Client Credentials - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the addition of new credentials for Service Principals and Applications in addition to existing legitimate credentials in Azure AD. These credentials include both x509 certificates and passwords. With sufficient permissions, there are a variety of ways to add credentials including the Azure Portal, Azure command line interface, and Azure or Az PowerShell modules. Adversaries and red teams alike who have obtained privileged access to Azure AD may add credentials to Service Principals to maintain persistent access to victim accounts and other instances within the Azure environment. By compromising an account who is an Owner of an application with privileged access, attackers may also escalate their privileges in an Azure AD environment by adding new credentials and logging in as the service principal.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098", "T1098.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the addition of new credentials for Service Principals and Applications in addition to existing legitimate credentials in Azure AD. These credentials include both x509 certificates and passwords. With sufficient permissions, there are a variety of ways to add credentials including the Azure Portal, Azure command line interface, and Azure or Az PowerShell modules. Adversaries and red teams alike who have obtained privileged access to Azure AD may add credentials to Service Principals to maintain persistent access to victim accounts and other instances within the Azure environment. By compromising an account who is an Owner of an application with privileged access, attackers may also escalate their privileges in an Azure AD environment by adding new credentials and logging in as the service principal.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = Service Principal client credential modifications may be part of legitimate administrative operations. Filter as needed.
action.escu.creation_date = 2022-08-17
action.escu.modification_date = 2022-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Service Principal New Client Credentials - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence", "Azure Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = New credentials added for Service Principal $properties.targetResources{}.displayName$
action.risk.param._risk = [{"risk_object_field": "displayName", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "initiatedBy.user.userPrincipalName", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Service Principal New Client Credentials - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence", "Azure Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1098", "T1098.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the addition of new credentials for Service Principals and Applications in addition to existing legitimate credentials in Azure AD. These credentials include both x509 certificates and passwords. With sufficient permissions, there are a variety of ways to add credentials including the Azure Portal, Azure command line interface, and Azure or Az PowerShell modules. Adversaries and red teams alike who have obtained privileged access to Azure AD may add credentials to Service Principals to maintain persistent access to victim accounts and other instances within the Azure environment. By compromising an account who is an Owner of an application with privileged access, attackers may also escalate their privileges in an Azure AD environment by adding new credentials and logging in as the service principal.
action.notable.param.rule_title = Azure AD Service Principal New Client Credentials
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=AuditLogs operationName="Update application*Certificates and secrets management " | rename * as * | rename properties.* as * | rename targetResources{}.* as * | stats values(displayName) as displayName by _time, initiatedBy.user.userPrincipalName, modifiedProperties{}.newValue | `azure_ad_service_principal_new_client_credentials_filter`

[ESCU - Azure AD Service Principal Owner Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the addition of a new owner for a Service Principal within an Azure AD tenant. An Azure Service Principal is an identity designed to be used with applications, services, and automated tools to access resources. It is similar to a service account within an Active Directory environment. Service Principal authentication does not support multi-factor authentication nor conditional access policies. Adversaries and red teams alike who have obtained administrative access may add a new owner for an existing Service Principal to establish Persistence and obtain single-factor access to an Azure AD environment. Attackers who are looking to escalate their privileges by leveraging a Service Principals permissions may also add a new owner.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the addition of a new owner for a Service Principal within an Azure AD tenant. An Azure Service Principal is an identity designed to be used with applications, services, and automated tools to access resources. It is similar to a service account within an Active Directory environment. Service Principal authentication does not support multi-factor authentication nor conditional access policies. Adversaries and red teams alike who have obtained administrative access may add a new owner for an existing Service Principal to establish Persistence and obtain single-factor access to an Azure AD environment. Attackers who are looking to escalate their privileges by leveraging a Service Principals permissions may also add a new owner.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = Administrator may legitimately add new owners for Service Principals. Filter as needed.
action.escu.creation_date = 2022-08-30
action.escu.modification_date = 2022-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Service Principal Owner Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence", "Azure Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = A new owner was added for service principal $displayName$ by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "displayName", "risk_object_type": "other", "risk_score": 54}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Service Principal Owner Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence", "Azure Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the addition of a new owner for a Service Principal within an Azure AD tenant. An Azure Service Principal is an identity designed to be used with applications, services, and automated tools to access resources. It is similar to a service account within an Active Directory environment. Service Principal authentication does not support multi-factor authentication nor conditional access policies. Adversaries and red teams alike who have obtained administrative access may add a new owner for an existing Service Principal to establish Persistence and obtain single-factor access to an Azure AD environment. Attackers who are looking to escalate their privileges by leveraging a Service Principals permissions may also add a new owner.
action.notable.param.rule_title = Azure AD Service Principal Owner Added
action.notable.param.security_domain = audit
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Add owner to application" | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.userPrincipalName as newOwner | rename targetResources{}.modifiedProperties{}.newValue as displayName | eval displayName = mvindex(displayName,1) | where initiatedBy!=newOwner | stats values(displayName) as displayName by _time, initiatedBy, result, operationName, newOwner | `azure_ad_service_principal_owner_added_filter`

[ESCU - Azure AD Successful Authentication From Different Ips - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an Azure AD account successfully authenticating from more than one unique Ip address in the span of 5 minutes. This behavior could represent an adversary who has stolen credentials via a phishing attack or some other method and using them to access corporate online resources around the same time as a legitimate user. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110", "T1110.001", "T1110.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an Azure AD account successfully authenticating from more than one unique Ip address in the span of 5 minutes. This behavior could represent an adversary who has stolen credentials via a phishing attack or some other method and using them to access corporate online resources around the same time as a legitimate user. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = A user with successful authentication events from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
action.escu.creation_date = 2023-01-24
action.escu.modification_date = 2023-01-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Successful Authentication From Different Ips - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Compromised User Account", "Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $userPrincipalName$ has had successful authentication events from more than one unique IP address in the span of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Successful Authentication From Different Ips - Rule
action.correlationsearch.annotations = {"analytic_story": ["Compromised User Account", "Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1110", "T1110.001", "T1110.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an Azure AD account successfully authenticating from more than one unique Ip address in the span of 5 minutes. This behavior could represent an adversary who has stolen credentials via a phishing attack or some other method and using them to access corporate online resources around the same time as a legitimate user. As users may behave differently across organizations, security teams should test and customize this detection to fit their environments.
action.notable.param.rule_title = Azure AD Successful Authentication From Different Ips
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` properties.authenticationDetails{}.succeeded=true category=SignInLogs | rename properties.* as * | bucket span=5m _time | stats  dc(ipAddress) AS unique_ips values(ipAddress) as ipAddress values(appDisplayName) by _time, userPrincipalName | where unique_ips > 1 | `azure_ad_successful_authentication_from_different_ips_filter`

[ESCU - Azure AD Successful PowerShell Authentication - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a successful authentication event against an Azure AD tenant using PowerShell commandlets. This behavior is not common for regular, non administrative users. After compromising an account in Azure AD, attackers and red teams  alike will perform enumeration and discovery techniques. One method of executing these techniques is leveraging the native PowerShell modules.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a successful authentication event against an Azure AD tenant using PowerShell commandlets. This behavior is not common for regular, non administrative users. After compromising an account in Azure AD, attackers and red teams  alike will perform enumeration and discovery techniques. One method of executing these techniques is leveraging the native PowerShell modules.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = Administrative users will likely use PowerShell commandlets to troubleshoot and maintain the environment. Filter as needed.
action.escu.creation_date = 2022-07-13
action.escu.modification_date = 2022-07-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Successful PowerShell Authentication - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Successful authentication for user $userPrincipalName$ using PowerShell.
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 54}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Successful PowerShell Authentication - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a successful authentication event against an Azure AD tenant using PowerShell commandlets. This behavior is not common for regular, non administrative users. After compromising an account in Azure AD, attackers and red teams  alike will perform enumeration and discovery techniques. One method of executing these techniques is leveraging the native PowerShell modules.
action.notable.param.rule_title = Azure AD Successful PowerShell Authentication
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=SignInLogs properties.authenticationDetails{}.succeeded=true properties.appDisplayName="Microsoft Azure PowerShell" | rename properties.* as * |  stats values(userPrincipalName) as userPrincipalName by _time, ipAddress, appDisplayName, userAgent | `azure_ad_successful_powershell_authentication_filter`

[ESCU - Azure AD Successful Single-Factor Authentication - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a successful authentication event against Azure Active Directory for an account without Multi-Factor Authentication enabled. This could be evidence of a missconfiguration, a policy violation or an account take over attempt that should be investigated
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = The following analytic identifies a successful authentication event against Azure Active Directory for an account without Multi-Factor Authentication enabled. This could be evidence of a missconfiguration, a policy violation or an account take over attempt that should be investigated
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = Although not recommended, certain users may be required without multi-factor authentication. Filter as needed
action.escu.creation_date = 2022-07-12
action.escu.modification_date = 2022-07-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Successful Single-Factor Authentication - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Successful authentication for user $userPrincipalName$ without MFA
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Successful Single-Factor Authentication - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a successful authentication event against Azure Active Directory for an account without Multi-Factor Authentication enabled. This could be evidence of a missconfiguration, a policy violation or an account take over attempt that should be investigated
action.notable.param.rule_title = Azure AD Successful Single-Factor Authentication
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread`  category=SignInLogs properties.authenticationRequirement=singleFactorAuthentication properties.authenticationDetails{}.succeeded=true | rename properties.* as * |  stats values(userPrincipalName) as userPrincipalName by _time, ipAddress, appDisplayName, authenticationRequirement | `azure_ad_successful_single_factor_authentication_filter`

[ESCU - Azure AD Unusual Number of Failed Authentications From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source Ip failing to authenticate with multiple valid users. This behavior could represent an adversary performing a Password Spraying attack against an Azure Active Directory tenant to obtain initial access or elevate privileges. Error Code 50126 represents an invalid password.\
The detection calculates the standard deviation for source Ip and leverages the 3-sigma statistical rule to identify an unusual number of failed authentication attempts. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
While looking for anomalies using statistical methods like the standard deviation can have benefits, we also recommend using threshold-based detections to complement coverage. A similar analytic following the threshold model is `Azure AD Multiple Users Failing To Authenticate From Ip`.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source Ip failing to authenticate with multiple valid users. This behavior could represent an adversary performing a Password Spraying attack against an Azure Active Directory tenant to obtain initial access or elevate privileges. Error Code 50126 represents an invalid password.\
The detection calculates the standard deviation for source Ip and leverages the 3-sigma statistical rule to identify an unusual number of failed authentication attempts. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
While looking for anomalies using statistical methods like the standard deviation can have benefits, we also recommend using threshold-based detections to complement coverage. A similar analytic following the threshold model is `Azure AD Multiple Users Failing To Authenticate From Ip`.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the SignInLogs log category.
action.escu.known_false_positives = A source Ip failing to authenticate with multiple users is not a common for legitimate behavior.
action.escu.creation_date = 2022-07-11
action.escu.modification_date = 2022-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD Unusual Number of Failed Authentications From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Possible Password Spraying attack against Azure AD from source ip $ipAddress$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 54}, {"risk_object_field": "ipAddress", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD Unusual Number of Failed Authentications From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | stats  dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName by _time, ipAddress | eventstats  avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std by ipAddress | eval  upperBound=(ip_avg+ip_std*3) | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0) | where isOutlier = 1 | `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`

[ESCU - Azure AD User Enabled And Password Reset - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an Azure AD user enabling a previously disabled account and resetting its password within 2 minutes. This behavior could represent an adversary who has obtained administrative access and is trying to establish a backdoor identity within an Azure AD tenant.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an Azure AD user enabling a previously disabled account and resetting its password within 2 minutes. This behavior could represent an adversary who has obtained administrative access and is trying to establish a backdoor identity within an Azure AD tenant.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = While not common, Administrators may enable accounts and reset their passwords for legitimate reasons. Filter as needed.
action.escu.creation_date = 2022-08-29
action.escu.modification_date = 2022-08-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD User Enabled And Password Reset - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A user account, $userPrincipalName$, was enabled and its password reset within 2 minutes by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD User Enabled And Password Reset - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an Azure AD user enabling a previously disabled account and resetting its password within 2 minutes. This behavior could represent an adversary who has obtained administrative access and is trying to establish a backdoor identity within an Azure AD tenant.
action.notable.param.rule_title = Azure AD User Enabled And Password Reset
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` (operationName="Enable account" OR operationName="Reset password (by admin)" OR operationName="Update user") | transaction properties.targetResources{}.userPrincipalName startsWith=(operationName="Enable account") endsWith=(operationName="Reset password (by admin)") maxspan=2m | rename properties.* as * | rename targetResources{}.userPrincipalName as user | rename initiatedBy.user.userPrincipalName as initiatedBy | stats values(operationName) values(initiatedBy) as initiatedBy by _time, user, result | `azure_ad_user_enabled_and_password_reset_filter`

[ESCU - Azure AD User ImmutableId Attribute Updated - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the modification of the SourceAnchor (also called ImmutableId) attribute for an Azure Active Directory user. Updating this attribute is a step required to set up the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the modification of the SourceAnchor (also called ImmutableId) attribute for an Azure Active Directory user. Updating this attribute is a step required to set up the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. Specifically, this analytic leverages the AuditLogs log category.
action.escu.known_false_positives = The SourceAnchor (also called ImmutableId) Azure AD attribute has legitimate uses for directory synchronization. Investigate and filter as needed.
action.escu.creation_date = 2022-09-02
action.escu.modification_date = 2022-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure AD User ImmutableId Attribute Updated - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = The SourceAnchor or ImmutableID attribute has been modified for user $userPrincipalName$ by $initiatedBy$
action.risk.param._risk = [{"risk_object_field": "userPrincipalName", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "initiatedBy", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure AD User ImmutableId Attribute Updated - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the modification of the SourceAnchor (also called ImmutableId) attribute for an Azure Active Directory user. Updating this attribute is a step required to set up the Azure Active Directory identity federation backdoor technique discovered by security researcher Nestori Syynimaa. Similar to Active Directory, Azure AD uses the concept of domains to manage directories of identities. A new Azure AD tenant will initially contain a single domain that is commonly called the `cloud-only` onmicrosoft.com domain. Organizations can also add their registered custom domains to Azure AD for email addresses to match the organizations domain name. If the organization intends to use a third-party identity provider such as ADFS for authentication, the added custom domains can be configured as federated. An adversary who has obtained privileged access to an Azure AD tenant may leverage this technique to establish persistence and be able to authenticate to Azure AD impersonating any user and bypassing the requirement to have a valid password and/or perform MFA.
action.notable.param.rule_title = Azure AD User ImmutableId Attribute Updated
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azuread` operationName="Update user" properties.targetResources{}.modifiedProperties{}.displayName=SourceAnchor | rename properties.* as * | rename targetResources{}.userPrincipalName as userPrincipalName | rename initiatedBy.user.userPrincipalName as initiatedBy | rename callerIpAddress as callerIpAddress | rename targetResources{}.modifiedProperties{}.newValue as modifiedProperties | stats values(userPrincipalName) as userPrincipalName values(modifiedProperties) by _time, initiatedBy, callerIpAddress, result, operationName | `azure_ad_user_immutableid_attribute_updated_filter`

[ESCU - Azure Automation Account Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the creation of a new Azure Automation account within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure using PowerShell and Python. Azure Automation can also be configured to automate tasks on on premise infrastructure using a component called a Hybrid Runbook Worker. Automation accounts serve as a container to isolate Automation resources, runbooks, assets, and configurations from the resources of other accounts. They allow administrators to separate resources into logical environments or delegated responsibilities. Adversaries or red teams who have obtained privileged access to an Azure tenant may create an Azure Automation account with elevated privileges to maintain persistence in the Azure tenant. A malicious Automation Runbook can be created to create Global Administrators in Azure AD, execute code on VMs, etc.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136", "T1136.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the creation of a new Azure Automation account within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure using PowerShell and Python. Azure Automation can also be configured to automate tasks on on premise infrastructure using a component called a Hybrid Runbook Worker. Automation accounts serve as a container to isolate Automation resources, runbooks, assets, and configurations from the resources of other accounts. They allow administrators to separate resources into logical environments or delegated responsibilities. Adversaries or red teams who have obtained privileged access to an Azure tenant may create an Azure Automation account with elevated privileges to maintain persistence in the Azure tenant. A malicious Automation Runbook can be created to create Global Administrators in Azure AD, execute code on VMs, etc.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
action.escu.known_false_positives = Administrators may legitimately create Azure Automation accounts. Filter as needed.
action.escu.creation_date = 2022-08-18
action.escu.modification_date = 2022-08-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure Automation Account Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A new Azure Automation account $object$ was created by $caller$
action.risk.param._risk = [{"risk_object_field": "object", "risk_object_type": "other", "risk_score": 63}, {"risk_object_field": "caller", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure Automation Account Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1136", "T1136.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the creation of a new Azure Automation account within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure using PowerShell and Python. Azure Automation can also be configured to automate tasks on on premise infrastructure using a component called a Hybrid Runbook Worker. Automation accounts serve as a container to isolate Automation resources, runbooks, assets, and configurations from the resources of other accounts. They allow administrators to separate resources into logical environments or delegated responsibilities. Adversaries or red teams who have obtained privileged access to an Azure tenant may create an Azure Automation account with elevated privileges to maintain persistence in the Azure tenant. A malicious Automation Runbook can be created to create Global Administrators in Azure AD, execute code on VMs, etc.
action.notable.param.rule_title = Azure Automation Account Created
action.notable.param.security_domain = audit
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azure_audit` operationName.localizedValue="Create or Update an Azure Automation account" status.value=Succeeded | dedup object | stats values(object) by _time, caller, claims.ipaddr, resourceGroupName, object_path | `azure_automation_account_created_filter`

[ESCU - Azure Automation Runbook Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the creation of a new Azure Automation Runbook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. Adversaries or red teams who have obtained privileged access to an Azure tenant may create an Azure Automation Runbook that runs with elevated privileges to maintain persistence in the Azure tenant. A malicious Automation Runbook can be created to create Global Administrators in Azure AD, execute code on VMs, etc.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136", "T1136.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the creation of a new Azure Automation Runbook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. Adversaries or red teams who have obtained privileged access to an Azure tenant may create an Azure Automation Runbook that runs with elevated privileges to maintain persistence in the Azure tenant. A malicious Automation Runbook can be created to create Global Administrators in Azure AD, execute code on VMs, etc.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
action.escu.known_false_positives = Administrators may legitimately create Azure Automation Runbooks. Filter as needed.
action.escu.creation_date = 2022-08-22
action.escu.modification_date = 2022-08-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure Automation Runbook Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A new Azure Automation Runbook $object$ was created by $caller$
action.risk.param._risk = [{"risk_object_field": "object", "risk_object_type": "other", "risk_score": 63}, {"risk_object_field": "caller", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure Automation Runbook Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1136", "T1136.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the creation of a new Azure Automation Runbook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. Adversaries or red teams who have obtained privileged access to an Azure tenant may create an Azure Automation Runbook that runs with elevated privileges to maintain persistence in the Azure tenant. A malicious Automation Runbook can be created to create Global Administrators in Azure AD, execute code on VMs, etc.
action.notable.param.rule_title = Azure Automation Runbook Created
action.notable.param.security_domain = audit
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azure_audit` operationName.localizedValue="Create or Update an Azure Automation Runbook" object!=AzureAutomationTutorial* status.value=Succeeded | dedup object | stats values(object) by _time, caller, claims.ipaddr, resourceGroupName, object_path | `azure_automation_runbook_created_filter`

[ESCU - Azure Runbook Webhook Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the creation of a new Automation Runbook Webhook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. One of the ways administrators can configure a Runbook to be executed is through HTTP Webhooks. Webhooks leverage custom unauthenticated URLs that are exposed to the Internet. An adversary who has obtained privileged access to an Azure tenant may create a Webhook to trigger the execution of an Automation Runbook with malicious code that can create users or execute code on a VM. This provides a persistent foothold on the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the creation of a new Automation Runbook Webhook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. One of the ways administrators can configure a Runbook to be executed is through HTTP Webhooks. Webhooks leverage custom unauthenticated URLs that are exposed to the Internet. An adversary who has obtained privileged access to an Azure tenant may create a Webhook to trigger the execution of an Automation Runbook with malicious code that can create users or execute code on a VM. This provides a persistent foothold on the environment.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
action.escu.known_false_positives = Administrators may legitimately create Azure Runbook Webhooks. Filter as needed.
action.escu.creation_date = 2022-08-23
action.escu.modification_date = 2022-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Azure Runbook Webhook Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azure Active Directory Persistence"]
action.risk = 1
action.risk.param._risk_message = A new Azure Runbook Webhook $object$ was created by $caller$
action.risk.param._risk = [{"risk_object_field": "object", "risk_object_type": "other", "risk_score": 63}, {"risk_object_field": "caller", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Azure Runbook Webhook Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azure Active Directory Persistence"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the creation of a new Automation Runbook Webhook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. One of the ways administrators can configure a Runbook to be executed is through HTTP Webhooks. Webhooks leverage custom unauthenticated URLs that are exposed to the Internet. An adversary who has obtained privileged access to an Azure tenant may create a Webhook to trigger the execution of an Automation Runbook with malicious code that can create users or execute code on a VM. This provides a persistent foothold on the environment.
action.notable.param.rule_title = Azure Runbook Webhook Created
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `azure_audit` operationName.localizedValue="Create or Update an Azure Automation webhook" status.value=Succeeded | stats values(object) by _time, caller, claims.ipaddr, resourceGroupName, object_path | `azure_runbook_webhook_created_filter`

[ESCU - Circle CI Disable Security Job - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic searches for a specific behavior in CircleCI pipelines such as the disabling of security jobs. The detection is made by using a Splunk query that renames certain fields and retrieves values for specified job names, workflow IDs and names, user information, commit messages, URLs, and branches. Then, the query identifies mandatory jobs for each workflow and searches for instances where they were run. The search also identifies the phase of the pipeline as "build" and extracts the repository name from the URL using regular expressions. The detection is important because it detects attempts to bypass security measures in CircleCI pipelines, which can potentially lead to malicious code being introduced into the pipeline, data breaches, system downtime, and reputational damage.  False positives might occur since legitimate use cases can require the disabling of security jobs. However, you can proactively monitor and identify any suspicious activity in the pipeline using this analytic and mitigate potential threats through early detection.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1554"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic searches for a specific behavior in CircleCI pipelines such as the disabling of security jobs. The detection is made by using a Splunk query that renames certain fields and retrieves values for specified job names, workflow IDs and names, user information, commit messages, URLs, and branches. Then, the query identifies mandatory jobs for each workflow and searches for instances where they were run. The search also identifies the phase of the pipeline as "build" and extracts the repository name from the URL using regular expressions. The detection is important because it detects attempts to bypass security measures in CircleCI pipelines, which can potentially lead to malicious code being introduced into the pipeline, data breaches, system downtime, and reputational damage.  False positives might occur since legitimate use cases can require the disabling of security jobs. However, you can proactively monitor and identify any suspicious activity in the pipeline using this analytic and mitigate potential threats through early detection.
action.escu.how_to_implement = You must index CircleCI logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-02
action.escu.modification_date = 2021-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Circle CI Disable Security Job - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = disable security job $mandatory_job$ in workflow $workflow_name$ from user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Circle CI Disable Security Job - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 90, "impact": 80, "mitre_attack": ["T1554"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `circleci` | rename vcs.committer_name as user vcs.subject as commit_message vcs.url as url workflows.* as *  | stats values(job_name) as job_names by workflow_id workflow_name user commit_message url branch | lookup mandatory_job_for_workflow workflow_name OUTPUTNEW job_name AS mandatory_job | search mandatory_job=* | eval mandatory_job_executed=if(like(job_names, "%".mandatory_job."%"), 1, 0) | where mandatory_job_executed=0 | eval phase="build" | rex field=url "(?<repository>[^\/]*\/[^\/]*)$" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `circle_ci_disable_security_job_filter`

[ESCU - Circle CI Disable Security Step - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the disablement of security steps in a CircleCI pipeline. Addressing instances of security step disablement in CircleCI pipelines can mitigate the risks associated with potential security vulnerabilities and unauthorized changes. A proactive approach helps protect the organization's infrastructure, data, and overall security posture. The detection is made by a Splunk query that searches for specific criteria within CircleCI logs through a combination of field renaming, joining, and statistical analysis to identify instances where security steps are disabled. It retrieves information such as job IDs, job names, commit details, and user information from the CircleCI logs.  The detection is important because it indicates potential security vulnerabilities or unauthorized changes to the pipeline caused by someone within the organization intentionally or unintentionally disabling security steps in the CircleCI pipeline.Disabling security steps can leave the pipeline and the associated infrastructure exposed to potential attacks, data breaches, or the introduction of malicious code into the pipeline. Investigate by reviewing the job name, commit details, and user information associated with the disablement of security steps. You must also examine any relevant on-disk artifacts and identify concurrent processes that might indicate the source of the attack or unauthorized change.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1554"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the disablement of security steps in a CircleCI pipeline. Addressing instances of security step disablement in CircleCI pipelines can mitigate the risks associated with potential security vulnerabilities and unauthorized changes. A proactive approach helps protect the organization's infrastructure, data, and overall security posture. The detection is made by a Splunk query that searches for specific criteria within CircleCI logs through a combination of field renaming, joining, and statistical analysis to identify instances where security steps are disabled. It retrieves information such as job IDs, job names, commit details, and user information from the CircleCI logs.  The detection is important because it indicates potential security vulnerabilities or unauthorized changes to the pipeline caused by someone within the organization intentionally or unintentionally disabling security steps in the CircleCI pipeline.Disabling security steps can leave the pipeline and the associated infrastructure exposed to potential attacks, data breaches, or the introduction of malicious code into the pipeline. Investigate by reviewing the job name, commit details, and user information associated with the disablement of security steps. You must also examine any relevant on-disk artifacts and identify concurrent processes that might indicate the source of the attack or unauthorized change.
action.escu.how_to_implement = You must index CircleCI logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Circle CI Disable Security Step - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = disable security step $mandatory_step$ in job $job_name$ from user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Circle CI Disable Security Step - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 90, "impact": 80, "mitre_attack": ["T1554"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `circleci` | rename workflows.job_id AS job_id | join job_id [ | search `circleci` | stats values(name) as step_names count by job_id job_name ] | stats count by step_names job_id job_name vcs.committer_name vcs.subject vcs.url owners{} | rename vcs.* as * , owners{} as user | lookup mandatory_step_for_job job_name OUTPUTNEW step_name AS mandatory_step | search mandatory_step=* | eval mandatory_step_executed=if(like(step_names, "%".mandatory_step."%"), 1, 0) | where mandatory_step_executed=0 | rex field=url "(?<repository>[^\/]*\/[^\/]*)$" | eval phase="build"  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `circle_ci_disable_security_step_filter`

[ESCU - Cloud API Calls From Previously Unseen User Roles - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects when a new command is run by a user, who typically does not run those commands. The detection is made by a Splunk query to search for these commands in the Change data model. Identifies commands run by users with the user_type of AssumedRole and a status of success. The query retrieves the earliest and latest timestamps of each command run and groups the results by the user and command. Then, it drops the unnecessary data model object name and creates a lookup to verify if the command was seen before. The lookup table contains information about previously seen cloud API calls for each user role, including the first time the command was seen and whether enough data is available for analysis. If the firstTimeSeenUserApiCall field is null or greater than the relative time of 24 hours ago, it indicates that the command is new and was not seen before. The final result table includes the firstTime, user, object, and command fields of the new commands. It also applies the security_content_ctime function to format the timestamps and applies a filter to remove any cloud API calls from previously unseen user roles. The detection is important because it helps to identify new commands run by different user roles. New commands can indicate potential malicious activity or unauthorized actions within the environment. Detecting and investigating these new commands can help identify and mitigate potential security threats earlier, preventing data breaches, unauthorized access, or other damaging outcomes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic detects when a new command is run by a user, who typically does not run those commands. The detection is made by a Splunk query to search for these commands in the Change data model. Identifies commands run by users with the user_type of AssumedRole and a status of success. The query retrieves the earliest and latest timestamps of each command run and groups the results by the user and command. Then, it drops the unnecessary data model object name and creates a lookup to verify if the command was seen before. The lookup table contains information about previously seen cloud API calls for each user role, including the first time the command was seen and whether enough data is available for analysis. If the firstTimeSeenUserApiCall field is null or greater than the relative time of 24 hours ago, it indicates that the command is new and was not seen before. The final result table includes the firstTime, user, object, and command fields of the new commands. It also applies the security_content_ctime function to format the timestamps and applies a filter to remove any cloud API calls from previously unseen user roles. The detection is important because it helps to identify new commands run by different user roles. New commands can indicate potential malicious activity or unauthorized actions within the environment. Detecting and investigating these new commands can help identify and mitigate potential security threats earlier, preventing data breaches, unauthorized access, or other damaging outcomes.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter`
action.escu.known_false_positives = .
action.escu.creation_date = 2020-09-04
action.escu.modification_date = 2020-09-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud API Calls From Previously Unseen User Roles - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ of type AssumedRole attempting to execute new API calls $command$ that have not been seen before
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud API Calls From Previously Unseen User Roles - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter`

[ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for cloud compute instances created by users who have not created them before.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud compute instances created by users who have not created them before.
action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users.
action.escu.known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior.
action.escu.creation_date = 2021-07-13
action.escu.modification_date = 2021-07-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Cloud Cryptomining"]
action.risk = 1
action.risk.param._risk_message = User $user$ is creating a new instance $dest$ for the first time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 18}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Cryptomining"], "cis20": ["CIS 10"], "confidence": 60, "impact": 30, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter`

[ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro.
action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate.
action.escu.creation_date = 2020-09-02
action.escu.modification_date = 2020-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Cloud Cryptomining"]
action.risk = 1
action.risk.param._risk_message = User $user$ is creating an instance $dest$ in a new region for the first time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Cryptomining"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter`

[ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential instances that are created in a cloud computing environment using new or unknown image IDs that have not been seen before. This detection is important because it helps to investigate and take appropriate action to prevent further damage or unauthorized access to the Cloud environment, which can include data breaches, unauthorized access to sensitive information, or the deployment of malicious payloads within the cloud environment. False positives might occur since legitimate instances can also have previously unseen image IDs. Next steps include conducting an extensive triage and investigation to determine the nature of the activity. During triage, review the details of the created instances, including the user responsible for the creation, the image ID used, and any associated metadata. Additionally, consider inspecting any relevant on-disk artifacts and analyzing concurrent processes to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic detects potential instances that are created in a cloud computing environment using new or unknown image IDs that have not been seen before. This detection is important because it helps to investigate and take appropriate action to prevent further damage or unauthorized access to the Cloud environment, which can include data breaches, unauthorized access to sensitive information, or the deployment of malicious payloads within the cloud environment. False positives might occur since legitimate instances can also have previously unseen image IDs. Next steps include conducting an extensive triage and investigation to determine the nature of the activity. During triage, review the details of the created instances, including the user responsible for the creation, the image ID used, and any associated metadata. Additionally, consider inspecting any relevant on-disk artifacts and analyzing concurrent processes to identify the source of the attack.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro.
action.escu.known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire.  Verify that the image being used was created by a legitimate user.
action.escu.creation_date = 2018-10-12
action.escu.modification_date = 2018-10-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Cloud Cryptomining"]
action.risk = 1
action.risk.param._risk_message = User $user$ is creating an instance $dest$ with an image that has not been previously seen.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Cryptomining"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter`

[ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of EC2 instances with previously unseen instance types. The detection is made by using a Splunk query to identify the EC2 instances. First, the query searches for changes in the EC2 instance creation action and filters for instances with instance types that are not recognized or previously seen. Next, the query uses the Splunk tstats command to gather the necessary information from the Change data model. Then, it filters the instances with unknown instance types and reviews previously seen instance types to determine if they are new or not. The detection is important because it identifies attackers attempting to create instances with unknown or potentially compromised instance types, which can be an attempt to gain unauthorized access to sensitive data, compromise of systems, exfiltrate data, potential disruption of services, or launch other malicious activities within the environment. False positives might occur since there might be legitimate reasons for creating instances with previously unseen instance types. Therefore, you must carefully review and triage all alerts.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic detects the creation of EC2 instances with previously unseen instance types. The detection is made by using a Splunk query to identify the EC2 instances. First, the query searches for changes in the EC2 instance creation action and filters for instances with instance types that are not recognized or previously seen. Next, the query uses the Splunk tstats command to gather the necessary information from the Change data model. Then, it filters the instances with unknown instance types and reviews previously seen instance types to determine if they are new or not. The detection is important because it identifies attackers attempting to create instances with unknown or potentially compromised instance types, which can be an attempt to gain unauthorized access to sensitive data, compromise of systems, exfiltrate data, potential disruption of services, or launch other malicious activities within the environment. False positives might occur since there might be legitimate reasons for creating instances with previously unseen instance types. Therefore, you must carefully review and triage all alerts.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro.
action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type.
action.escu.creation_date = 2020-09-12
action.escu.modification_date = 2020-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Cloud Cryptomining"]
action.risk = 1
action.risk.param._risk_message = User $user$ is creating an instance $dest$ with an instance type $instance_type$ that has not been previously seen.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Cryptomining"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`

[ESCU - Cloud Instance Modified By Previously Unseen User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for cloud instances being modified by users who have not previously modified them.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud instances being modified by users who have not previously modified them.
action.escu.how_to_implement = This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work.
action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.
action.escu.creation_date = 2020-07-29
action.escu.modification_date = 2020-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Instance Modified By Previously Unseen User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Instance Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is modifying an instance $object_id$ for the first time.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Instance Modified By Previously Unseen User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter`

[ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2020-10-09
action.escu.modification_date = 2020-10-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is starting or creating an instance $object$ for the first time in City $City$ from IP address $src$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 18}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 18}, {"risk_object_field": "object", "risk_object_type": "system", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 60, "impact": 30, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)`

[ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2020-10-09
action.escu.modification_date = 2020-10-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is starting or creating an instance $object$ for the first time in Country $Country$ from IP address $src$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "object", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)`

[ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2020-08-16
action.escu.modification_date = 2020-08-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is starting or creating an instance $object_id$ for the first time from IP address $src$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "object_id", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)`

[ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something.
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2020-08-16
action.escu.modification_date = 2020-08-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is starting or creating an instance $object$ for the first time in region $Region$ from IP address $src$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "object", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)`

[ESCU - Correlation by Repository and Risk - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects by correlating repository and risk score to identify patterns and trends in the data based on the level of risk associated. The analytic adds any null values and calculates the sum of the risk scores for each detection. Then, the analytic captures the source and user information for each detection and sorts the results in ascending order based on the risk score. Finally, the analytic filters the detections with a risk score below 80 and focuses only on high-risk detections.This detection is important because it provides valuable insights into the distribution of high-risk activities across different repositories. It also identifies the most vulnerable repositories that are frequently targeted by potential threats. Additionally, it proactively detects and responds to potential threats, thereby minimizing the impact of attacks and safeguarding critical assets. Finally, it provides a comprehensive view of the risk landscape and helps to make informed decisions to protect the organization's data and infrastructure. False positives might occur so it is important to identify the impact of the attack and prioritize response and mitigation efforts.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects by correlating repository and risk score to identify patterns and trends in the data based on the level of risk associated. The analytic adds any null values and calculates the sum of the risk scores for each detection. Then, the analytic captures the source and user information for each detection and sorts the results in ascending order based on the risk score. Finally, the analytic filters the detections with a risk score below 80 and focuses only on high-risk detections.This detection is important because it provides valuable insights into the distribution of high-risk activities across different repositories. It also identifies the most vulnerable repositories that are frequently targeted by potential threats. Additionally, it proactively detects and responds to potential threats, thereby minimizing the impact of attacks and safeguarding critical assets. Finally, it provides a comprehensive view of the risk landscape and helps to make informed decisions to protect the organization's data and infrastructure. False positives might occur so it is important to identify the impact of the attack and prioritize response and mitigation efforts.
action.escu.how_to_implement = For Dev Sec Ops POC
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-06
action.escu.modification_date = 2021-09-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Correlation by Repository and Risk - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Correlation by Repository and Risk - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 100, "impact": 70, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects by correlating repository and risk score to identify patterns and trends in the data based on the level of risk associated. The analytic adds any null values and calculates the sum of the risk scores for each detection. Then, the analytic captures the source and user information for each detection and sorts the results in ascending order based on the risk score. Finally, the analytic filters the detections with a risk score below 80 and focuses only on high-risk detections.This detection is important because it provides valuable insights into the distribution of high-risk activities across different repositories. It also identifies the most vulnerable repositories that are frequently targeted by potential threats. Additionally, it proactively detects and responds to potential threats, thereby minimizing the impact of attacks and safeguarding critical assets. Finally, it provides a comprehensive view of the risk landscape and helps to make informed decisions to protect the organization's data and infrastructure. False positives might occur so it is important to identify the impact of the attack and prioritize response and mitigation efforts.
action.notable.param.rule_title = RBA: Correlation by Repository and Risk
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `risk_index` | fillnull | stats sum(risk_score) as risk_score values(source) as signals values(user) as user by repository | sort - risk_score | where risk_score > 80 | `correlation_by_repository_and_risk_filter`

[ESCU - Correlation by User and Risk - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the correlation between the user and risk score and identifies users with a high risk score that pose a significant security risk such as unauthorized access attempts, suspicious behavior, or potential insider threats. Next, the analytic calculates the sum of the risk scores and groups the results by user, the corresponding signals, and the repository. The results are sorted in descending order based on the risk score and filtered to include records with a risk score greater than 80. Finally, the results are passed through a correlation filter specific to the user and risk. This detection is important because it identifies users who have a high risk score and helps to prioritize investigations and allocate resources. False positives might occur but the impact of such an attack can vary depending on the specific scenario such as data exfiltration, system compromise, or the disruption of critical services. Please investigate this notable event.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the correlation between the user and risk score and identifies users with a high risk score that pose a significant security risk such as unauthorized access attempts, suspicious behavior, or potential insider threats. Next, the analytic calculates the sum of the risk scores and groups the results by user, the corresponding signals, and the repository. The results are sorted in descending order based on the risk score and filtered to include records with a risk score greater than 80. Finally, the results are passed through a correlation filter specific to the user and risk. This detection is important because it identifies users who have a high risk score and helps to prioritize investigations and allocate resources. False positives might occur but the impact of such an attack can vary depending on the specific scenario such as data exfiltration, system compromise, or the disruption of critical services. Please investigate this notable event.
action.escu.how_to_implement = For Dev Sec Ops POC
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-06
action.escu.modification_date = 2021-09-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Correlation by User and Risk - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Correlation by User and Risk - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 100, "impact": 70, "mitre_attack": ["T1204.003", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the correlation between the user and risk score and identifies users with a high risk score that pose a significant security risk such as unauthorized access attempts, suspicious behavior, or potential insider threats. Next, the analytic calculates the sum of the risk scores and groups the results by user, the corresponding signals, and the repository. The results are sorted in descending order based on the risk score and filtered to include records with a risk score greater than 80. Finally, the results are passed through a correlation filter specific to the user and risk. This detection is important because it identifies users who have a high risk score and helps to prioritize investigations and allocate resources. False positives might occur but the impact of such an attack can vary depending on the specific scenario such as data exfiltration, system compromise, or the disruption of critical services. Please investigate this notable event.
action.notable.param.rule_title = RBA: Correlation by User and Risk
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `risk_index` | fillnull | stats sum(risk_score) as risk_score values(source) as signals values(repository) as repository by user | sort - risk_score | where risk_score > 80 | `correlation_by_user_and_risk_filter`

[ESCU - Detect AWS Console Login by New User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1552"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.
action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
action.escu.creation_date = 2022-05-10
action.escu.modification_date = 2022-05-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AWS Console Login by New User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect AWS Console Login by New User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1586", "T1586.003", "T1552"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | join user type=outer [ | inputlookup previously_seen_users_console_logins | stats min(firstTime) as earliestseen by user] | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "First Time Logging into AWS Console", "Previously Seen User") | where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_console_login_by_new_user_filter`

[ESCU - Detect AWS Console Login by User from New City - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1535"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro.
action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AWS Console Login by User from New City - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover", "Compromised User Account"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New City - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 60, "impact": 30, "mitre_attack": ["T1586", "T1586.003", "T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename City as justSeenCity | table firstTime lastTime user justSeenCity | join user type=outer [| inputlookup previously_seen_users_console_logins | rename City as previouslySeenCity | stats min(firstTime) AS earliestseen by user previouslySeenCity | fields earliestseen user previouslySeenCity] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | where userCity = "New City" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenCity justSeenCity userCity | `detect_aws_console_login_by_user_from_new_city_filter`

[ESCU - Detect AWS Console Login by User from New Country - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1535"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro.
action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AWS Console Login by User from New Country - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover", "Compromised User Account"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New Country - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1586", "T1586.003", "T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename Country as justSeenCountry | table firstTime lastTime user justSeenCountry | join user type=outer [| inputlookup previously_seen_users_console_logins | rename Country as previouslySeenCountry | stats min(firstTime) AS earliestseen by user previouslySeenCountry | fields earliestseen user previouslySeenCountry] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | where userCountry = "New Country" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenCountry justSeenCountry userCountry | `detect_aws_console_login_by_user_from_new_country_filter`

[ESCU - Detect AWS Console Login by User from New Region - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1535"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro.
action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AWS Console Login by User from New Region - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover", "Compromised User Account"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities", "AWS Identity and Access Management Account Takeover", "Compromised User Account"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename Region as justSeenRegion | table firstTime lastTime user justSeenRegion | join user type=outer [| inputlookup previously_seen_users_console_logins | rename Region as previouslySeenRegion | stats min(firstTime) AS earliestseen by user previouslySeenRegion | fields earliestseen user previouslySeenRegion] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | where userRegion= "New Region" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenRegion justSeenRegion userRegion | `detect_aws_console_login_by_user_from_new_region_filter`

[ESCU - Detect GCP Storage access from a new IP - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1530"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket.
action.escu.how_to_implement = This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour.  It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets.
action.escu.known_false_positives = GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours.
action.escu.creation_date = 2020-08-10
action.escu.modification_date = 2020-08-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect GCP Storage access from a new IP - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious GCP Storage Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "remote_ip", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect GCP Storage access from a new IP - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious GCP Storage Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1530"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table  first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter`

[ESCU - Detect New Open GCP Storage Buckets - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1530"], "nist": ["DE.CM"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket.
action.escu.how_to_implement = This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
action.escu.known_false_positives = While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group.
action.escu.creation_date = 2020-08-05
action.escu.modification_date = 2020-08-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Open GCP Storage Buckets - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious GCP Storage Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect New Open GCP Storage Buckets - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious GCP Storage Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1530"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket.
action.notable.param.rule_title = Detect New Open GCP Storage Buckets
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member | search (member=allUsers AND action=ADD) | table  _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter`

[ESCU - Detect New Open S3 buckets - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1530"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket.
action.escu.how_to_implement = You must install the AWS App for Splunk.
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.
action.escu.creation_date = 2021-07-19
action.escu.modification_date = 2021-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has created an open/public bucket $bucketName$ with the following permissions $permission$
action.risk.param._risk = [{"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "bucketName", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect New Open S3 buckets - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1530"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket.
action.notable.param.rule_title = Detect New Open S3 buckets
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?<json_field>{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` 

[ESCU - Detect New Open S3 Buckets over AWS CLI - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket over the aws cli.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1530"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket over the aws cli.
action.escu.how_to_implement = 
action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.
action.escu.creation_date = 2021-07-19
action.escu.modification_date = 2021-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Open S3 Buckets over AWS CLI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_message = User $userIdentity.userName$ has created an open/public bucket $bucketName$ using AWS CLI with the following permissions - $requestParameters.accessControlList.x-amz-grant-read$ $requestParameters.accessControlList.x-amz-grant-read-acp$ $requestParameters.accessControlList.x-amz-grant-write$ $requestParameters.accessControlList.x-amz-grant-write-acp$ $requestParameters.accessControlList.x-amz-grant-full-control$
action.risk.param._risk = [{"risk_object_field": "userIdentity.userName", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "bucketName", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect New Open S3 Buckets over AWS CLI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1530"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket over the aws cli.
action.notable.param.rule_title = Detect New Open S3 Buckets over AWS CLI
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` 

[ESCU - Detect S3 access from a new IP - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1530"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names.
action.escu.known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour
action.escu.creation_date = 2018-06-28
action.escu.modification_date = 2018-06-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect S3 access from a new IP - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1530"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter`

[ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
action.escu.known_false_positives = None
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Security Hub Alerts"]
action.risk = 1
action.risk.param._risk_message = Spike in AWS security Hub alerts with title $Title$ for EC2 instance $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Security Hub Alerts"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`

[ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
action.escu.known_false_positives = None
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Security Hub Alerts"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Security Hub Alerts"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter`

[ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will detect spike in blocked outbound network connections originating from within your AWS environment.  It will also update the cache file that factors in the latest data.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will detect spike in blocked outbound network connections originating from within your AWS environment.  It will also update the cache file that factors in the latest data.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections.
action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections.
action.escu.creation_date = 2018-05-07
action.escu.modification_date = 2018-05-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`

[ESCU - Detect Spike in S3 Bucket deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1530"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity.
action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment.
action.escu.creation_date = 2018-11-27
action.escu.modification_date = 2018-11-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Spike in S3 Bucket deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1530"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter`

[ESCU - GCP Authentication Failed During MFA Challenge - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an authentication attempt event against a Google Cloud Platform tenant that fails during the Multi Factor Authentication challenge. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004", "T1621"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an authentication attempt event against a Google Cloud Platform tenant that fails during the Multi Factor Authentication challenge. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled. 
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events.
action.escu.known_false_positives = Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake.
action.escu.creation_date = 2022-10-12
action.escu.modification_date = 2022-10-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Authentication Failed During MFA Challenge - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Account Takeover"]
action.risk = 1
action.risk.param._risk_message = User $user$ failed to pass MFA challenge
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 54}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Authentication Failed During MFA Challenge - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004", "T1621"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an authentication attempt event against a Google Cloud Platform tenant that fails during the Multi Factor Authentication challenge. This behavior may represent an adversary trying to authenticate with compromised credentials for an account that has multi-factor authentication enabled. 
action.notable.param.rule_title = GCP Authentication Failed During MFA Challenge
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | stats values(user) by _time, src_ip, login_challenge_method | `gcp_authentication_failed_during_mfa_challenge_filter`

[ESCU - GCP Detect gcploit framework - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.
action.escu.how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
action.escu.known_false_positives = Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects
action.escu.creation_date = 2020-10-08
action.escu.modification_date = 2020-10-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Detect gcploit framework - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Cross Account Activity"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Detect gcploit framework - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.
action.notable.param.rule_title = GCP Detect gcploit framework
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent | `gcp_detect_gcploit_framework_filter`

[ESCU - GCP Kubernetes cluster pod scan detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods
action.escu.how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods  will provide context.
action.escu.creation_date = 2020-07-17
action.escu.modification_date = 2020-07-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Kubernetes cluster pod scan detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Kubernetes cluster pod scan detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` category=kube-audit |spath input=properties.log |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter`

[ESCU - GCP Multi-Factor Authentication Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies an attempt to disable multi-factor authentication for a GCP user. An adversary who has obtained access to an GCP tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1556", "T1556.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies an attempt to disable multi-factor authentication for a GCP user. An adversary who has obtained access to an GCP tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the Admin log events.
action.escu.known_false_positives = Legitimate use case may require for users to disable MFA. Filter as needed.
action.escu.creation_date = 2022-10-12
action.escu.modification_date = 2022-10-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Multi-Factor Authentication Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Account Takeover"]
action.risk = 1
action.risk.param._risk_message = MFA disabled for User $user$ initiated by $actor.email$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "actor.email", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Multi-Factor Authentication Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1586", "T1586.003", "T1556", "T1556.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies an attempt to disable multi-factor authentication for a GCP user. An adversary who has obtained access to an GCP tenant may disable multi-factor authentication as a way to plant a backdoor and maintain persistence using a valid account. This way the attackers can keep persistance in the environment without adding new users.
action.notable.param.rule_title = GCP Multi-Factor Authentication Disabled
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `gws_reports_admin` command=UNENROLL_USER_FROM_STRONG_AUTH | stats values(user) by _time, command, actor.email, status | `gcp_multi_factor_authentication_disabled_filter`

[ESCU - GCP Multiple Failed MFA Requests For User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies multiple failed multi-factor authentication requests for a single user within a Google Cloud Platform tenant. Specifically, the analytic triggers when 10 or more MFA user prompts fail within 5 minutes. Google CLoud tenants can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1621", "T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies multiple failed multi-factor authentication requests for a single user within a Google Cloud Platform tenant. Specifically, the analytic triggers when 10 or more MFA user prompts fail within 5 minutes. Google CLoud tenants can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `mfa_prompts` threshold values according to your environment. Specifically, this analytic leverages the User log events.
action.escu.known_false_positives = Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
action.escu.creation_date = 2022-10-14
action.escu.modification_date = 2022-10-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Multiple Failed MFA Requests For User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Multiple Failed MFA requests for user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 54}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Multiple Failed MFA Requests For User - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1621", "T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies multiple failed multi-factor authentication requests for a single user within a Google Cloud Platform tenant. Specifically, the analytic triggers when 10 or more MFA user prompts fail within 5 minutes. Google CLoud tenants can be very different depending on the organization, Security teams should test this detection and customize these arbitrary thresholds. The detected behavior may represent an adversary who has obtained legitimate credentials for a user and continuously repeats login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls potentially resulting in the user finally accepting the authentication request. Threat actors like the Lapsus team and APT29 have leveraged this technique to bypass multi-factor authentication controls as reported by Mandiant and others.
action.notable.param.rule_title = GCP Multiple Failed MFA Requests For User
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | bucket span=5m _time | stats dc(_raw) AS mfa_prompts values(user) AS user by src_ip, login_challenge_method,  _time | where mfa_prompts >= 10 | `gcp_multiple_failed_mfa_requests_for_user_filter`

[ESCU - GCP Multiple Users Failing To Authenticate From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source Ip failing to authenticate into the Google Workspace user accounts with more than 20 unique valid users within 5 minutes. These user accounts may have other privileges with respect to access to other sensitive resources in the Google Cloud Platform. This behavior could represent an adversary performing a Password Spraying attack against an Google Workspace environment to obtain initial access or elevate privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source Ip failing to authenticate into the Google Workspace user accounts with more than 20 unique valid users within 5 minutes. These user accounts may have other privileges with respect to access to other sensitive resources in the Google Cloud Platform. This behavior could represent an adversary performing a Password Spraying attack against an Google Workspace environment to obtain initial access or elevate privileges.
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `unique_accounts` threshold values according to your environment. Specifically, this analytic leverages the User log events.
action.escu.known_false_positives = No known false postives for this detection. Please review this alert.
action.escu.creation_date = 2022-10-12
action.escu.modification_date = 2022-10-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Multiple Users Failing To Authenticate From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Multiple failed login attempts against users $tried_accounts$ seen from $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 54}, {"risk_object_field": "tried_accounts", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Multiple Users Failing To Authenticate From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gws_reports_login` event.type = login event.name = login_failure | bucket span=5m _time | stats count dc(user) AS unique_accounts values(user) as tried_accounts values(authentication_method) AS authentication_method earliest(_time) as firstTime latest(_time) as lastTime by _time event.name src app id.applicationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |  where unique_accounts > 20 | `gcp_multiple_users_failing_to_authenticate_from_ip_filter`

[ESCU - GCP Successful Single-Factor Authentication - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a successful authentication event against Google Cloud Platform for an account without Multi-Factor Authentication enabled. This could be evidence of a missconfiguration, a policy violation or an account take over attempt that should be investigated
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a successful authentication event against Google Cloud Platform for an account without Multi-Factor Authentication enabled. This could be evidence of a missconfiguration, a policy violation or an account take over attempt that should be investigated
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events.
action.escu.known_false_positives = Although not recommended, certain users may be required without multi-factor authentication. Filter as needed
action.escu.creation_date = 2022-10-12
action.escu.modification_date = 2022-10-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Successful Single-Factor Authentication - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Successful authentication for user $user$ without MFA
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 45}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Successful Single-Factor Authentication - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1586", "T1586.003", "T1078", "T1078.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a successful authentication event against Google Cloud Platform for an account without Multi-Factor Authentication enabled. This could be evidence of a missconfiguration, a policy violation or an account take over attempt that should be investigated
action.notable.param.rule_title = GCP Successful Single-Factor Authentication
action.notable.param.security_domain = identity
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `gws_reports_login` event.name=login_success NOT `gws_login_mfa_methods` | stats values(user) by _time, src_ip, login_challenge_method | `gcp_successful_single_factor_authentication_filter`

[ESCU - GCP Unusual Number of Failed Authentications From Ip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source IP failing to authenticate into the Google Workspace with multiple valid users. This behavior could represent an adversary performing a Password Spraying attack against a Google Workspace enviroment to obtain initial access or elevate privileges. The detection calculates the standard deviation for source IP and leverages the 3-sigma statistical rule to identify an unusual number of failed authentication attempts. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.  While looking for anomalies using statistical methods like the standard deviation can have benefits, we also recommend using threshold-based detections to complement coverage. A similar analytic following the threshold model is `GCP Multiple Users Failing To Authenticate From Ip`
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source IP failing to authenticate into the Google Workspace with multiple valid users. This behavior could represent an adversary performing a Password Spraying attack against a Google Workspace enviroment to obtain initial access or elevate privileges. The detection calculates the standard deviation for source IP and leverages the 3-sigma statistical rule to identify an unusual number of failed authentication attempts. To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. This logic can be used for real time security monitoring as well as threat hunting exercises.  While looking for anomalies using statistical methods like the standard deviation can have benefits, we also recommend using threshold-based detections to complement coverage. A similar analytic following the threshold model is `GCP Multiple Users Failing To Authenticate From Ip`
action.escu.how_to_implement = You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `unique_accounts` threshold values according to your environment. Specifically, this analytic leverages the User log events.
action.escu.known_false_positives = No known false positives for this detection. Please review this alert
action.escu.creation_date = 2022-10-13
action.escu.modification_date = 2022-10-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Unusual Number of Failed Authentications From Ip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Account Takeover"]
action.risk = 1
action.risk.param._risk_message = Unusual number of failed console login attempts against users $tried_accounts$ seen from $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 54}, {"risk_object_field": "tried_accounts", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GCP Unusual Number of Failed Authentications From Ip - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Account Takeover"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1586", "T1586.003", "T1110", "T1110.003", "T1110.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gws_reports_login` event.type = login event.name = login_failure| bucket span=5m _time | stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts values(authentication_method) AS authentication_method by _time, src | eventstats  avg(unique_accounts) as ip_avg , stdev(unique_accounts) as ip_std by _time | eval  upperBound=(ip_avg+ip_std*3) | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | where isOutlier =1| `gcp_unusual_number_of_failed_authentications_from_ip_filter`

[ESCU - Gdrive suspicious file sharing - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search can help the detection of compromised accounts or internal users sharing potentially malicious/classified documents with users outside your organization via GSuite file sharing .
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search can help the detection of compromised accounts or internal users sharing potentially malicious/classified documents with users outside your organization via GSuite file sharing .
action.escu.how_to_implement = Need to implement Gsuite logging targeting Google suite drive activity. In order for the search to work for your environment please update `yourdomain.com` value in the query with the domain relavant for your organization.
action.escu.known_false_positives = This is an anomaly search, you must specify your domain in the parameters so it either filters outside domains or focus on internal domains. This search may also help investigate compromise of accounts. By looking at for example source ip addresses, document titles and abnormal number of shares and shared target users.
action.escu.creation_date = 2021-10-24
action.escu.modification_date = 2021-10-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gdrive suspicious file sharing - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spearphishing Attachments", "Data Exfiltration"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gdrive suspicious file sharing - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_drive` name=change_user_access | rename parameters.* as * | search email = "*@yourdomain.com" target_user != "*@yourdomain.com" | stats count values(owner) as owner values(target_user) as target values(doc_type) as doc_type values(doc_title) as doc_title dc(target_user) as distinct_target by src_ip email | where distinct_target > 50 | `gdrive_suspicious_file_sharing_filter`

[ESCU - GitHub Actions Disable Security Workflow - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects a disabled security workflow in GitHub Actions. An attacker can disable a security workflow in GitHub actions to hide malicious code in it.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1195.002", "T1195"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects a disabled security workflow in GitHub Actions. An attacker can disable a security workflow in GitHub actions to hide malicious code in it.
action.escu.how_to_implement = You must index GitHub logs. You can follow the url in reference to onboard GitHub logs. Sometimes GitHub logs are truncated, make sure to disable it in props.conf. Replace *security-testing* with the name of your security testing workflow in GitHub Actions.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-04-04
action.escu.modification_date = 2022-04-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - GitHub Actions Disable Security Workflow - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Security Workflow is disabled in branch $branch$ for repository $repository$
action.risk.param._risk = [{"risk_object_field": "repository", "risk_object_type": "other", "risk_score": 27}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GitHub Actions Disable Security Workflow - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 90, "impact": 30, "mitre_attack": ["T1195.002", "T1195"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `github` workflow_run.event=push OR workflow_run.event=pull_request | stats values(workflow_run.name) as workflow_run.name by workflow_run.head_commit.id workflow_run.event workflow_run.head_branch workflow_run.head_commit.author.email workflow_run.head_commit.author.name workflow_run.head_commit.message workflow_run.head_commit.timestamp workflow_run.head_repository.full_name workflow_run.head_repository.owner.id workflow_run.head_repository.owner.login workflow_run.head_repository.owner.type | rename workflow_run.head_commit.author.name as user, workflow_run.head_commit.author.email as user_email, workflow_run.head_repository.full_name as repository, workflow_run.head_branch as branch | search NOT workflow_run.name=*security-testing* | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_actions_disable_security_workflow_filter`

[ESCU - Github Commit Changes In Master - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a pushed or commit to master or main branch. This is to avoid unwanted modification to master without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1199"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a pushed or commit to master or main branch. This is to avoid unwanted modification to master without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project.
action.escu.known_false_positives = admin can do changes directly to master branch
action.escu.creation_date = 2021-08-20
action.escu.modification_date = 2021-08-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Github Commit Changes In Master - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = suspicious commit by $commit.commit.author.email$ to main branch
action.risk.param._risk = [{"risk_object_field": "commit.commit.author.email", "risk_object_type": "user", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Github Commit Changes In Master - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1199"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `github` branches{}.name = main OR branches{}.name = master |  stats count min(_time) as firstTime max(_time) as lastTime by commit.commit.author.email commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date repository.full_name | rename commit.author.login as user, repository.full_name as repository | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_commit_changes_in_master_filter`

[ESCU - Github Commit In Develop - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a pushed or commit to develop branch. This is to avoid unwanted modification to develop without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1199"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a pushed or commit to develop branch. This is to avoid unwanted modification to develop without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project.
action.escu.known_false_positives = admin can do changes directly to develop branch
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Github Commit In Develop - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = suspicious commit by $commit.commit.author.email$ to develop branch
action.risk.param._risk = [{"risk_object_field": "commit.commit.author.email", "risk_object_type": "user", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Github Commit In Develop - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1199"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `github` branches{}.name = main OR branches{}.name = develop |  stats count min(_time) as firstTime max(_time) as lastTime  by commit.author.html_url commit.commit.author.email commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_commit_in_develop_filter`

[ESCU - GitHub Dependabot Alert - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is made by first searching for logs that contain the action "create" and renames certain fields for easier analysis. Then, this analytic uses the "stats" command to calculate the first and last occurrence of the alert based on the timestamp. The fields included in the output are the action, affected package name, affected range, created date, external identifier, external reference, fixed version, severity, repository, repository URL, and user. The "phase" field is set to "code" to indicate that the alert pertains to code-related issues. The detection is important because dependabot Alerts can indicate vulnerabilities in the codebase that can be exploited by attackers. Detecting and investigating these alerts can help a SOC to proactively address security risks and prevent potential breaches or unauthorized access to sensitive information. False positives might occur since there are legitimate actions that trigger the "create" action or if other factors exist that can generate similar log entries. Next steps include reviewing the details of the alert, such as the affected package, severity, and fixed version to determine the appropriate response and mitigation steps.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1195.001", "T1195"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic is made by first searching for logs that contain the action "create" and renames certain fields for easier analysis. Then, this analytic uses the "stats" command to calculate the first and last occurrence of the alert based on the timestamp. The fields included in the output are the action, affected package name, affected range, created date, external identifier, external reference, fixed version, severity, repository, repository URL, and user. The "phase" field is set to "code" to indicate that the alert pertains to code-related issues. The detection is important because dependabot Alerts can indicate vulnerabilities in the codebase that can be exploited by attackers. Detecting and investigating these alerts can help a SOC to proactively address security risks and prevent potential breaches or unauthorized access to sensitive information. False positives might occur since there are legitimate actions that trigger the "create" action or if other factors exist that can generate similar log entries. Next steps include reviewing the details of the alert, such as the affected package, severity, and fixed version to determine the appropriate response and mitigation steps.
action.escu.how_to_implement = You must index GitHub logs. You can follow the url in reference to onboard GitHub logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - GitHub Dependabot Alert - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Vulnerabilities found in packages used by GitHub repository $repository$
action.risk.param._risk = [{"risk_object_field": "repository", "risk_object_type": "other", "risk_score": 27}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GitHub Dependabot Alert - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 90, "impact": 30, "mitre_attack": ["T1195.001", "T1195"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `github` alert.id=* action=create | rename repository.full_name as repository, repository.html_url as repository_url sender.login as user | stats min(_time) as firstTime max(_time) as lastTime by action alert.affected_package_name alert.affected_range alert.created_at alert.external_identifier alert.external_reference alert.fixed_in alert.severity repository repository_url user | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_dependabot_alert_filter`

[ESCU - GitHub Pull Request from Unknown User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects pull requests from unknown users on GitHub. The detection is made by using a Splunk query to search for pull requests in the `check_suite.pull_requests` field where the `id` is not specified. Next, the analytic retrieves information such as the author's name, the repository's full name, the head reference of the pull request, and the commit message from the `check_suite.head_commit` field. The analytic also includes a step to exclude known users by using the `github_known_users` lookup table, which helps to filter out pull requests from known users and focus on the pull requests from unknown users. The detection is important because it locates potential malicious activity or unauthorized access since unknown users can introduce malicious code or gain unauthorized access to repositories leading to unauthorized code changes, data breaches, or other security incidents. Next steps include reviewing the author's name, the repository involved, the head reference of the pull request, and the commit message upon triage of a potential pull request from an unknown user. You must also analyze any relevant on-disk artifacts and investigate any concurrent processes to determine the source and intent of the pull request."
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1195.001", "T1195"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects pull requests from unknown users on GitHub. The detection is made by using a Splunk query to search for pull requests in the `check_suite.pull_requests` field where the `id` is not specified. Next, the analytic retrieves information such as the author's name, the repository's full name, the head reference of the pull request, and the commit message from the `check_suite.head_commit` field. The analytic also includes a step to exclude known users by using the `github_known_users` lookup table, which helps to filter out pull requests from known users and focus on the pull requests from unknown users. The detection is important because it locates potential malicious activity or unauthorized access since unknown users can introduce malicious code or gain unauthorized access to repositories leading to unauthorized code changes, data breaches, or other security incidents. Next steps include reviewing the author's name, the repository involved, the head reference of the pull request, and the commit message upon triage of a potential pull request from an unknown user. You must also analyze any relevant on-disk artifacts and investigate any concurrent processes to determine the source and intent of the pull request."
action.escu.how_to_implement = You must index GitHub logs. You can follow the url in reference to onboard GitHub logs.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - GitHub Pull Request from Unknown User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Vulnerabilities found in packages used by GitHub repository $repository$
action.risk.param._risk = [{"risk_object_field": "repository", "risk_object_type": "other", "risk_score": 27}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GitHub Pull Request from Unknown User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 90, "impact": 30, "mitre_attack": ["T1195.001", "T1195"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `github` check_suite.pull_requests{}.id=* | stats count by check_suite.head_commit.author.name repository.full_name check_suite.pull_requests{}.head.ref check_suite.head_commit.message | rename check_suite.head_commit.author.name as user repository.full_name as repository check_suite.pull_requests{}.head.ref as ref_head check_suite.head_commit.message as commit_message | search NOT `github_known_users` | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_pull_request_from_unknown_user_filter`

[ESCU - Gsuite Drive Share In External Email - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect suspicious google drive or google docs files shared outside or externally. This behavior might be a good hunting query to monitor exfitration of data made by an attacker or insider to a targetted machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1567.002", "T1567"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect suspicious google drive or google docs files shared outside or externally. This behavior might be a good hunting query to monitor exfitration of data made by an attacker or insider to a targetted machine.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. In order for the search to work for your environment, please edit the query to use your company specific email domain instead of `internal_test_email.com`.
action.escu.known_false_positives = network admin or normal user may share files to customer and external team.
action.escu.creation_date = 2021-08-16
action.escu.modification_date = 2021-08-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gsuite Drive Share In External Email - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops", "Insider Threat"]
action.risk = 1
action.risk.param._risk_message = suspicious share gdrive from $parameters.owner$ to $email$ namely as $parameters.doc_title$
action.risk.param._risk = [{"risk_object_field": "parameters.owner", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "email", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gsuite Drive Share In External Email - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops", "Insider Threat"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1567.002", "T1567"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_drive` NOT (email IN("", "null")) | rex field=parameters.owner "[^@]+@(?<src_domain>[^@]+)" | rex field=email "[^@]+@(?<dest_domain>[^@]+)" | where src_domain = "internal_test_email.com" and not dest_domain = "internal_test_email.com" | eval phase="plan" | eval severity="low" | stats values(parameters.doc_title) as doc_title, values(parameters.doc_type) as doc_types, values(email) as dst_email_list, values(parameters.visibility) as visibility, values(parameters.doc_id) as doc_id, count min(_time) as firstTime max(_time) as lastTime by parameters.owner ip_address phase severity  | rename parameters.owner as user ip_address as src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_drive_share_in_external_email_filter`

[ESCU - GSuite Email Suspicious Attachment - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious attachment file extension in Gsuite email that may related to spear phishing attack. This file type is commonly used by malware to lure user to click on it to execute malicious code to compromised targetted machine. But this search can also catch some normal files related to this file type that maybe send by employee or network admin.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious attachment file extension in Gsuite email that may related to spear phishing attack. This file type is commonly used by malware to lure user to click on it to execute malicious code to compromised targetted machine. But this search can also catch some normal files related to this file type that maybe send by employee or network admin.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
action.escu.known_false_positives = network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack.
action.escu.creation_date = 2021-08-16
action.escu.modification_date = 2021-08-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - GSuite Email Suspicious Attachment - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = suspicious email from $source.address$ to $destination{}.address$
action.risk.param._risk = [{"risk_object_field": "source.address", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "destination{}.address", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GSuite Email Suspicious Attachment - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_gmail` "attachment{}.file_extension_type" IN ("pl", "py", "rb", "sh", "bat", "exe", "dll", "cpl", "com", "js", "vbs", "ps1", "reg","swf", "cmd", "go") | eval phase="plan" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size by destination{}.service num_message_attachments  subject destination{}.address source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_suspicious_attachment_filter`

[ESCU - Gsuite Email Suspicious Subject With Attachment - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a gsuite email contains suspicious subject having known file type used in spear phishing. This technique is a common and effective entry vector of attacker to compromise a network by luring the user to click or execute the suspicious attachment send from external email account because of the effective social engineering of subject related to delivery, bank and so on. On the other hand this detection may catch a normal email traffic related to legitimate transaction so better to check the email sender, spelling and etc. avoid click link or opening the attachment if you are not expecting this type of e-mail.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a gsuite email contains suspicious subject having known file type used in spear phishing. This technique is a common and effective entry vector of attacker to compromise a network by luring the user to click or execute the suspicious attachment send from external email account because of the effective social engineering of subject related to delivery, bank and so on. On the other hand this detection may catch a normal email traffic related to legitimate transaction so better to check the email sender, spelling and etc. avoid click link or opening the attachment if you are not expecting this type of e-mail.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
action.escu.known_false_positives = normal user or normal transaction may contain the subject and file type attachment that this detection try to search.
action.escu.creation_date = 2021-08-19
action.escu.modification_date = 2021-08-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gsuite Email Suspicious Subject With Attachment - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = suspicious email from $source.address$ to $destination{}.address$
action.risk.param._risk = [{"risk_object_field": "source.address", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gsuite Email Suspicious Subject With Attachment - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_gmail` num_message_attachments > 0 subject IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "* fedex *", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") attachment{}.file_extension_type IN ("doc", "docx", "xls", "xlsx", "ppt", "pptx", "pdf", "zip", "rar", "html","htm","hta") | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size by destination{}.service num_message_attachments  subject destination{}.address source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_suspicious_subject_with_attachment_filter`

[ESCU - Gsuite Email With Known Abuse Web Service Link - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
action.escu.known_false_positives = normal email contains this link that are known application within the organization or network can be catched by this detection.
action.escu.creation_date = 2021-08-23
action.escu.modification_date = 2021-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gsuite Email With Known Abuse Web Service Link - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = suspicious email from $source.address$ to $destination{}.address$
action.risk.param._risk = [{"risk_object_field": "source.address", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gsuite Email With Known Abuse Web Service Link - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me") | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" |stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count by is_spam source.address source.from_header_address subject destination{}.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_with_known_abuse_web_service_link_filter`

[ESCU - Gsuite Outbound Email With Attachment To External Domain - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious outbound e-mail from internal email to external email domain. This can be a good hunting query to monitor insider or outbound email traffic for not common domain e-mail. The idea is to parse the domain of destination email check if there is a minimum outbound traffic < 20 with attachment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious outbound e-mail from internal email to external email domain. This can be a good hunting query to monitor insider or outbound email traffic for not common domain e-mail. The idea is to parse the domain of destination email check if there is a minimum outbound traffic < 20 with attachment.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
action.escu.known_false_positives = network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack.
action.escu.creation_date = 2021-08-17
action.escu.modification_date = 2021-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gsuite Outbound Email With Attachment To External Domain - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops", "Insider Threat"]
action.risk = 1
action.risk.param._risk_message = suspicious email from $source.address$ to $destination{}.address$
action.risk.param._risk = [{"risk_object_field": "dest_domain", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gsuite Outbound Email With Attachment To External Domain - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops", "Insider Threat"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_gmail` num_message_attachments > 0 | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" | where source_domain="internal_test_email.com" and not dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" | stats values(subject) as subject, values(source.from_header_address) as src_domain_list, count as numEvents, dc(source.from_header_address) as numSrcAddresses, min(_time) as firstTime max(_time) as lastTime by dest_domain phase severity | where numSrcAddresses < 20 |sort - numSrcAddresses | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_outbound_email_with_attachment_to_external_domain_filter`

[ESCU - Gsuite suspicious calendar invite - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search can help the detection of compromised accounts or internal users sending suspcious calendar invites via GSuite calendar. These invites may contain malicious links or attachments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search can help the detection of compromised accounts or internal users sending suspcious calendar invites via GSuite calendar. These invites may contain malicious links or attachments.
action.escu.how_to_implement = In order to successfully implement this search, you need to be ingesting logs related to gsuite (gsuite:calendar:json) having the file sharing metadata like file type, source owner, destination target user, description, etc. This search can also be made more specific by selecting specific emails, subdomains timeframe, organizational units, targeted user, etc. In order for the search to work for your environment please update `yourdomain.com` value in the query with the domain relavant for your organization.
action.escu.known_false_positives = This search will also produce normal activity statistics. Fields such as email, ip address, name, parameters.organizer_calendar_id, parameters.target_calendar_id and parameters.event_title may give away phishing intent.For more specific results use email parameter.
action.escu.creation_date = 2021-10-24
action.escu.modification_date = 2021-10-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gsuite suspicious calendar invite - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spearphishing Attachments"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gsuite suspicious calendar invite - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_calendar` |bin span=5m _time |rename parameters.* as * |search target_calendar_id!=null email="*yourdomain.com"| stats  count values(target_calendar_id) values(event_title) values(event_guest) by email _time | where count >100| `gsuite_suspicious_calendar_invite_filter`

[ESCU - Gsuite Suspicious Shared File Name - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a shared file in google drive with suspicious file name that are commonly used by spear phishing campaign. This technique is very popular to lure the user by running a malicious document or click a malicious link within the shared file that will redirected to malicious website. This detection can also catch some normal email communication between organization and its external customer.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a shared file in google drive with suspicious file name that are commonly used by spear phishing campaign. This technique is very popular to lure the user by running a malicious document or click a malicious link within the shared file that will redirected to malicious website. This detection can also catch some normal email communication between organization and its external customer.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. In order for the search to work for your environment, please edit the query to use your company specific email domain instead of `internal_test_email.com`.
action.escu.known_false_positives = normal user or normal transaction may contain the subject and file type attachment that this detection try to search
action.escu.creation_date = 2021-08-23
action.escu.modification_date = 2021-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Gsuite Suspicious Shared File Name - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = suspicious share gdrive from $parameters.owner$ to $email$ namely as $parameters.doc_title$
action.risk.param._risk = [{"risk_object_field": "parameters.owner", "risk_object_type": "user", "risk_score": 21}, {"risk_object_field": "email", "risk_object_type": "user", "risk_score": 21}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Gsuite Suspicious Shared File Name - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `gsuite_drive` parameters.owner_is_team_drive=false "parameters.doc_title" IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "*fedex*", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") parameters.doc_type IN ("document","pdf", "msexcel", "msword", "spreadsheet", "presentation") | rex field=parameters.owner "[^@]+@(?<source_domain>[^@]+)" | rex field=parameters.target_user "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" | stats count min(_time) as firstTime max(_time) as lastTime by email parameters.owner parameters.target_user parameters.doc_title parameters.doc_type phase severity | rename parameters.target_user AS user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_suspicious_shared_file_name_filter`

[ESCU - High Number of Login Failures from a single source - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.001", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment.
action.escu.how_to_implement = 
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - High Number of Login Failures from a single source - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - High Number of Login Failures from a single source - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1110.001", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter`

[ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
action.escu.known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
action.escu.creation_date = 2022-11-11
action.escu.modification_date = 2022-11-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous  | stats  count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_filter`

[ESCU - Kubernetes Nginx Ingress LFI - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1212"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks.
action.escu.how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-20
action.escu.modification_date = 2021-08-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Nginx Ingress LFI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Local File Inclusion Attack detected on $host$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kubernetes Nginx Ingress LFI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "mitre_attack": ["T1212"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks.
action.notable.param.rule_title = Kubernetes Nginx Ingress LFI
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_container_controller` | rex field=_raw "^(?<remote_addr>\S+)\s+-\s+-\s+\[(?<time_local>[^\]]*)\]\s\"(?<request>[^\"]*)\"\s(?<status>\S*)\s(?<body_bytes_sent>\S*)\s\"(?<http_referer>[^\"]*)\"\s\"(?<http_user_agent>[^\"]*)\"\s(?<request_length>\S*)\s(?<request_time>\S*)\s\[(?<proxy_upstream_name>[^\]]*)\]\s\[(?<proxy_alternative_upstream_name>[^\]]*)\]\s(?<upstream_addr>\S*)\s(?<upstream_response_length>\S*)\s(?<upstream_response_time>\S*)\s(?<upstream_status>\S*)\s(?<req_id>\S*)" | lookup local_file_inclusion_paths local_file_inclusion_paths AS request OUTPUT lfi_path | search lfi_path=yes | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | rex field=request "^(?<http_method>\S+)\s(?<url>\S+)\s" | eval phase="operate" | eval severity="high" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, status, url, http_method, host, http_user_agent, proxy, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_lfi_filter`

[ESCU - Kubernetes Nginx Ingress RFI - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1212"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks.
action.escu.how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-23
action.escu.modification_date = 2021-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Nginx Ingress RFI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Remote File Inclusion Attack detected on $host$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kubernetes Nginx Ingress RFI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "mitre_attack": ["T1212"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks.
action.notable.param.rule_title = Kubernetes Nginx Ingress RFI
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_container_controller` | rex field=_raw "^(?<remote_addr>\S+)\s+-\s+-\s+\[(?<time_local>[^\]]*)\]\s\"(?<request>[^\"]*)\"\s(?<status>\S*)\s(?<body_bytes_sent>\S*)\s\"(?<http_referer>[^\"]*)\"\s\"(?<http_user_agent>[^\"]*)\"\s(?<request_length>\S*)\s(?<request_time>\S*)\s\[(?<proxy_upstream_name>[^\]]*)\]\s\[(?<proxy_alternative_upstream_name>[^\]]*)\]\s(?<upstream_addr>\S*)\s(?<upstream_response_length>\S*)\s(?<upstream_response_time>\S*)\s(?<upstream_status>\S*)\s(?<req_id>\S*)" | rex field=request "^(?<http_method>\S+)?\s(?<url>\S+)\s" | rex field=url "(?<dest_ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" | search dest_ip=* | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | eval phase="operate" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, dest_ip status, url, http_method, host, http_user_agent, proxy, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_rfi_filter`

[ESCU - Kubernetes Scanner Image Pulling - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1526"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner.
action.escu.how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Scanner Image Pulling - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Kubernetes Scanner image pulled on host $host$
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kubernetes Scanner Image Pulling - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 90, "impact": 90, "mitre_attack": ["T1526"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner.
action.notable.param.rule_title = Kubernetes Scanner Image Pulling
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kube_objects_events` object.message IN ("Pulling image *kube-hunter*", "Pulling image *kube-bench*", "Pulling image *kube-recon*", "Pulling image *kube-recon*") | rename object.* AS * | rename involvedObject.* AS * | rename source.host AS host | eval phase="operate" | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime count by host, name, namespace, kind, reason, message, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_scanner_image_pulling_filter`

[ESCU - O365 Add App Role Assignment Grant User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is designed to detect the creation of a new Federation setting by alerting on a specific event associated with its creation. By monitoring for this event, the search can identify any instances where a Federation setting is being created within the system. This can help in detecting and monitoring any unauthorized or suspicious changes to the Federation settings, providing an additional layer of security for your environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is designed to detect the creation of a new Federation setting by alerting on a specific event associated with its creation. By monitoring for this event, the search can identify any instances where a Federation setting is being created within the system. This can help in detecting and monitoring any unauthorized or suspicious changes to the Federation settings, providing an additional layer of security for your environment.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Add App Role Assignment Grant User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $user$ has created a new federation setting $modified_properties_name$ on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 18}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Add App Role Assignment Grant User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 60, "impact": 30, "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is designed to detect the creation of a new Federation setting by alerting on a specific event associated with its creation. By monitoring for this event, the search can identify any instances where a Federation setting is being created within the system. This can help in detecting and monitoring any unauthorized or suspicious changes to the Federation settings, providing an additional layer of security for your environment.
action.notable.param.rule_title = O365 Add App Role Assignment Grant User
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type values(ModifiedProperties{}.Name) as modified_properties_name by user dest ResultStatus Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter`

[ESCU - O365 Added Service Principal - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects addition of new service principal accounts added to O365 tenants. Attackers can abuse service principals in Office 365 (now known as Microsoft 365) to gain unauthorized access and perform malicious actions within an organization's environment. Service principals are essentially non-human accounts used by applications, services, or scripts to access resources and interact with APIs on behalf of the organization.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects addition of new service principal accounts added to O365 tenants. Attackers can abuse service principals in Office 365 (now known as Microsoft 365) to gain unauthorized access and perform malicious actions within an organization's environment. Service principals are essentially non-human accounts used by applications, services, or scripts to access resources and interact with APIs on behalf of the organization.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.
action.escu.creation_date = 2023-08-02
action.escu.modification_date = 2023-08-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Added Service Principal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $src_user$ has created new service principal $new_value$ in AzureActiveDirectory
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Added Service Principal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects addition of new service principal accounts added to O365 tenants. Attackers can abuse service principals in Office 365 (now known as Microsoft 365) to gain unauthorized access and perform malicious actions within an organization's environment. Service principals are essentially non-human accounts used by applications, services, or scripts to access resources and interact with APIs on behalf of the organization.
action.notable.param.rule_title = O365 Added Service Principal
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Workload=AzureActiveDirectory Operation="*Add service principal*" OR (Operation = "*principal*" AND action = "created") | stats count values(ModifiedProperties{}.NewValue) as new_value by src_user src_user_type action Operation authentication_service Workload | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_added_service_principal_filter`

[ESCU - O365 Bypass MFA via Trusted IP - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system.
action.escu.how_to_implement = You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration.
action.escu.creation_date = 2022-02-03
action.escu.modification_date = 2022-02-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Bypass MFA via Trusted IP - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections"]
action.risk = 1
action.risk.param._risk_message = User $user_id$ has added new IP addresses $ip_addresses_new_added$ to a list of trusted IPs to bypass MFA
action.risk.param._risk = [{"risk_object_field": "ip_addresses_new_added", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "user_id", "risk_object_type": "user", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Bypass MFA via Trusted IP - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system.
action.notable.param.rule_title = O365 Bypass MFA via Trusted IP
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added Operation Workload vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter`

[ESCU - O365 Disable MFA - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1556"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user
action.escu.how_to_implement = You must install the Splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = Unless it is a special case, it is uncommon to disable MFA or Strong Authentication
action.escu.creation_date = 2022-02-03
action.escu.modification_date = 2022-02-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Disable MFA - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections"]
action.risk = 1
action.risk.param._risk_message = User $src_user$ has executed an operation $action$ for user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Disable MFA - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1556"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user
action.notable.param.rule_title = O365 Disable MFA
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation UserId ResultStatus object | rename UserType AS user_type, Operation AS action, UserId AS src_user, object AS user, ResultStatus AS result | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_disable_mfa_filter`

[ESCU - O365 Excessive Authentication Failures Alert - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = The threshold for alert is above 10 attempts and this should reduce the number of false positives.
action.escu.creation_date = 2022-02-18
action.escu.modification_date = 2022-02-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Excessive Authentication Failures Alert - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections"]
action.risk = 1
action.risk.param._risk_message = User $user$ has caused excessive number of authentication failures from $src_ip$ using UserAgent $UserAgent$.
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Excessive Authentication Failures Alert - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=failure | stats count earliest(_time) AS firstTime latest(_time) AS lastTime values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter`

[ESCU - O365 Excessive SSO logon errors - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1556"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack.
action.escu.creation_date = 2023-08-02
action.escu.modification_date = 2023-08-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Excessive SSO logon errors - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = Excessive number of SSO logon errors from $src_ip$ using UserAgent $user_agent$.
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Excessive SSO logon errors - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1556"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Workload=AzureActiveDirectory LogonError=*Sso* Operation=UserLoginFailed | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by  src_ip signature user_agent authentication_service action| where count >= 5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter`

[ESCU - O365 New Federated Domain Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the addition of a new federated domain in an organization's Office 365 environment. Identifies instances where a new federated domain is added to the organization's Office 365 configuration and helps to take immediate action to mitigate the risks, prevent further unauthorized access, and protect the organization's data and systems. The detection is made by the Splunk query `o365_management_activity` with the parameters `Workload=Exchange` and `Operation="Add-FederatedDomain"`, which analyzes the management activity logs in Office 365 and filters for the specific operation to add a federated domain. The detection is important because identifying the addition of a new federated domain can indicate potential unauthorized access or compromise of the organization's Office 365 environment. A new Federated domain can be added by an attacker to gain unauthorized access, exfiltrate data, or carry out other malicious activity, which can lead to data breaches, unauthorized access to sensitive information, or compromise of the organization's systems and infrastructure. Next steps include viewing the details of the added federated domain, including the organization name, originating server, user ID, and user key. You must also capture and analyze any relevant on-disk artifacts. Additionally, you must identify the source of the attack by looking for concurrent processes or other indicators of compromise.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the addition of a new federated domain in an organization's Office 365 environment. Identifies instances where a new federated domain is added to the organization's Office 365 configuration and helps to take immediate action to mitigate the risks, prevent further unauthorized access, and protect the organization's data and systems. The detection is made by the Splunk query `o365_management_activity` with the parameters `Workload=Exchange` and `Operation="Add-FederatedDomain"`, which analyzes the management activity logs in Office 365 and filters for the specific operation to add a federated domain. The detection is important because identifying the addition of a new federated domain can indicate potential unauthorized access or compromise of the organization's Office 365 environment. A new Federated domain can be added by an attacker to gain unauthorized access, exfiltrate data, or carry out other malicious activity, which can lead to data breaches, unauthorized access to sensitive information, or compromise of the organization's systems and infrastructure. Next steps include viewing the details of the added federated domain, including the organization name, originating server, user ID, and user key. You must also capture and analyze any relevant on-disk artifacts. Additionally, you must identify the source of the attack by looking for concurrent processes or other indicators of compromise.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity.
action.escu.known_false_positives = The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider.
action.escu.creation_date = 2023-08-02
action.escu.modification_date = 2023-08-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 New Federated Domain Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $user$ has added a new federated domain $new_value$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 New Federated Domain Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1136.003", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the addition of a new federated domain in an organization's Office 365 environment. Identifies instances where a new federated domain is added to the organization's Office 365 configuration and helps to take immediate action to mitigate the risks, prevent further unauthorized access, and protect the organization's data and systems. The detection is made by the Splunk query `o365_management_activity` with the parameters `Workload=Exchange` and `Operation="Add-FederatedDomain"`, which analyzes the management activity logs in Office 365 and filters for the specific operation to add a federated domain. The detection is important because identifying the addition of a new federated domain can indicate potential unauthorized access or compromise of the organization's Office 365 environment. A new Federated domain can be added by an attacker to gain unauthorized access, exfiltrate data, or carry out other malicious activity, which can lead to data breaches, unauthorized access to sensitive information, or compromise of the organization's systems and infrastructure. Next steps include viewing the details of the added federated domain, including the organization name, originating server, user ID, and user key. You must also capture and analyze any relevant on-disk artifacts. Additionally, you must identify the source of the attack by looking for concurrent processes or other indicators of compromise.
action.notable.param.rule_title = O365 New Federated Domain Added
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation IN ("*add*", "*new*") AND Operation="*domain*" | stats count values(ModifiedProperties{}.NewValue) as new_value by  user user_agent authentication_service action Workload Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter`

[ESCU - O365 PST export alert - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1114"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored.
action.escu.creation_date = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 PST export alert - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = User $Source$ has exported a PST file from the search using this operation- $Operation$ with a severity of $Severity$
action.risk.param._risk = [{"risk_object_field": "Source", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 PST export alert - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 60, "impact": 80, "mitre_attack": ["T1114"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content
action.notable.param.rule_title = O365 PST export alert
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter`

[ESCU - O365 Suspicious Admin Email Forwarding - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Suspicious Admin Email Forwarding - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = User $user$ has configured a forwarding rule for multiple mailboxes to the same destination $ForwardingAddress$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Suspicious Admin Email Forwarding - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 60, "impact": 80, "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter`

[ESCU - O365 Suspicious Rights Delegation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1114.002", "T1114"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = Service Accounts
action.escu.creation_date = 2020-12-15
action.escu.modification_date = 2020-12-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Suspicious Rights Delegation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections"]
action.risk = 1
action.risk.param._risk_message = User $user$ has delegated suspicious rights $AccessRights$ to user $dest_user$ that allow access to sensitive
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Suspicious Rights Delegation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 10"], "confidence": 60, "impact": 80, "mitre_attack": ["T1114.002", "T1114"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account.
action.notable.param.rule_title = O365 Suspicious Rights Delegation
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter`

[ESCU - O365 Suspicious User Email Forwarding - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects when multiple users have configured a forwarding rule to the same destination to proactively identify and investigate potential security risks related to email forwarding and take appropriate actions to protect the organization's data and prevent unauthorized access or data breaches. This detection is made by a Splunk query to O365 management activity logs with the operation `Set-Mailbox` to gather information about mailbox configurations. Then, the query uses the `spath` function to extract the parameters and rename the "Identity" field as "src_user" and searches for entries where the "ForwardingSmtpAddress" field is not empty, which indicates the presence of a forwarding rule. Next, the analytic uses the `stats` command to group the results by the forwarding email address and count the number of unique source users (`src_user`). Finally, it filters the results and only retains entries where the count of source users (`count_src_user`) is greater than 1, which indicates that multiple users have set up forwarding rules to the same destination. This detection is important because it suggests that multiple users are forwarding emails to the same destination without proper authorization, which can lead to the exposure of sensitive information, loss of data control, or unauthorized access to confidential emails. Investigating and addressing this issue promptly can help prevent data breaches and mitigate potential damage.indicates a potential security risk since multiple users forwarding emails to the same destination can be a sign of unauthorized access, data exfiltration, or a compromised account. Additionally, it also helps to determine if the forwarding rules are legitimate or if they indicate a security incident. False positives can occur if there are legitimate reasons for multiple users to forward emails to the same destination, such as a shared mailbox or a team collaboration scenario. Next steps include further investigation and context analysis to determine the legitimacy of the forwarding rules.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects when multiple users have configured a forwarding rule to the same destination to proactively identify and investigate potential security risks related to email forwarding and take appropriate actions to protect the organization's data and prevent unauthorized access or data breaches. This detection is made by a Splunk query to O365 management activity logs with the operation `Set-Mailbox` to gather information about mailbox configurations. Then, the query uses the `spath` function to extract the parameters and rename the "Identity" field as "src_user" and searches for entries where the "ForwardingSmtpAddress" field is not empty, which indicates the presence of a forwarding rule. Next, the analytic uses the `stats` command to group the results by the forwarding email address and count the number of unique source users (`src_user`). Finally, it filters the results and only retains entries where the count of source users (`count_src_user`) is greater than 1, which indicates that multiple users have set up forwarding rules to the same destination. This detection is important because it suggests that multiple users are forwarding emails to the same destination without proper authorization, which can lead to the exposure of sensitive information, loss of data control, or unauthorized access to confidential emails. Investigating and addressing this issue promptly can help prevent data breaches and mitigate potential damage.indicates a potential security risk since multiple users forwarding emails to the same destination can be a sign of unauthorized access, data exfiltration, or a compromised account. Additionally, it also helps to determine if the forwarding rules are legitimate or if they indicate a security incident. False positives can occur if there are legitimate reasons for multiple users to forward emails to the same destination, such as a shared mailbox or a team collaboration scenario. Next steps include further investigation and context analysis to determine the legitimacy of the forwarding rules.
action.escu.how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Suspicious User Email Forwarding - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Office 365 Detections", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = User $user$ configured multiple users $src_user$ with a count of $count_src_user$, a forwarding rule to same destination $ForwardingSmtpAddress$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "ForwardingSmtpAddress", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - O365 Suspicious User Email Forwarding - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 60, "impact": 80, "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter`

[ESCU - Abnormally High AWS Instances Launched by User - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. The threshold value should be tuned to your environment.
action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Abnormally High AWS Instances Launched by User - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining", "Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter`

[ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. The threshold value should be tuned to your environment.
action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Abnormally High AWS Instances Launched by User - MLTK - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining", "Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time  | stats count as instances_launched by _time src_user  | apply ec2_excessive_runinstances_v1  | rename "IsOutlier(instances_launched)" as isOutlier  | where isOutlier=1

[ESCU - Abnormally High AWS Instances Terminated by User - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs.
action.escu.known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated by User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Abnormally High AWS Instances Terminated by User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter`

[ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. The threshold value should be tuned to your environment.
action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Abnormally High AWS Instances Terminated by User - MLTK - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time  | stats count as instances_terminated by _time src_user  | apply ec2_excessive_terminateinstances_v1  | rename "IsOutlier(instances_terminated)" as isOutlier  | where isOutlier=1

[ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities.  Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.  
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen cities.  Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2018-03-16
action.escu.modification_date = 2018-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From Previously Unseen City - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter`

[ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.  
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2018-03-16
action.escu.modification_date = 2018-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From Previously Unseen Country - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter`

[ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.  
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2018-03-16
action.escu.modification_date = 2018-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From Previously Unseen IP Address - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter`

[ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources.
action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\
 This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
action.escu.creation_date = 2018-03-16
action.escu.modification_date = 2018-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From Previously Unseen Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter`

[ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets
action.escu.how_to_implement = You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs.
action.escu.known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - AWS EKS Kubernetes cluster sensitive object access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` objectRef.resource=secrets OR configmaps sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1  |table sourceIPs{} user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`aws_eks_kubernetes_cluster_sensitive_object_access_filter`

[ESCU - Clients Connecting to Multiple DNS Servers - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.
action.escu.how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\
This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Clients Connecting to Multiple DNS Servers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DNS Hijacking", "Suspicious DNS Traffic", "Host Redirection", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Clients Connecting to Multiple DNS Servers - Rule
action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking", "Suspicious DNS Traffic", "Host Redirection", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.
action.notable.param.rule_title = Clients Connecting to Multiple DNS Servers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` 

[ESCU - Cloud Network Access Control List Deleted - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate 
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate
action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro.
action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL.
action.escu.creation_date = 2020-09-08
action.escu.modification_date = 2020-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cloud Network Access Control List Deleted - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Cloud Network ACL Activity"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Cloud Network Access Control List Deleted - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Network ACL Activity"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn  | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `cloud_network_access_control_list_deleted_filter`

[ESCU - Detect Activity Related to Pass the Hash Attacks - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. This search is DEPRECATED as it is possible for event code 4624 to generate a high level of noise, as legitimate logon events may also trigger this event code. This can be especially true in environments with high levels of user activity, such as those with many concurrent logons or frequent logon attempts. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550", "T1550.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. This search is DEPRECATED as it is possible for event code 4624 to generate a high level of noise, as legitimate logon events may also trigger this event code. This can be especially true in environments with high levels of user activity, such as those with many concurrent logons or frequent logon attempts.
action.escu.how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows.
action.escu.known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.
action.escu.creation_date = 2020-10-15
action.escu.modification_date = 2020-10-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Activity Related to Pass the Hash Attacks - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Activity Related to Pass the Hash Attacks - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1550", "T1550.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter`

[ESCU - Detect API activity from users without MFA - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. 
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\
This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\
1. \
1. **Label:** AWS User ARN, **Field:** userIdentity.arn\
1. \
1. **Label:** AWS User Type, **Field:** userIdentity.type\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS.
action.escu.creation_date = 2018-05-17
action.escu.modification_date = 2018-05-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect API activity from users without MFA - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS User Monitoring"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect API activity from users without MFA - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats  count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter`

[ESCU - Detect AWS API Activities From Unapproved Accounts - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for successful AWS CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for successful AWS CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\
This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\
1. \
1. **Label:** First Time, **Field:** firstTime\
1. \
1. **Label:** Last Time, **Field:** lastTime\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AWS API Activities From Unapproved Accounts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS User Monitoring"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect AWS API Activities From Unapproved Accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_api_activities_from_unapproved_accounts_filter`

[ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1566.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution", "Web"]
action.escu.eli5 = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites.
action.escu.how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \
 **Splunk>Phantom Playbook Integration**\
If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\

action.escu.known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Common Phishing Frameworks"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Common Phishing Frameworks"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites.
action.notable.param.rule_title = Detect DNS requests to Phishing Sites leveraging EvilGinx2
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter`

[ESCU - Detect Long DNS TXT Record Response - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, Command And Control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, Command And Control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses.
action.escu.how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol.
action.escu.known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious DNS Traffic", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Long DNS TXT Record Response - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious DNS Traffic", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, Command And Control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses.
action.notable.param.rule_title = Detect Long DNS TXT Record Response
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type |  `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type"  "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter`

[ESCU - Detect Mimikatz Using Loaded Images - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.
action.escu.how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = Other tools can import the same DLLs. These tools should be part of a whitelist. False positives may be present with any process that authenticates or uses credentials, PowerShell included. Filter based on parent process.
action.escu.creation_date = 2019-12-03
action.escu.modification_date = 2019-12-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Mimikatz Using Loaded Images - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping", "Detect Zerologon Attack", "Cloud Federated Credential Abuse", "DarkSide Ransomware", "CISA AA22-257A", "CISA AA22-264A", "CISA AA22-320A", "Sandworm Tools"]
action.risk = 1
action.risk.param._risk_message = A process, $Image$, has loaded $ImageLoaded$ that are typically related to credential dumping on $dest$. Review for further details.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Mimikatz Using Loaded Images - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Detect Zerologon Attack", "Cloud Federated Credential Abuse", "DarkSide Ransomware", "CISA AA22-257A", "CISA AA22-264A", "CISA AA22-320A", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.
action.notable.param.rule_title = Detect Mimikatz Using Loaded Images
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by dest, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter`

[ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective.
action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs.  More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes.
action.escu.known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.
action.escu.creation_date = 2019-02-27
action.escu.modification_date = 2019-02-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Federated Credential Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective.
action.notable.param.rule_title = Detect Mimikatz Via PowerShell And EventCode 4703
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?<privs>\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process |  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter`

[ESCU - Detect new API calls from user roles - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in AWS CloudTrail" support search once to create a history of previously seen user roles.
action.escu.known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger.
action.escu.creation_date = 2018-04-16
action.escu.modification_date = 2018-04-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect new API calls from user roles - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect new API calls from user roles - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success  userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName |  inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName]  |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter`

[ESCU - Detect new user AWS Console Login - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen users in AWS CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in AWS CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.
action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect new user AWS Console Login - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Suspicious AWS Login Activities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect new user AWS Console Login - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail  | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console"  | `detect_new_user_aws_console_login_filter`

[ESCU - Detect Spike in AWS API Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes of API activity in your AWS environment.  It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will detect users creating spikes of API activity in your AWS environment.  It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\
This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\
1. \
1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\
1. \
1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = 
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Spike in AWS API Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter`

[ESCU - Detect Spike in Network ACL Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1562.007"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`.
action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment.
action.escu.creation_date = 2018-05-21
action.escu.modification_date = 2018-05-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in Network ACL Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Spike in Network ACL Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Network ACL Activity"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.007"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter`

[ESCU - Detect Spike in Security Group Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to security groups in your AWS environment.  It will also update the cache file that factors in the latest data.  This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search will detect users creating spikes in API activity related to security groups in your AWS environment.  It will also update the cache file that factors in the latest data.  This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment.
action.escu.creation_date = 2018-04-18
action.escu.modification_date = 2018-04-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in Security Group Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect Spike in Security Group Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter`

[ESCU - Detect USB device insertion - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change", "Change_Analysis"]
action.escu.eli5 = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework.
action.escu.known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate.
action.escu.creation_date = 2017-11-27
action.escu.modification_date = 2017-11-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect USB device insertion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Protection"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect USB device insertion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Protection"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework.
action.notable.param.rule_title = Detect USB device insertion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)`  | `detect_usb_device_insertion_filter`

[ESCU - Detect web traffic to dynamic domain providers - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for web connections to dynamic DNS providers. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1071.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This search looks for web connections to dynamic DNS providers.
action.escu.how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\
This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate.
action.escu.known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect web traffic to dynamic domain providers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Dynamic DNS"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detect web traffic to dynamic domain providers - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dynamic DNS"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for web connections to dynamic DNS providers.
action.notable.param.rule_title = Detect web traffic to dynamic domain providers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter`

[ESCU - Detection of DNS Tunnels - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, Command And Control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. \
NOTE:Deprecated because existing detection is doing the same. This detection is replaced with two other variations, if you are using MLTK then you can use this search `ESCU - DNS Query Length Outliers - MLTK - Rule` or use the standard deviation version `ESCU - DNS Query Length With High Standard Deviation - Rule`, as an alternantive. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, Command And Control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. \
NOTE:Deprecated because existing detection is doing the same. This detection is replaced with two other variations, if you are using MLTK then you can use this search `ESCU - DNS Query Length Outliers - MLTK - Rule` or use the standard deviation version `ESCU - DNS Query Length With High Standard Deviation - Rule`, as an alternantive.
action.escu.how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue.
action.escu.known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment.
action.escu.creation_date = 2022-02-15
action.escu.modification_date = 2022-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detection of DNS Tunnels - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Detection of DNS Tunnels - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Protection", "Suspicious DNS Traffic", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, Command And Control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. \
NOTE:Deprecated because existing detection is doing the same. This detection is replaced with two other variations, if you are using MLTK then you can use this search `ESCU - DNS Query Length Outliers - MLTK - Rule` or use the standard deviation version `ESCU - DNS Query Length With High Standard Deviation - Rule`, as an alternantive.
action.notable.param.rule_title = Detection of DNS Tunnels
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` dc("DNS.query") as count  from datamodel=Network_Resolution  where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*"   ) by "DNS.src","DNS.query" | rename "DNS.src" as src  "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count  from datamodel=Network_Resolution  where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*"   ) by "DNS.src","DNS.answer" | rename "DNS.src" as src  "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter`

[ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1071.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework.
action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security.
action.escu.known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DNS Hijacking", "Suspicious DNS Traffic", "Host Redirection", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule
action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking", "Suspicious DNS Traffic", "Host Redirection", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework.
action.notable.param.rule_title = DNS Query Requests Resolved by Unauthorized DNS Servers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` 

[ESCU - DNS record changed - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1071.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day.
action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \
 **Splunk>Phantom Playbook Integration**\
If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\

action.escu.known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - DNS record changed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DNS Hijacking"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - DNS record changed - Rule
action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day.
action.notable.param.rule_title = DNS record changed
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer |  makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer  | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter`

[ESCU - Dump LSASS via procdump Rename - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2021-02-01
action.escu.modification_date = 2021-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Dump LSASS via procdump Rename - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping", "HAFNIUM Group", "CISA AA22-257A"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Dump LSASS via procdump Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group", "CISA AA22-257A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` OriginalFileName=procdump  process_name!=procdump*.exe  EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* |  stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter`

[ESCU - EC2 Instance Modified With Previously Unseen User - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unseen User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["Unusual AWS EC2 Modifications"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Modified With Previously Unseen User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual AWS EC2 Modifications"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter`

[ESCU - EC2 Instance Started In Previously Unseen Region - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started 
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for AWS CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions.  This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate.
action.escu.creation_date = 2018-02-23
action.escu.modification_date = 2018-02-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - EC2 Instance Started In Previously Unseen Region - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Started In Previously Unseen Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining", "Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1535"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter`

[ESCU - EC2 Instance Started With Previously Unseen AMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen AMIs.  This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for EC2 instances being created with previously unseen AMIs.  This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs.
action.escu.known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire.  Verify that the AMI being used was created by a legitimate user.
action.escu.creation_date = 2018-03-12
action.escu.modification_date = 2018-03-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen AMI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cryptomining"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Started With Previously Unseen AMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter`

[ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen instance types.  This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for EC2 instances being created with previously unseen instance types.  This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types.
action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type.
action.escu.creation_date = 2020-02-07
action.escu.modification_date = 2020-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cryptomining"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Started With Previously Unseen Instance Type - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter`

[ESCU - EC2 Instance Started With Previously Unseen User - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs.
action.escu.known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Amazon Web Services - Cloudtrail"]
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Started With Previously Unseen User - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining", "Suspicious AWS EC2 Activities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter`

[ESCU - Execution of File With Spaces Before Extension - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2020-11-19
action.escu.modification_date = 2020-11-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Execution of File With Spaces Before Extension - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Execution of File With Spaces Before Extension - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1036.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.
action.notable.param.rule_title = Execution of File With Spaces Before Extension
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*     .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter`

[ESCU - Extended Period Without Successful Netbackup Backups - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring.
action.escu.how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Extended Period Without Successful Netbackup Backups - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Monitor Backup Solution"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Extended Period Without Successful Netbackup Backups - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor Backup Solution"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter`

[ESCU - First time seen command line argument - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - First time seen command line argument - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Hidden Cobra Malware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - First time seen command line argument - Rule
action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Hidden Cobra Malware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` 

[ESCU - GCP Detect accounts with high risk roles by project - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema.
action.escu.how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
action.escu.known_false_positives = Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization
action.escu.creation_date = 2020-10-09
action.escu.modification_date = 2020-10-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Detect accounts with high risk roles by project - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - GCP Detect accounts with high risk roles by project - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.protoPayload.request.policy.bindings{}.role=roles/owner OR roles/editor OR roles/iam.serviceAccountUser OR roles/iam.serviceAccountAdmin OR roles/iam.serviceAccountTokenCreator OR roles/dataflow.developer OR roles/dataflow.admin OR roles/composer.admin OR roles/dataproc.admin OR roles/dataproc.editor | table data.resource.type data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.authorizationInfo{}.resource data.protoPayload.response.bindings{}.role data.protoPayload.response.bindings{}.members{} | `gcp_detect_accounts_with_high_risk_roles_by_project_filter`

[ESCU - GCP Detect high risk permissions by resource and account - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges.
action.escu.how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
action.escu.known_false_positives = High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives.
action.escu.creation_date = 2020-10-09
action.escu.modification_date = 2020-10-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Detect high risk permissions by resource and account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - GCP Detect high risk permissions by resource and account - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.permission=iam.serviceAccounts.getaccesstoken OR iam.serviceAccounts.setIamPolicy OR iam.serviceAccounts.actas OR dataflow.jobs.create OR composer.environments.create OR dataproc.clusters.create |table data.protoPayload.requestMetadata.callerIp data.protoPayload.authenticationInfo.principalEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.response.bindings{}.members{} data.resource.labels.project_id | `gcp_detect_high_risk_permissions_by_resource_and_account_filter`

[ESCU - gcp detect oauth token abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally.
action.escu.how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
action.escu.known_false_positives = GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs.
action.escu.creation_date = 2020-09-01
action.escu.modification_date = 2020-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - gcp detect oauth token abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["GCP Cross Account Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - gcp detect oauth token abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Cross Account Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` type.googleapis.com/google.cloud.audit.AuditLog |table protoPayload.@type protoPayload.status.details{}.@type protoPayload.status.details{}.violations{}.callerIp protoPayload.status.details{}.violations{}.type protoPayload.status.message  | `gcp_detect_oauth_token_abuse_filter`

[ESCU - GCP Kubernetes cluster scan detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1526"], "nist": ["DE.CM"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster
action.escu.how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs.
action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context.
action.escu.creation_date = 2020-04-15
action.escu.modification_date = 2020-04-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP Kubernetes cluster scan detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - GCP Kubernetes cluster scan detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1526"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster
action.notable.param.rule_title = GCP Kubernetes cluster scan detection
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`  | `gcp_kubernetes_cluster_scan_detection_filter` 

[ESCU - Identify New User Accounts - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week.
action.escu.how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework.
action.escu.known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately.
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Identify New User Accounts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Account Monitoring and Controls"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Identify New User Accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | from datamodel Identity_Management.All_Identities  | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter`

[ESCU - Kubernetes AWS detect most active service accounts by pod - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
action.escu.known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes AWS detect most active service accounts by pod - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes AWS detect most active service accounts by pod - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts  objectRef.resource=pods | table  sourceIPs{} user.username userAgent verb annotations.authorization.k8s.io/decision  | top  sourceIPs{} user.username verb annotations.authorization.k8s.io/decision |`kubernetes_aws_detect_most_active_service_accounts_by_pod_filter`

[ESCU - Kubernetes AWS detect sensitive role access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
action.escu.known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. 
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes AWS detect sensitive role access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes AWS detect sensitive role access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrolebindings sourceIPs{}!=::1 sourceIPs{}!=127.0.0.1  | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter`

[ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI
action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
action.escu.known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes AWS detect service accounts forbidden failure access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `aws_cloudwatchlogs_eks` user.groups{}=system:serviceaccounts responseStatus.status = Failure | table sourceIPs{} user.username userAgent verb responseStatus.status requestURI | `kubernetes_aws_detect_service_accounts_forbidden_failure_access_filter`

[ESCU - Kubernetes Azure active service accounts by pod namespace - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness.
action.escu.creation_date = 2020-05-26
action.escu.modification_date = 2020-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure active service accounts by pod namespace - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure active service accounts by pod namespace - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* OR user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow  | table  sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_active_service_accounts_by_pod_namespace_filter`

[ESCU - Kubernetes Azure detect RBAC authorization by account - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
action.escu.creation_date = 2020-05-26
action.escu.modification_date = 2020-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure detect RBAC authorization by account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure detect RBAC authorization by account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter`

[ESCU - Kubernetes Azure detect sensitive object access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure detect sensitive object access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure detect sensitive object access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps user.username=system.anonymous OR annotations.authorization.k8s.io/decision=allow  |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter`

[ESCU - Kubernetes Azure detect sensitive role access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. 
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure detect sensitive role access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure detect sensitive role access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter`

[ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts with failure or forbidden access status 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes service accounts with failure or forbidden access status
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure detect service accounts forbidden failure access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts*  responseStatus.reason=Forbidden | table  sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace  |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter`

[ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on rare Kubectl calls with IP, verb namespace and object access context 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on rare Kubectl calls with IP, verb namespace and object access context
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets
action.escu.creation_date = 2020-05-26
action.escu.modification_date = 2020-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure detect suspicious kubectl calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log | spath input=responseObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | search userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 | table sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI | rare sourceIPs{} verb userAgent user.groups{} objectRef.resource objectRef.namespace requestURI |`kubernetes_azure_detect_suspicious_kubectl_calls_filter`

[ESCU - Kubernetes Azure pod scan fingerprint - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure pod scan fingerprint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure pod scan fingerprint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table  sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter`

[ESCU - Kubernetes Azure scan fingerprint - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure
action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
action.escu.known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.
action.escu.creation_date = 2020-05-19
action.escu.modification_date = 2020-05-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes Azure scan fingerprint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes Azure scan fingerprint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1526"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table  sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter`

[ESCU - Kubernetes GCP detect most active service accounts by pod - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision
action.escu.how_to_implement = You must install splunk GCP add on. This search works with pubsub messaging service logs
action.escu.known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.
action.escu.creation_date = 2020-07-10
action.escu.modification_date = 2020-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes GCP detect most active service accounts by pod - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect most active service accounts by pod - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message`  data.protoPayload.request.spec.group{}=system:serviceaccounts | table src_ip src_user http_user_agent data.protoPayload.request.spec.nonResourceAttributes.verb data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource | top src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.protoPayload.response.spec.resourceAttributes.resource |`kubernetes_gcp_detect_most_active_service_accounts_by_pod_filter`

[ESCU - Kubernetes GCP detect RBAC authorizations by account - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences
action.escu.how_to_implement = You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs
action.escu.known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
action.escu.creation_date = 2020-07-11
action.escu.modification_date = 2020-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes GCP detect RBAC authorizations by account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect RBAC authorizations by account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole  | table src_ip src_user data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | rare src_user data.labels.authorization.k8s.io/reason |`kubernetes_gcp_detect_rbac_authorizations_by_account_filter`

[ESCU - Kubernetes GCP detect sensitive object access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets
action.escu.how_to_implement = You must install splunk add on for GCP . This search works with pubsub messaging service logs.
action.escu.known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
action.escu.creation_date = 2020-07-11
action.escu.modification_date = 2020-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes GCP detect sensitive object access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect sensitive object access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.protoPayload.authorizationInfo{}.resource=configmaps OR secrets  | table data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name data.protoPayload.request.metadata.namespace data.labels.authorization.k8s.io/decision | dedup data.protoPayload.requestMetadata.callerIp src_user data.resource.labels.cluster_name |`kubernetes_gcp_detect_sensitive_object_access_filter`

[ESCU - Kubernetes GCP detect sensitive role access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
action.escu.how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging servicelogs.
action.escu.known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. 
action.escu.creation_date = 2020-07-11
action.escu.modification_date = 2020-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes GCP detect sensitive role access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect sensitive role access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=ClusterRoleBinding OR Clusterrole dest=apis/rbac.authorization.k8s.io/v1 src_ip!=::1  | table src_ip src_user http_user_agent data.labels.authorization.k8s.io/decision data.labels.authorization.k8s.io/reason | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter`

[ESCU - Kubernetes GCP detect service accounts forbidden failure access - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI
action.escu.how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging service logs.
action.escu.known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes GCP detect service accounts forbidden failure access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect service accounts forbidden failure access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` system:serviceaccounts data.protoPayload.response.status.allowed!=* | table src_ip src_user http_user_agent data.protoPayload.response.spec.resourceAttributes.namespace data.resource.labels.cluster_name data.protoPayload.response.spec.resourceAttributes.verb  data.protoPayload.request.status.allowed data.protoPayload.response.status.reason data.labels.authorization.k8s.io/decision | dedup src_ip src_user | `kubernetes_gcp_detect_service_accounts_forbidden_failure_access_filter`

[ESCU - Kubernetes GCP detect suspicious kubectl calls - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
action.escu.how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging logs.
action.escu.known_false_positives = Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
action.escu.creation_date = 2020-07-11
action.escu.modification_date = 2020-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kubernetes GCP detect suspicious kubectl calls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect suspicious kubectl calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter`

[ESCU - Monitor DNS For Brand Abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. 
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse.
action.escu.how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. You also need the [`dnstwist`](https://gist.github.com/d1vious/c4c2aae7fa7d5cbb1f24adc5f6303ac1) custom command.
action.escu.known_false_positives = None at this time
action.escu.creation_date = 2017-09-23
action.escu.modification_date = 2017-09-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Monitor DNS For Brand Abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brand Monitoring"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Monitor DNS For Brand Abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brand Monitoring"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse.
action.notable.param.rule_title = Monitor DNS For Brand Abuse
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter`

[ESCU - Open Redirect in Splunk Web - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. 
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability.
action.escu.how_to_implement = No extra steps needed to implement this search.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2017-09-19
action.escu.modification_date = 2017-09-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Open Redirect in Splunk Web - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Open Redirect in Splunk Web - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2016-4859"], "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability.
action.notable.param.rule_title = Open Redirect in Splunk Web
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter`

[ESCU - Osquery pack - ColdRoot detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for ColdRoot events from the osx-attacks osquery pack. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for ColdRoot events from the osx-attacks osquery pack.
action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model
action.escu.known_false_positives = There are no known false positives.
action.escu.creation_date = 2019-01-29
action.escu.modification_date = 2019-01-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Osquery pack - ColdRoot detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["ColdRoot MacOS RAT"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Osquery pack - ColdRoot detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["ColdRoot MacOS RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for ColdRoot events from the osx-attacks osquery pack.
action.notable.param.rule_title = Osquery pack - ColdRoot detection
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter`

[ESCU - Processes created by netsh - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft.
action.escu.creation_date = 2020-11-23
action.escu.modification_date = 2020-11-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Processes created by netsh - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Netsh Abuse"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Processes created by netsh - Rule
action.correlationsearch.annotations = {"analytic_story": ["Netsh Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type.
action.notable.param.rule_title = Processes created by netsh
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter`

[ESCU - Prohibited Software On Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as prohibited. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for applications on the endpoint that you have marked as prohibited.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2019-10-11
action.escu.modification_date = 2019-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Prohibited Software On Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Monitor for Unauthorized Software", "Emotet Malware  DHS Report TA18-201A ", "SamSam Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Prohibited Software On Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor for Unauthorized Software", "Emotet Malware  DHS Report TA18-201A ", "SamSam Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_processes` | `prohibited_software_on_endpoint_filter`

[ESCU - Reg exe used to hide files directories via registry keys - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for command-line arguments used to hide a file or directory using the reg add command. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for command-line arguments used to hide a file or directory using the reg add command.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None at the moment
action.escu.creation_date = 2019-02-27
action.escu.modification_date = 2019-02-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Reg exe used to hide files directories via registry keys - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Reg exe used to hide files directories via registry keys - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for command-line arguments used to hide a file or directory using the reg add command.
action.notable.param.rule_title = Reg exe used to hide files directories via registry keys
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter`

[ESCU - Remote Registry Key modifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search monitors for remote modifications to registry keys. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search monitors for remote modifications to registry keys.
action.escu.how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right.
action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out.
action.escu.creation_date = 2020-03-02
action.escu.modification_date = 2020-03-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Registry Key modifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Remote Registry Key modifications - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search monitors for remote modifications to registry keys.
action.notable.param.rule_title = Remote Registry Key modifications
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path="\\\\*"  by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter`

[ESCU - Scheduled tasks used in BadRabbit ransomware - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = No known false positives
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Scheduled tasks used in BadRabbit ransomware - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Scheduled tasks used in BadRabbit ransomware - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection
action.notable.param.rule_title = Scheduled tasks used in BadRabbit ransomware
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process  from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*"  OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter`

[ESCU - Spectre and Meltdown Vulnerable Systems - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Vulnerabilities"]
action.escu.eli5 = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities.
action.escu.how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified.
action.escu.known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied.
action.escu.creation_date = 2017-01-07
action.escu.modification_date = 2017-01-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spectre and Meltdown Vulnerable Systems - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Spectre and Meltdown Vulnerable Systems - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spectre And Meltdown Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2017-5753"], "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities.
action.notable.param.rule_title = Spectre and Meltdown Vulnerable Systems
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter`

[ESCU - Splunk Enterprise Information Disclosure - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. 
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug.
action.escu.how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives.
action.escu.known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information.
action.escu.creation_date = 2018-06-14
action.escu.modification_date = 2018-06-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Enterprise Information Disclosure - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Splunk Enterprise Information Disclosure - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2018-11409"], "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug.
action.notable.param.rule_title = Splunk Enterprise Information Disclosure
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter`

[ESCU - Suspicious Changes to File Associations - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions.
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Changes to File Associations - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Suspicious Changes to File Associations - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1546.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area.
action.notable.param.rule_title = Suspicious Changes to File Associations
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count from datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` 

[ESCU - Suspicious Email - UBA Anomaly - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566"], "nist": ["DE.AE"]}
action.escu.data_models = ["Email", "UEBA"]
action.escu.eli5 = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA).
action.escu.how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called  "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance.
action.escu.known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender.
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Email - UBA Anomaly - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Emails"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Suspicious Email - UBA Anomaly - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Emails"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_email___uba_anomaly_filter`

[ESCU - Suspicious File Write - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for files created with names that have been linked to malicious activity. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for files created with names that have been linked to malicious activity.
action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor.
action.escu.known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate.
action.escu.creation_date = 2019-04-25
action.escu.modification_date = 2019-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious File Write - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hidden Cobra Malware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Suspicious File Write - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` | `suspicious_file_write_filter`

[ESCU - Suspicious Powershell Command-Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common.
action.escu.creation_date = 2021-01-19
action.escu.modification_date = 2021-01-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Powershell Command-Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Suspicious Powershell Command-Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command
action.notable.param.rule_title = Suspicious Powershell Command-Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* | `suspicious_powershell_command_line_arguments_filter`

[ESCU - Suspicious Rundll32 Rename - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The following hunting analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the original filename or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1036", "T1218.011", "T1036.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the original filename or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
action.escu.creation_date = 2022-04-07
action.escu.modification_date = 2022-04-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Rundll32 Rename - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Masquerading - Rename System Utilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Suspicious Rundll32 Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Masquerading - Rename System Utilities"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1218", "T1036", "T1218.011", "T1036.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=RUNDLL32.exe AND Processes.process_name!=rundll32.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_rename_filter`

[ESCU - Suspicious writes to System Volume Information - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects writes to the 'System Volume Information' folder by something other than the System process.
action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate.
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious writes to System Volume Information - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Collection and Staging"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Suspicious writes to System Volume Information - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1036"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = (`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_writes_to_system_volume_information_filter`

[ESCU - Uncommon Processes On Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as uncommon. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for applications on the endpoint that you have marked as uncommon.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Uncommon Processes On Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Privilege Escalation", "Unusual Processes", "Hermetic Wiper"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Uncommon Processes On Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Privilege Escalation", "Unusual Processes", "Hermetic Wiper"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1204.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` 

[ESCU - Unsigned Image Loaded by LSASS - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects loading of unsigned images by LSASS. Deprecated because too noisy. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects loading of unsigned images by LSASS. Deprecated because too noisy.
action.escu.how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs.
action.escu.creation_date = 2019-12-06
action.escu.modification_date = 2019-12-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unsigned Image Loaded by LSASS - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Unsigned Image Loaded by LSASS - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects loading of unsigned images by LSASS. Deprecated because too noisy.
action.notable.param.rule_title = Unsigned Image Loaded by LSASS
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, ImageLoaded, Signed, SHA1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_lsass_filter` 

[ESCU - Unsuccessful Netbackup backups - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search gives you the hosts where a backup was attempted and then failed. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search gives you the hosts where a backup was attempted and then failed.
action.escu.how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Monitor Backup Solution"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Unsuccessful Netbackup backups - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor Backup Solution"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter`

[ESCU - Web Fraud - Account Harvesting - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to identify the creation of multiple user accounts using the same email domain name. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is used to identify the creation of multiple user accounts using the same email domain name.
action.escu.how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons.  After we have the username and email domain, we look for numerous account creations per email domain.  Common data sources used for this detection are customized Apache logs or Splunk Stream.
action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment&#151;improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important.  Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search.  You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated.
action.escu.creation_date = 2018-10-08
action.escu.modification_date = 2018-10-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web Fraud - Account Harvesting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Web Fraud Detection"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Web Fraud - Account Harvesting - Rule
action.correlationsearch.annotations = {"analytic_story": ["Web Fraud Detection"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to identify the creation of multiple user accounts using the same email domain name.
action.notable.param.rule_title = Web Fraud - Account Harvesting
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?<SessionID>\w+)" | rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)" | search Username=* | rex field=Username "@(?<email_domain>.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter`

[ESCU - Web Fraud - Anomalous User Clickspeed - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session.
action.escu.how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.
action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior.
action.escu.creation_date = 2018-10-08
action.escu.modification_date = 2018-10-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web Fraud - Anomalous User Clickspeed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Web Fraud Detection"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Web Fraud - Anomalous User Clickspeed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Web Fraud Detection"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_content_type=text* | rex field=cookie "form_key=(?<session_id>\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter`

[ESCU - Web Fraud - Password Sharing Across Accounts - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to identify user accounts that share a common password. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is used to identify user accounts that share a common password.
action.escu.how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.
action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior.
action.escu.creation_date = 2018-10-08
action.escu.modification_date = 2018-10-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accounts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Web Fraud Detection"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Web Fraud - Password Sharing Across Accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Web Fraud Detection"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost*  | rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)" | rex field=form_data "login\[password\]=(?<Password>[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter`

[ESCU - Windows connhost exe started forcefully - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk.  
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. 
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This process should not be ran forcefully, we have not see any false positives for this detection
action.escu.creation_date = 2020-11-06
action.escu.modification_date = 2020-11-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows connhost exe started forcefully - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Windows connhost exe started forcefully - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. 
action.notable.param.rule_title = Windows connhost exe started forcefully
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter`

[ESCU - Windows hosts file modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for modifications to the hosts file on all Windows endpoints across your environment. 
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for modifications to the hosts file on all Windows endpoints across your environment.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
action.escu.known_false_positives = There may be legitimate reasons for system administrators to add entries to this file.
action.escu.creation_date = 2018-11-02
action.escu.modification_date = 2018-11-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows hosts file modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Host Redirection"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "field", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Windows hosts file modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Host Redirection"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for modifications to the hosts file on all Windows endpoints across your environment.
action.notable.param.rule_title = Windows hosts file modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem  by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter`

[ESCU - 3CX Supply Chain Attack Network Indicators - Rule]
action.escu = 0
action.escu.enabled = 1
description = The analytic provided below employs the Network_Resolution datamodel to detect domain indicators associated with the 3CX supply chain attack. By leveraging this query, you can efficiently conduct retrospective analysis of your data to uncover potential compromises.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1195.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The analytic provided below employs the Network_Resolution datamodel to detect domain indicators associated with the 3CX supply chain attack. By leveraging this query, you can efficiently conduct retrospective analysis of your data to uncover potential compromises.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information into the `Network Resolution` datamodel in the `DNS` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA''s are installed.
action.escu.known_false_positives = False positives will be present for accessing the 3cx[.]com website. Remove from the lookup as needed.
action.escu.creation_date = 2023-03-30
action.escu.modification_date = 2023-03-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - 3CX Supply Chain Attack Network Indicators - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["3CX Supply Chain Attack"]
action.risk = 1
action.risk.param._risk_message = Indicators related to 3CX supply chain attack have been identified on $src$.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 100}, {"threat_object_field": "query", "threat_object_type": "url string"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - 3CX Supply Chain Attack Network Indicators - Rule
action.correlationsearch.annotations = {"analytic_story": ["3CX Supply Chain Attack"], "cis20": ["CIS 13"], "confidence": 100, "cve": ["CVE-2023-29059"], "impact": 100, "mitre_attack": ["T1195.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The analytic provided below employs the Network_Resolution datamodel to detect domain indicators associated with the 3CX supply chain attack. By leveraging this query, you can efficiently conduct retrospective analysis of your data to uncover potential compromises.
action.notable.param.rule_title = 3CX Supply Chain Attack Network Indicators
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | lookup 3cx_ioc_domains domain as query OUTPUT Description isIOC | search isIOC=true | `3cx_supply_chain_attack_network_indicators_filter`

[ESCU - 7zip CommandLine To SMB Share Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious 7z process with commandline pointing to SMB network share. This technique was seen in CONTI LEAK tools where it use 7z to archive a sensitive files and place it in network share tmp folder. This search is a good hunting query that may give analyst a hint why specific user try to archive a file pointing to SMB user which is un usual.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious 7z process with commandline pointing to SMB network share. This technique was seen in CONTI LEAK tools where it use 7z to archive a sensitive files and place it in network share tmp folder. This search is a good hunting query that may give analyst a hint why specific user try to archive a file pointing to SMB user which is un usual.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-17
action.escu.modification_date = 2021-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - 7zip CommandLine To SMB Share Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - 7zip CommandLine To SMB Share Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name ="7z.exe" OR Processes.process_name = "7za.exe" OR Processes.original_file_name = "7z.exe" OR Processes.original_file_name =  "7za.exe") AND (Processes.process="*\\C$\\*" OR Processes.process="*\\Admin$\\*" OR Processes.process="*\\IPC$\\*") by Processes.original_file_name Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.parent_process_id Processes.process_id  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `7zip_commandline_to_smb_share_path_filter`

[ESCU - Access LSASS Memory for Dump Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the dumping of the LSASS process memory, which occurs during credential dumping attacks.The detection is made by using Sysmon logs, specifically EventCode 10, which is related to lsass.exe. This helps to search for indicators of LSASS memory dumping such as specific call traces to dbgcore.dll and dbghelp.dll. This detection is important because it prevents credential dumping attacks and the theft of sensitive information such as login credentials, which can be used to gain unauthorized access to systems and data. False positives might occur due to legitimate administrative tasks. Next steps include reviewing and investigating each case, given the high risk associated with potential credential dumping attacks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the dumping of the LSASS process memory, which occurs during credential dumping attacks.The detection is made by using Sysmon logs, specifically EventCode 10, which is related to lsass.exe. This helps to search for indicators of LSASS memory dumping such as specific call traces to dbgcore.dll and dbghelp.dll. This detection is important because it prevents credential dumping attacks and the theft of sensitive information such as login credentials, which can be used to gain unauthorized access to systems and data. False positives might occur due to legitimate administrative tasks. Next steps include reviewing and investigating each case, given the high risk associated with potential credential dumping attacks.
action.escu.how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
action.escu.creation_date = 2019-12-06
action.escu.modification_date = 2019-12-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Access LSASS Memory for Dump Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ injected into $TargetImage$ and was attempted dump LSASS on $dest$. Adversaries tend to do this when trying to accesss credential material stored in the process memory of the Local Security Authority Subsystem Service (LSASS).
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Access LSASS Memory for Dump Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the dumping of the LSASS process memory, which occurs during credential dumping attacks.The detection is made by using Sysmon logs, specifically EventCode 10, which is related to lsass.exe. This helps to search for indicators of LSASS memory dumping such as specific call traces to dbgcore.dll and dbghelp.dll. This detection is important because it prevents credential dumping attacks and the theft of sensitive information such as login credentials, which can be used to gain unauthorized access to systems and data. False positives might occur due to legitimate administrative tasks. Next steps include reviewing and investigating each case, given the high risk associated with potential credential dumping attacks.
action.notable.param.rule_title = Access LSASS Memory for Dump Creation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetImage, TargetProcessId, SourceImage, SourceProcessId  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` 

[ESCU - Account Discovery With Net App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Admin or power user may used this series of command.
action.escu.creation_date = 2023-01-04
action.escu.modification_date = 2023-01-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Account Discovery With Net App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trickbot", "IcedID"]
action.risk = 1
action.risk.param._risk_message = Suspicious $process_name$ usage detected on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 5}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Account Discovery With Net App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "IcedID"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network.
action.notable.param.rule_title = Account Discovery With Net App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND (Processes.process="* user *" OR  Processes.process="*config*" OR Processes.process="*view /all*") by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_filter`

[ESCU - Active Directory Lateral Movement Identified - Rule]
action.escu = 0
action.escu.enabled = 1
description = The primary objective of this correlation rule is to detect and alert on potential lateral movement activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Lateral Movement analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Lateral Movement analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The primary objective of this correlation rule is to detect and alert on potential lateral movement activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Lateral Movement analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Lateral Movement analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
action.escu.how_to_implement = Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
action.escu.known_false_positives = False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed.
action.escu.creation_date = 2023-04-19
action.escu.modification_date = 2023-04-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Active Directory Lateral Movement Identified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Lateral Movement"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Active Directory Lateral Movement Identified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The primary objective of this correlation rule is to detect and alert on potential lateral movement activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Lateral Movement analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Lateral Movement analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
action.notable.param.rule_title = RBA: Active Directory Lateral Movement Identified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active Directory Lateral Movement" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `active_directory_lateral_movement_identified_filter`

[ESCU - Active Directory Privilege Escalation Identified - Rule]
action.escu = 0
action.escu.enabled = 1
description = The primary objective of this correlation rule is to detect and alert on potential privilege escalation activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Privilege Escalation analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Privilege Escalation analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The primary objective of this correlation rule is to detect and alert on potential privilege escalation activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Privilege Escalation analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Privilege Escalation analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
action.escu.how_to_implement = Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
action.escu.known_false_positives = False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed.
action.escu.creation_date = 2023-05-23
action.escu.modification_date = 2023-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Active Directory Privilege Escalation Identified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Active Directory Privilege Escalation Identified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1484"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The primary objective of this correlation rule is to detect and alert on potential privilege escalation activities within an organization's Active Directory (AD) environment. By identifying multiple analytics associated with the Active Directory Privilege Escalation analytic story, security analysts can gain better insight into possible threats and respond accordingly to mitigate risks. The correlation rule will trigger an alert when multiple analytics from the Active Directory Privilege Escalation analytic story are detected within a specified time frame. The rule will generate an alert if a predetermined threshold of correlated analytics is reached within the specified time frame. This threshold can be customized to suit the needs and risk appetite of the organization.
action.notable.param.rule_title = RBA: Active Directory Privilege Escalation Identified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active Directory Privilege Escalation" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `active_directory_privilege_escalation_identified_filter`

[ESCU - Active Setup Registry Autostart - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification of the active setup registry for persistence and privilege escalation. This technique was seen in several malware (poisonIvy), adware and APT to gain persistence to the compromised machine upon boot up. This TTP is a good indicator to further check the process id that do the modification since modification of this registry is not commonly done. check the legitimacy of the file and process involve in this rules to check if it is a valid setup installer that creating or modifying this registry.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.014", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification of the active setup registry for persistence and privilege escalation. This technique was seen in several malware (poisonIvy), adware and APT to gain persistence to the compromised machine upon boot up. This TTP is a good indicator to further check the process id that do the modification since modification of this registry is not commonly done. check the legitimacy of the file and process involve in this rules to check if it is a valid setup installer that creating or modifying this registry.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = Active setup installer may add or modify this registry.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Active Setup Registry Autostart - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Windows Privilege Escalation", "Hermetic Wiper", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Active Setup Registry Autostart - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Windows Privilege Escalation", "Hermetic Wiper", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1547.014", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious modification of the active setup registry for persistence and privilege escalation. This technique was seen in several malware (poisonIvy), adware and APT to gain persistence to the compromised machine upon boot up. This TTP is a good indicator to further check the process id that do the modification since modification of this registry is not commonly done. check the legitimacy of the file and process involve in this rules to check if it is a valid setup installer that creating or modifying this registry.
action.notable.param.rule_title = Active Setup Registry Autostart
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name= "StubPath" Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Active Setup\\Installed Components*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `active_setup_registry_autostart_filter`

[ESCU - Add DefaultUser And Password In Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to detect a suspicious registry modification to implement auto admin logon to a host. This technique was seen in BlackMatter ransomware to automatically logon to the compromise host after  triggering a safemode boot to continue encrypting the whole network. This behavior is not a common practice and really a suspicious TTP or alert need to be consider if found within then network premise.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.002", "T1552"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to detect a suspicious registry modification to implement auto admin logon to a host. This technique was seen in BlackMatter ransomware to automatically logon to the compromise host after  triggering a safemode boot to continue encrypting the whole network. This behavior is not a common practice and really a suspicious TTP or alert need to be consider if found within then network premise.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-03-29
action.escu.modification_date = 2023-03-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Add DefaultUser And Password In Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackMatter Ransomware"]
action.risk = 1
action.risk.param._risk_message = modified registry key $registry_key_name$ with registry value $registry_value_name$ to prepare autoadminlogon
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Add DefaultUser And Password In Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackMatter Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1552.002", "T1552"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name= DefaultPassword OR Registry.registry_value_name= DefaultUserName) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `add_defaultuser_and_password_in_registry_filter`

[ESCU - Add or Set Windows Defender Exclusion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify a suspicious process command-line related to Windows Defender exclusion feature. This command is abused by adversaries, malware authors and red teams to bypass Windows Defender Antivirus products by excluding folder path, file path, process and extensions. From its real time or schedule scan to execute their malicious code. This is a good indicator for defense evasion and to look further for events after this behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify a suspicious process command-line related to Windows Defender exclusion feature. This command is abused by adversaries, malware authors and red teams to bypass Windows Defender Antivirus products by excluding folder path, file path, process and extensions. From its real time or schedule scan to execute their malicious code. This is a good indicator for defense evasion and to look further for events after this behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Admin or user may choose to use this windows features. Filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Add or Set Windows Defender Exclusion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["CISA AA22-320A", "AgentTesla", "Remcos", "Windows Defense Evasion Tactics", "Data Destruction", "WhisperGate"]
action.risk = 1
action.risk.param._risk_message = exclusion command $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Add or Set Windows Defender Exclusion - Rule
action.correlationsearch.annotations = {"analytic_story": ["CISA AA22-320A", "AgentTesla", "Remcos", "Windows Defense Evasion Tactics", "Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify a suspicious process command-line related to Windows Defender exclusion feature. This command is abused by adversaries, malware authors and red teams to bypass Windows Defender Antivirus products by excluding folder path, file path, process and extensions. From its real time or schedule scan to execute their malicious code. This is a good indicator for defense evasion and to look further for events after this behavior.
action.notable.param.rule_title = Add or Set Windows Defender Exclusion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*Add-MpPreference *" OR Processes.process = "*Set-MpPreference *") AND Processes.process="*-exclusion*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `add_or_set_windows_defender_exclusion_filter`

[ESCU - AdsiSearcher Account Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - AdsiSearcher Account Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Data Destruction", "Active Directory Discovery", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = Powershell process having commandline "AdsiSearcher" used for user enumeration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - AdsiSearcher Account Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Active Directory Discovery", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = AdsiSearcher Account Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*objectcategory=user*" ScriptBlockText = "*.findAll()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Computer ScriptBlockText UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adsisearcher_account_discovery_filter`

[ESCU - Allow File And Printing Sharing In Firewall - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious modification of firewall to allow file and printer sharing. This technique was seen in ransomware to be able to discover more machine connected to the compromised host to encrypt more files
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious modification of firewall to allow file and printer sharing. This technique was seen in ransomware to be able to discover more machine connected to the compromised host to encrypt more files
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network admin may modify this firewall feature that may cause this rule to be triggered.
action.escu.creation_date = 2021-06-23
action.escu.modification_date = 2021-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Allow File And Printing Sharing In Firewall - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Allow File And Printing Sharing In Firewall - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious modification of firewall to allow file and printer sharing. This technique was seen in ransomware to be able to discover more machine connected to the compromised host to encrypt more files
action.notable.param.rule_title = Allow File And Printing Sharing In Firewall
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*"  Processes.process="*enable=Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_file_and_printing_sharing_in_firewall_filter`

[ESCU - Allow Inbound Traffic By Firewall Rule Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a potential suspicious modification of firewall rule registry allowing inbound traffic in specific port with public profile. This technique was identified when an adversary wants to grant remote access to a machine by allowing the traffic in a firewall rule.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects a potential suspicious modification of firewall rule registry allowing inbound traffic in specific port with public profile. This technique was identified when an adversary wants to grant remote access to a machine by allowing the traffic in a firewall rule.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered.
action.escu.creation_date = 2023-03-29
action.escu.modification_date = 2023-03-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Allow Inbound Traffic By Firewall Rule Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Windows Registry Abuse", "Azorult", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Suspicious firewall allow rule modifications were detected via the registry on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Allow Inbound Traffic By Firewall Rule Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Windows Registry Abuse", "Azorult", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a potential suspicious modification of firewall rule registry allowing inbound traffic in specific port with public profile. This technique was identified when an adversary wants to grant remote access to a machine by allowing the traffic in a firewall rule.
action.notable.param.rule_title = Allow Inbound Traffic By Firewall Rule Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_data = "*|Action=Allow|*" Registry.registry_value_data = "*|Dir=In|*"  Registry.registry_value_data = "*|LPort=*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid  Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_inbound_traffic_by_firewall_rule_registry_filter`

[ESCU - Allow Inbound Traffic In Firewall Rule - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell command to allow inbound traffic inbound to a specific local port within the public profile. This technique was seen in some attacker want to have a remote access to a machine by allowing the traffic in firewall rule.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell command to allow inbound traffic inbound to a specific local port within the public profile. This technique was seen in some attacker want to have a remote access to a machine by allowing the traffic in firewall rule.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
action.escu.known_false_positives = administrator may allow inbound traffic in certain network or machine.
action.escu.creation_date = 2021-05-19
action.escu.modification_date = 2021-05-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Allow Inbound Traffic In Firewall Rule - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"]
action.risk = 1
action.risk.param._risk_message = Suspicious firewall modification detected on endpoint $ComputerName$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 3}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 3}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Allow Inbound Traffic In Firewall Rule - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch"], "cis20": ["CIS 10"], "confidence": 30, "impact": 10, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious PowerShell command to allow inbound traffic inbound to a specific local port within the public profile. This technique was seen in some attacker want to have a remote access to a machine by allowing the traffic in firewall rule.
action.notable.param.rule_title = Allow Inbound Traffic In Firewall Rule
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 Message = "*firewall*" Message = "*Inbound*" Message = "*Allow*"  Message = "*-LocalPort*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_inbound_traffic_in_firewall_rule_filter`

[ESCU - Allow Network Discovery In Firewall - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious modification to the firewall to allow network discovery on a machine. This technique was seen in couple of ransomware (revil, reddot) to discover other machine connected to the compromised host to encrypt more files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious modification to the firewall to allow network discovery on a machine. This technique was seen in couple of ransomware (revil, reddot) to discover other machine connected to the compromised host to encrypt more files.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network admin may modify this firewall feature that may cause this rule to be triggered.
action.escu.creation_date = 2021-06-23
action.escu.modification_date = 2021-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Allow Network Discovery In Firewall - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Revil Ransomware", "BlackByte Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Allow Network Discovery In Firewall - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware", "BlackByte Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious modification to the firewall to allow network discovery on a machine. This technique was seen in couple of ransomware (revil, reddot) to discover other machine connected to the compromised host to encrypt more files.
action.notable.param.rule_title = Allow Network Discovery In Firewall
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= "*firewall*" Processes.process= "*group=\"Network Discovery\"*"  Processes.process="*enable*" Processes.process="*Yes*" by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_network_discovery_in_firewall_filter`

[ESCU - Allow Operation with Consent Admin - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a potential privilege escalation attempt to perform malicious task. This registry modification is designed to allow the `Consent Admin` to perform an operation that requires elevation without consent or credentials. We also found this in some attacker to gain privilege escalation to the compromise machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a potential privilege escalation attempt to perform malicious task. This registry modification is designed to allow the `Consent Admin` to perform an operation that requires elevation without consent or credentials. We also found this in some attacker to gain privilege escalation to the compromise machine.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-03-29
action.escu.modification_date = 2023-03-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Allow Operation with Consent Admin - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Registry Abuse", "Azorult"]
action.risk = 1
action.risk.param._risk_message = Suspicious registry modification was performed on endpoint $dest$ by user $user$. This behavior is indicative of privilege escalation.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Allow Operation with Consent Admin - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Registry Abuse", "Azorult"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a potential privilege escalation attempt to perform malicious task. This registry modification is designed to allow the `Consent Admin` to perform an operation that requires elevation without consent or credentials. We also found this in some attacker to gain privilege escalation to the compromise machine.
action.notable.param.rule_title = Allow Operation with Consent Admin
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" Registry.registry_value_name = ConsentPromptBehaviorAdmin Registry.registry_value_data = "0x00000000") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_operation_with_consent_admin_filter`

[ESCU - Anomalous usage of 7zip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies a 7z.exe spawned from `Rundll32.exe` or `Dllhost.exe`. It is assumed that the adversary has brought in `7z.exe` and `7z.dll`. It has been observed where an adversary will rename `7z.exe`. Additional coverage may be required to identify the behavior of renamed instances of `7z.exe`. During triage, identify the source of injection into `Rundll32.exe` or `Dllhost.exe`. Capture any files written to disk and analyze as needed. Review parallel processes for additional behaviors. Typically, archiving files will result in exfiltration.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies a 7z.exe spawned from `Rundll32.exe` or `Dllhost.exe`. It is assumed that the adversary has brought in `7z.exe` and `7z.dll`. It has been observed where an adversary will rename `7z.exe`. Additional coverage may be required to identify the behavior of renamed instances of `7z.exe`. During triage, identify the source of injection into `Rundll32.exe` or `Dllhost.exe`. Capture any files written to disk and analyze as needed. Review parallel processes for additional behaviors. Typically, archiving files will result in exfiltration.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as this behavior is not normal for `rundll32.exe` or `dllhost.exe` to spawn and run 7zip.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Anomalous usage of 7zip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NOBELIUM Group", "BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior is indicative of suspicious loading of 7zip.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Anomalous usage of 7zip - Rule
action.correlationsearch.annotations = {"analytic_story": ["NOBELIUM Group", "BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `anomalous_usage_of_7zip_filter`

[ESCU - Any Powershell DownloadFile - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of PowerShell downloading a file using `DownloadFile` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001", "T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of PowerShell downloading a file using `DownloadFile` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Any Powershell DownloadFile - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT", "Ingress Tool Transfer", "Hermetic Wiper", "Malicious PowerShell", "Data Destruction", "Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior identifies the use of DownloadFile within PowerShell.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Any Powershell DownloadFile - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT", "Ingress Tool Transfer", "Hermetic Wiper", "Malicious PowerShell", "Data Destruction", "Log4Shell CVE-2021-44228"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-44228"], "impact": 80, "mitre_attack": ["T1059", "T1059.001", "T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of PowerShell downloading a file using `DownloadFile` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.
action.notable.param.rule_title = Any Powershell DownloadFile
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*DownloadFile* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `any_powershell_downloadfile_filter`

[ESCU - Any Powershell DownloadString - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of PowerShell downloading a file using `DownloadString` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001", "T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of PowerShell downloading a file using `DownloadString` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Any Powershell DownloadString - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Winter Vivern", "Ingress Tool Transfer", "Hermetic Wiper", "Malicious PowerShell", "HAFNIUM Group", "Data Destruction", "IcedID"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior identifies the use of DownloadString within PowerShell.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Any Powershell DownloadString - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern", "Ingress Tool Transfer", "Hermetic Wiper", "Malicious PowerShell", "HAFNIUM Group", "Data Destruction", "IcedID"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1059", "T1059.001", "T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of PowerShell downloading a file using `DownloadString` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.
action.notable.param.rule_title = Any Powershell DownloadString
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*.DownloadString*  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `any_powershell_downloadstring_filter`

[ESCU - Attacker Tools On Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the use of tools that are commonly exploited by cybercriminals since these tools are usually associated with malicious activities such as unauthorized access, network scanning, or data exfiltration and pose a significant threat to an organization's security infrastructure. It also provides enhanced visibility into potential security threats and helps to proactively detect and respond to mitigate the risks associated with cybercriminal activities. This detection is made by examining the process activity on the host, specifically focusing on processes that are known to be associated with attacker tool names. This detection is important because it acts as an early warning system for potential security incidents that allows you to respond to security incidents promptly. False positives might occur due to legitimate administrative activities that can resemble malicious actions. You must develop a comprehensive understanding of typical endpoint activities and behaviors within the organization to accurately interpret and respond to the alerts generated by this analytic. This ensures a proper balance between precision and minimizing false positives.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036.005", "T1036", "T1003", "T1595"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the use of tools that are commonly exploited by cybercriminals since these tools are usually associated with malicious activities such as unauthorized access, network scanning, or data exfiltration and pose a significant threat to an organization's security infrastructure. It also provides enhanced visibility into potential security threats and helps to proactively detect and respond to mitigate the risks associated with cybercriminal activities. This detection is made by examining the process activity on the host, specifically focusing on processes that are known to be associated with attacker tool names. This detection is important because it acts as an early warning system for potential security incidents that allows you to respond to security incidents promptly. False positives might occur due to legitimate administrative activities that can resemble malicious actions. You must develop a comprehensive understanding of typical endpoint activities and behaviors within the organization to accurately interpret and respond to the alerts generated by this analytic. This ensures a proper balance between precision and minimizing false positives.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some administrator activity can be potentially triggered, please add those users to the filter macro.
action.escu.creation_date = 2021-11-04
action.escu.modification_date = 2021-11-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Attacker Tools On Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Monitor for Unauthorized Software", "XMRig", "SamSam Ransomware", "Unusual Processes", "CISA AA22-264A"]
action.risk = 1
action.risk.param._risk_message = An attacker tool $process_name$,listed in attacker_tools.csv is executed on host $dest$ by User $user$. This process $process_name$ is known to do- $description$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process", "risk_object_type": "other", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Attacker Tools On Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor for Unauthorized Software", "XMRig", "SamSam Ransomware", "Unusual Processes", "CISA AA22-264A"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1036.005", "T1036", "T1003", "T1595"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the use of tools that are commonly exploited by cybercriminals since these tools are usually associated with malicious activities such as unauthorized access, network scanning, or data exfiltration and pose a significant threat to an organization's security infrastructure. It also provides enhanced visibility into potential security threats and helps to proactively detect and respond to mitigate the risks associated with cybercriminal activities. This detection is made by examining the process activity on the host, specifically focusing on processes that are known to be associated with attacker tool names. This detection is important because it acts as an early warning system for potential security incidents that allows you to respond to security incidents promptly. False positives might occur due to legitimate administrative activities that can resemble malicious actions. You must develop a comprehensive understanding of typical endpoint activities and behaviors within the organization to accurately interpret and respond to the alerts generated by this analytic. This ensures a proper balance between precision and minimizing false positives.
action.notable.param.rule_title = Attacker Tools On Endpoint
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown by Processes.dest Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup attacker_tools attacker_tool_names AS process_name OUTPUT description | search description !=false| `attacker_tools_on_endpoint_filter`

[ESCU - Attempt To Add Certificate To Untrusted Store - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects whether a process is attempting to add a certificate to the untrusted certificate store, which might result in security tools being disabled. The detection is made by focusing on process activities and command-line arguments that are related to the 'certutil -addstore' command. This detection is important because it helps to identify attackers who might add a certificate to the untrusted certificate store to disable security tools and gain unauthorized access to a system. False positives might occur since legitimate reasons might exist for a process to add a certificate to the untrusted certificate store, such as system administration tasks. Next steps include conducting an extensive triage and investigation prior to taking any action. Additionally, you must understand the importance of trust and its subversion in system security.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1553.004", "T1553"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects whether a process is attempting to add a certificate to the untrusted certificate store, which might result in security tools being disabled. The detection is made by focusing on process activities and command-line arguments that are related to the 'certutil -addstore' command. This detection is important because it helps to identify attackers who might add a certificate to the untrusted certificate store to disable security tools and gain unauthorized access to a system. False positives might occur since legitimate reasons might exist for a process to add a certificate to the untrusted certificate store, such as system administration tasks. Next steps include conducting an extensive triage and investigation prior to taking any action. Additionally, you must understand the importance of trust and its subversion in system security.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted Store - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Disabling Security Tools"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified attempting to add a certificate to the store on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Attempt To Add Certificate To Untrusted Store - Rule
action.correlationsearch.annotations = {"analytic_story": ["Disabling Security Tools"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1553.004", "T1553"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects whether a process is attempting to add a certificate to the untrusted certificate store, which might result in security tools being disabled. The detection is made by focusing on process activities and command-line arguments that are related to the 'certutil -addstore' command. This detection is important because it helps to identify attackers who might add a certificate to the untrusted certificate store to disable security tools and gain unauthorized access to a system. False positives might occur since legitimate reasons might exist for a process to add a certificate to the untrusted certificate store, such as system administration tasks. Next steps include conducting an extensive triage and investigation prior to taking any action. Additionally, you must understand the importance of trust and its subversion in system security.
action.notable.param.rule_title = Attempt To Add Certificate To Untrusted Store
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*-addstore*) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter`

[ESCU - Attempt To Stop Security Service - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects attempts to stop security-related services on the endpoint and helps to mitigate potential threats earlier, thereby minimizing the impact on the organization's security. The detection is made by using a Splunk query that searches for processes that involve the "sc.exe" command and include the phrase "stop" in their command. The query collects information such as the process name, process ID, parent process, user, destination, and timestamps. The detection is important because attempts to stop security-related services can indicate malicious activity or an attacker's attempt to disable security measures. This can impact the organization's security posture and can lead to the compromise of the endpoint and potentially the entire network. Disabling security services can allow attackers to gain unauthorized access, exfiltrate sensitive data, or launch further attacks, such as malware installation or privilege escalation. False positives might occur since there might be legitimate reasons for stopping these services in certain situations. Therefore, you must exercise caution and consider the context of the activity before taking any action. Next steps include reviewing the identified process and its associated details. You must also investigate any on-disk artifacts related to the process and review concurrent processes to determine the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects attempts to stop security-related services on the endpoint and helps to mitigate potential threats earlier, thereby minimizing the impact on the organization's security. The detection is made by using a Splunk query that searches for processes that involve the "sc.exe" command and include the phrase "stop" in their command. The query collects information such as the process name, process ID, parent process, user, destination, and timestamps. The detection is important because attempts to stop security-related services can indicate malicious activity or an attacker's attempt to disable security measures. This can impact the organization's security posture and can lead to the compromise of the endpoint and potentially the entire network. Disabling security services can allow attackers to gain unauthorized access, exfiltrate sensitive data, or launch further attacks, such as malware installation or privilege escalation. False positives might occur since there might be legitimate reasons for stopping these services in certain situations. Therefore, you must exercise caution and consider the context of the activity before taking any action. Next steps include reviewing the identified process and its associated details. You must also investigate any on-disk artifacts related to the process and review concurrent processes to determine the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified. Attempts to disable security-related services should be identified and understood.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["WhisperGate", "Graceful Wipe Out Attack", "Disabling Security Tools", "Data Destruction", "Azorult", "Trickbot"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 20}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 20}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Attempt To Stop Security Service - Rule
action.correlationsearch.annotations = {"analytic_story": ["WhisperGate", "Graceful Wipe Out Attack", "Disabling Security Tools", "Data Destruction", "Azorult", "Trickbot"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects attempts to stop security-related services on the endpoint and helps to mitigate potential threats earlier, thereby minimizing the impact on the organization's security. The detection is made by using a Splunk query that searches for processes that involve the "sc.exe" command and include the phrase "stop" in their command. The query collects information such as the process name, process ID, parent process, user, destination, and timestamps. The detection is important because attempts to stop security-related services can indicate malicious activity or an attacker's attempt to disable security measures. This can impact the organization's security posture and can lead to the compromise of the endpoint and potentially the entire network. Disabling security services can allow attackers to gain unauthorized access, exfiltrate sensitive data, or launch further attacks, such as malware installation or privilege escalation. False positives might occur since there might be legitimate reasons for stopping these services in certain situations. Therefore, you must exercise caution and consider the context of the activity before taking any action. Next steps include reviewing the identified process and its associated details. You must also investigate any on-disk artifacts related to the process and review concurrent processes to determine the source of the attack.
action.notable.param.rule_title = Attempt To Stop Security Service
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name = sc.exe Processes.process="* stop *" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter`

[ESCU - Attempted Credential Dump From Registry via Reg exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Attempted Credential Dump From Registry via Reg exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Industroyer2", "Windows Registry Abuse", "Credential Dumping", "DarkSide Ransomware", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export the registry keys.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Attempted Credential Dump From Registry via Reg exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Industroyer2", "Windows Registry Abuse", "Credential Dumping", "DarkSide Ransomware", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.
action.notable.param.rule_title = Attempted Credential Dump From Registry via Reg exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg* OR Processes.process_name=cmd* Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name  Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter`

[ESCU - Auto Admin Logon Registry Entry - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to detect a suspicious registry modification to implement auto admin logon to a host. This technique was seen in BlackMatter ransomware to automatically logon to the compromise host after  triggering a safemode boot to continue encrypting the whole network. This behavior is not a common practice and really a suspicious TTP or alert need to be consider if found within then network premise.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.002", "T1552"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to detect a suspicious registry modification to implement auto admin logon to a host. This technique was seen in BlackMatter ransomware to automatically logon to the compromise host after  triggering a safemode boot to continue encrypting the whole network. This behavior is not a common practice and really a suspicious TTP or alert need to be consider if found within then network premise.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-11
action.escu.modification_date = 2023-04-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Auto Admin Logon Registry Entry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackMatter Ransomware", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = modified registry key $registry_key_name$ with registry value $registry_value_name$ to prepare autoadminlogon
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Auto Admin Logon Registry Entry - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackMatter Ransomware", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1552.002", "T1552"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to detect a suspicious registry modification to implement auto admin logon to a host. This technique was seen in BlackMatter ransomware to automatically logon to the compromise host after  triggering a safemode boot to continue encrypting the whole network. This behavior is not a common practice and really a suspicious TTP or alert need to be consider if found within then network premise.
action.notable.param.rule_title = Auto Admin Logon Registry Entry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name=AutoAdminLogon AND Registry.registry_value_data=1) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `auto_admin_logon_registry_entry_filter`

[ESCU - Batch File Write to System32 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for a batch file (.bat) written to the Windows system directory tree.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for a batch file (.bat) written to the Windows system directory tree.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary.
action.escu.creation_date = 2023-4-11
action.escu.modification_date = 2023-4-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware"]
action.risk = 1
action.risk.param._risk_message = A file - $file_name$ was written to system32 has occurred on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "file_name", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Batch File Write to System32 - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1204", "T1204.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for a batch file (.bat) written to the Windows system directory tree.
action.notable.param.rule_title = Batch File Write to System32
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.process_guid Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\system32\\*", "*\\syswow64\\*") Filesystem.file_name="*.bat" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)`] | table dest user file_create_time, file_name, file_path, process_name, firstTime, lastTime | dedup file_create_time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `batch_file_write_to_system32_filter`

[ESCU - Bcdedit Command Back To Normal Mode Boot - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious bcdedit commandline to configure the host from safe mode back to normal boot configuration. This technique was seen in blackMatter ransomware where it force the compromised host to boot in safe mode to continue its encryption and bring back to normal boot using bcdedit deletevalue command. This TTP can be a good alert for host that booted from safe mode forcefully since it need to modify the boot configuration to bring it back to normal.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious bcdedit commandline to configure the host from safe mode back to normal boot configuration. This technique was seen in blackMatter ransomware where it force the compromised host to boot in safe mode to continue its encryption and bring back to normal boot using bcdedit deletevalue command. This TTP can be a good alert for host that booted from safe mode forcefully since it need to modify the boot configuration to bring it back to normal.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-06
action.escu.modification_date = 2021-09-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Bcdedit Command Back To Normal Mode Boot - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackMatter Ransomware"]
action.risk = 1
action.risk.param._risk_message = bcdedit process with commandline $process$ to bring back to normal boot configuration the $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Bcdedit Command Back To Normal Mode Boot - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackMatter Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious bcdedit commandline to configure the host from safe mode back to normal boot configuration. This technique was seen in blackMatter ransomware where it force the compromised host to boot in safe mode to continue its encryption and bring back to normal boot using bcdedit deletevalue command. This TTP can be a good alert for host that booted from safe mode forcefully since it need to modify the boot configuration to bring it back to normal.
action.notable.param.rule_title = Bcdedit Command Back To Normal Mode Boot
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*/deletevalue*" Processes.process="*{current}*"  Processes.process="*safeboot*" by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_command_back_to_normal_mode_boot_filter`

[ESCU - BCDEdit Failure Recovery Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may modify the boot configuration.
action.escu.creation_date = 2020-12-21
action.escu.modification_date = 2020-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - BCDEdit Failure Recovery Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting disable the ability to recover the endpoint.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - BCDEdit Failure Recovery Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery.
action.notable.param.rule_title = BCDEdit Failure Recovery Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter`

[ESCU - BITS Job Persistence - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` scheduling a BITS job to persist on an endpoint. The query identifies the parameters used to create, resume or add a file to a BITS job. Typically seen combined in a oneliner or ran in sequence. If identified, review the BITS job created and capture any files written to disk. It is possible for BITS to be used to upload files and this may require further network data analysis to identify. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1197"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` scheduling a BITS job to persist on an endpoint. The query identifies the parameters used to create, resume or add a file to a BITS job. Typically seen combined in a oneliner or ran in sequence. If identified, review the BITS job created and capture any files written to disk. It is possible for BITS to be used to upload files and this may require further network data analysis to identify. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives will be present. Typically, applications will use `BitsAdmin.exe`. Any filtering should be done based on command-line arguments (legitimate applications) or parent process.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - BITS Job Persistence - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BITS Jobs", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to persist using BITS.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - BITS Job Persistence - Rule
action.correlationsearch.annotations = {"analytic_story": ["BITS Jobs", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1197"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` scheduling a BITS job to persist on an endpoint. The query identifies the parameters used to create, resume or add a file to a BITS job. Typically seen combined in a oneliner or ran in sequence. If identified, review the BITS job created and capture any files written to disk. It is possible for BITS to be used to upload files and this may require further network data analysis to identify. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.
action.notable.param.rule_title = BITS Job Persistence
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*, *setcustomheaders*, *resume* ) by Processes.dest Processes.user Processes.original_file_name Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bits_job_persistence_filter`

[ESCU - BITSAdmin Download File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1197", "T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives, however it may be required to filter based on parent process name or network connection.
action.escu.creation_date = 2022-11-29
action.escu.modification_date = 2022-11-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - BITSAdmin Download File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ingress Tool Transfer", "BITS Jobs", "DarkSide Ransomware", "Living Off The Land", "Flax Typhoon"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - BITSAdmin Download File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "BITS Jobs", "DarkSide Ransomware", "Living Off The Land", "Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1197", "T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.
action.notable.param.rule_title = BITSAdmin Download File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process IN ("*transfer*", "*addfile*") by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bitsadmin_download_file_filter`

[ESCU - CertUtil Download With URLCache and Split Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = Certutil.exe may download a file from a remote destination using `-urlcache`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. However, it is uncommon for `certutil.exe` to write files to world writeable paths.\ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Certutil.exe may download a file from a remote destination using `-urlcache`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. However, it is uncommon for `certutil.exe` to write files to world writeable paths.\ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
action.escu.creation_date = 2022-02-03
action.escu.modification_date = 2022-02-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - CertUtil Download With URLCache and Split Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ingress Tool Transfer", "DarkSide Ransomware", "Living Off The Land", "ProxyNotShell", "CISA AA22-277A", "Flax Typhoon", "Forest Blizzard"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CertUtil Download With URLCache and Split Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "DarkSide Ransomware", "Living Off The Land", "ProxyNotShell", "CISA AA22-277A", "Flax Typhoon", "Forest Blizzard"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Certutil.exe may download a file from a remote destination using `-urlcache`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. However, it is uncommon for `certutil.exe` to write files to world writeable paths.\ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question.
action.notable.param.rule_title = CertUtil Download With URLCache and Split Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*urlcache* Processes.process=*split*) OR Processes.process=*urlcache* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_download_with_urlcache_and_split_arguments_filter`

[ESCU - CertUtil Download With VerifyCtl and Split Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. 
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
action.escu.creation_date = 2022-02-03
action.escu.modification_date = 2022-02-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - CertUtil Download With VerifyCtl and Split Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ingress Tool Transfer", "DarkSide Ransomware", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CertUtil Download With VerifyCtl and Split Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "DarkSide Ransomware", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. 
action.notable.param.rule_title = CertUtil Download With VerifyCtl and Split Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*verifyctl* Processes.process=*split*) OR Processes.process=*verifyctl* by Processes.dest Processes.user Processes.original_file_name Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_download_with_verifyctl_and_split_arguments_filter`

[ESCU - Certutil exe certificate extraction - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services.
action.escu.creation_date = 2022-07-15
action.escu.modification_date = 2022-07-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Certutil exe certificate extraction - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques", "Cloud Federated Credential Abuse", "Living Off The Land", "Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting export a certificate.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 63}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Certutil exe certificate extraction - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Cloud Federated Credential Abuse", "Living Off The Land", "Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS.
action.notable.param.rule_title = Certutil exe certificate extraction
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "*-exportPFX*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter`

[ESCU - CertUtil With Decode Argument - Rule]
action.escu = 0
action.escu.enabled = 1
description = CertUtil.exe may be used to `encode` and `decode` a file, including PE and script code. Encoding will convert a file to base64 with `-----BEGIN CERTIFICATE-----` and `-----END CERTIFICATE-----` tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - `encodehex` and `decodehex`. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1140"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = CertUtil.exe may be used to `encode` and `decode` a file, including PE and script code. Encoding will convert a file to base64 with `-----BEGIN CERTIFICATE-----` and `-----END CERTIFICATE-----` tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - `encodehex` and `decodehex`. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Typically seen used to `encode` files, but it is possible to see legitimate use of `decode`. Filter based on parent-child relationship, file paths, endpoint or user.
action.escu.creation_date = 2021-03-23
action.escu.modification_date = 2021-03-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - CertUtil With Decode Argument - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Deobfuscate-Decode Files or Information", "Living Off The Land", "Forest Blizzard"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to decode a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CertUtil With Decode Argument - Rule
action.correlationsearch.annotations = {"analytic_story": ["Deobfuscate-Decode Files or Information", "Living Off The Land", "Forest Blizzard"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1140"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = CertUtil.exe may be used to `encode` and `decode` a file, including PE and script code. Encoding will convert a file to base64 with `-----BEGIN CERTIFICATE-----` and `-----END CERTIFICATE-----` tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - `encodehex` and `decodehex`. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis.
action.notable.param.rule_title = CertUtil With Decode Argument
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process=*decode* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_with_decode_argument_filter`

[ESCU - Change Default File Association - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect suspicious registry modification to change the default file association of windows to malicious payload. This technique was seen in some APT where it modify the default process to run file association, like .txt to notepad.exe. Instead notepad.exe it will point to a Script or other payload that will load malicious commands to the compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.001", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect suspicious registry modification to change the default file association of windows to malicious payload. This technique was seen in some APT where it modify the default process to run file association, like .txt to notepad.exe. Instead notepad.exe it will point to a Script or other payload that will load malicious commands to the compromised host.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Change Default File Association - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hermetic Wiper", "Windows Registry Abuse", "Prestige Ransomware", "Windows Privilege Escalation", "Windows Persistence Techniques", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $Registry.registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Change Default File Association - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Windows Registry Abuse", "Prestige Ransomware", "Windows Privilege Escalation", "Windows Persistence Techniques", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1546.001", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is developed to detect suspicious registry modification to change the default file association of windows to malicious payload. This technique was seen in some APT where it modify the default process to run file association, like .txt to notepad.exe. Instead notepad.exe it will point to a Script or other payload that will load malicious commands to the compromised host.
action.notable.param.rule_title = Change Default File Association
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\shell\\open\\command\\*" Registry.registry_path = "*HKCR\\*" by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `change_default_file_association_filter`

[ESCU - Change To Safe Mode With Network Config - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious bcdedit commandline to configure the host to boot in safe mode with network config. This technique was seen in blackMatter ransomware where it force the compromised host to boot in safe mode to continue its encryption and bring back to normal boot using bcdedit deletevalue command. This TTP can be a good alert for host that booted from safe mode forcefully since it need to modify the boot configuration to bring it back to normal.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious bcdedit commandline to configure the host to boot in safe mode with network config. This technique was seen in blackMatter ransomware where it force the compromised host to boot in safe mode to continue its encryption and bring back to normal boot using bcdedit deletevalue command. This TTP can be a good alert for host that booted from safe mode forcefully since it need to modify the boot configuration to bring it back to normal.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-06
action.escu.modification_date = 2021-09-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Change To Safe Mode With Network Config - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackMatter Ransomware"]
action.risk = 1
action.risk.param._risk_message = bcdedit process with commandline $process$ to force safemode boot the $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Change To Safe Mode With Network Config - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackMatter Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious bcdedit commandline to configure the host to boot in safe mode with network config. This technique was seen in blackMatter ransomware where it force the compromised host to boot in safe mode to continue its encryption and bring back to normal boot using bcdedit deletevalue command. This TTP can be a good alert for host that booted from safe mode forcefully since it need to modify the boot configuration to bring it back to normal.
action.notable.param.rule_title = Change To Safe Mode With Network Config
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*/set*" Processes.process="*{current}*"  Processes.process="*safeboot*" Processes.process="*network*" by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `change_to_safe_mode_with_network_config_filter`

[ESCU - CHCP Command Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect execution of chcp.exe application. this utility is used to change the active code page of the console. This technique was seen in icedid malware to know the locale region/language/country of the compromise host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect execution of chcp.exe application. this utility is used to change the active code page of the console. This technique was seen in icedid malware to know the locale region/language/country of the compromise host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = other tools or script may used this to change code page to UTF-* or others
action.escu.creation_date = 2021-07-27
action.escu.modification_date = 2021-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - CHCP Command Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Azorult", "Forest Blizzard"]
action.risk = 1
action.risk.param._risk_message = parent process $parent_process_name$ spawning chcp process $process_name$ with parent command line $parent_process$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CHCP Command Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Azorult", "Forest Blizzard"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect execution of chcp.exe application. this utility is used to change the active code page of the console. This technique was seen in icedid malware to know the locale region/language/country of the compromise host.
action.notable.param.rule_title = CHCP Command Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=chcp.com Processes.parent_process_name = cmd.exe (Processes.parent_process=*/c* OR Processes.parent_process=*/k*) by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `chcp_command_execution_filter`

[ESCU - Check Elevated CMD using whoami - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious whoami execution to check if the cmd or shell instance process is with elevated privileges. This technique was seen in FIN7 js implant where it execute this as part of its data collection to the infected machine to check if the running shell cmd process is elevated or not. This TTP is really a good alert for known attacker that recon on the targetted host. This command is not so commonly executed by a normal user or even an admin to check if a process is elevated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious whoami execution to check if the cmd or shell instance process is with elevated privileges. This technique was seen in FIN7 js implant where it execute this as part of its data collection to the infected machine to check if the running shell cmd process is elevated or not. This TTP is really a good alert for known attacker that recon on the targetted host. This command is not so commonly executed by a normal user or even an admin to check if a process is elevated.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-15
action.escu.modification_date = 2021-09-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Check Elevated CMD using whoami - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["FIN7"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Check Elevated CMD using whoami - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1033"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious whoami execution to check if the cmd or shell instance process is with elevated privileges. This technique was seen in FIN7 js implant where it execute this as part of its data collection to the infected machine to check if the running shell cmd process is elevated or not. This TTP is really a good alert for known attacker that recon on the targetted host. This command is not so commonly executed by a normal user or even an admin to check if a process is elevated.
action.notable.param.rule_title = Check Elevated CMD using whoami
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*whoami*" Processes.process = "*/group*" Processes.process = "* find *" Processes.process = "*12288*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `check_elevated_cmd_using_whoami_filter`

[ESCU - Child Processes of Spoolsv exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Child Processes of Spoolsv exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Child Processes of Spoolsv exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2018-8440"], "impact": 50, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.
action.notable.param.rule_title = Child Processes of Spoolsv exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` 

[ESCU - Clear Unallocated Sector Using Cipher App - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to detect execution of `cipher.exe` to clear the unallocated sectors of a specific disk. This technique was seen in some ransomware to make it impossible to forensically recover deleted files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070.004", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to detect execution of `cipher.exe` to clear the unallocated sectors of a specific disk. This technique was seen in some ransomware to make it impossible to forensically recover deleted files.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = administrator may execute this app to manage disk
action.escu.creation_date = 2021-06-10
action.escu.modification_date = 2021-06-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Clear Unallocated Sector Using Cipher App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to clear the unallocated sectors of a specific disk.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Clear Unallocated Sector Using Cipher App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1070.004", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to detect execution of `cipher.exe` to clear the unallocated sectors of a specific disk. This technique was seen in some ransomware to make it impossible to forensically recover deleted files.
action.notable.param.rule_title = Clear Unallocated Sector Using Cipher App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe" Processes.process = "*/w:*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clear_unallocated_sector_using_cipher_app_filter`

[ESCU - Clop Common Exec Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Operators can execute third party tools using these parameters.
action.escu.creation_date = 2023-03-17
action.escu.modification_date = 2023-03-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Clop Common Exec Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting using arguments to execute its main code or feature of its code related to Clop ransomware.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 100}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Clop Common Exec Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1204"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly.
action.notable.param.rule_title = Clop Common Exec Parameter
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter`

[ESCU - Clop Ransomware Known Service Name - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-03-17
action.escu.modification_date = 2021-03-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Clop Ransomware Known Service Name - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of a known Clop Ransomware Service Name detected on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Clop Ransomware Known Service Name - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry.
action.notable.param.rule_title = Clop Ransomware Known Service Name
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter`

[ESCU - CMD Carry Out String Command Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies command-line arguments where `cmd.exe /c` is used to execute a program. `cmd /c` is used to run commands in MS-DOS and terminate after command or process completion. This technique is commonly seen in adversaries and malware to execute batch command using different shell like PowerShell or different process other than `cmd.exe`. This is a good hunting query for suspicious command-line made by a script or relative process execute it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.003", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies command-line arguments where `cmd.exe /c` is used to execute a program. `cmd /c` is used to run commands in MS-DOS and terminate after command or process completion. This technique is commonly seen in adversaries and malware to execute batch command using different shell like PowerShell or different process other than `cmd.exe`. This is a good hunting query for suspicious command-line made by a script or relative process execute it.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be high based on legitimate scripted code in any environment. Filter as needed.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - CMD Carry Out String Command Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["ProxyNotShell", "Qakbot", "Winter Vivern", "DarkCrystal RAT", "AsyncRAT", "Log4Shell CVE-2021-44228", "Hermetic Wiper", "Chaos Ransomware", "IcedID", "WhisperGate", "Data Destruction", "Living Off The Land", "Azorult", "Data Destruction", "Warzone RAT", "NjRAT"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CMD Carry Out String Command Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyNotShell", "Qakbot", "Winter Vivern", "DarkCrystal RAT", "AsyncRAT", "Log4Shell CVE-2021-44228", "Hermetic Wiper", "Chaos Ransomware", "IcedID", "WhisperGate", "Data Destruction", "Living Off The Land", "Azorult", "Data Destruction", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-44228"], "impact": 60, "mitre_attack": ["T1059.003", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` AND Processes.process="* /c *" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_carry_out_string_command_parameter_filter`

[ESCU - CMD Echo Pipe - Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.003", "T1543.003", "T1543"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. It is possible filtering may be required to ensure fidelity.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - CMD Echo Pipe - Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ potentially performing privilege escalation using named pipes related to Cobalt Strike and other frameworks.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CMD Echo Pipe - Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1059", "T1059.003", "T1543.003", "T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`.
action.notable.param.rule_title = CMD Echo Pipe - Escalation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` OR Processes.process=*%comspec%* (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_echo_pipe___escalation_filter`

[ESCU - Cmdline Tool Not Executed In CMD Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a non-standard parent process (not matching CMD, PowerShell, or Explorer) spawning `ipconfig.exe` or `systeminfo.exe`. This particular behavior was seen in FIN7's JSSLoader .NET payload. This is also typically seen when an adversary is injected into another process performing different discovery techniques. This event stands out as a TTP since these tools are commonly executed with a shell application or Explorer parent, and not by another application. This TTP is a good indicator for an adversary gathering host information, but one possible false positive might be an automated tool used by a system administator.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a non-standard parent process (not matching CMD, PowerShell, or Explorer) spawning `ipconfig.exe` or `systeminfo.exe`. This particular behavior was seen in FIN7's JSSLoader .NET payload. This is also typically seen when an adversary is injected into another process performing different discovery techniques. This event stands out as a TTP since these tools are commonly executed with a shell application or Explorer parent, and not by another application. This TTP is a good indicator for an adversary gathering host information, but one possible false positive might be an automated tool used by a system administator.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = A network operator or systems administrator may utilize an automated host discovery application that may generate false positives. Filter as needed.
action.escu.creation_date = 2022-10-27
action.escu.modification_date = 2022-10-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cmdline Tool Not Executed In CMD Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["FIN7", "Qakbot", "CISA AA22-277A", "Qakbot", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = A non-standard parent process $parent_process_name$ spawned child process $process_name$ to execute command-line tool on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cmdline Tool Not Executed In CMD Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7", "Qakbot", "CISA AA22-277A", "Qakbot", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a non-standard parent process (not matching CMD, PowerShell, or Explorer) spawning `ipconfig.exe` or `systeminfo.exe`. This particular behavior was seen in FIN7's JSSLoader .NET payload. This is also typically seen when an adversary is injected into another process performing different discovery techniques. This event stands out as a TTP since these tools are commonly executed with a shell application or Explorer parent, and not by another application. This TTP is a good indicator for an adversary gathering host information, but one possible false positive might be an automated tool used by a system administator.
action.notable.param.rule_title = Cmdline Tool Not Executed In CMD Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "ipconfig.exe" OR Processes.process_name = "systeminfo.exe" OR Processes.process_name = "net.exe" OR Processes.process_name = "net1.exe" OR Processes.process_name = "arp.exe" OR Processes.process_name = "nslookup.exe" OR Processes.process_name = "route.exe" OR Processes.process_name = "netstat.exe" OR Processes.process_name = "whoami.exe") AND NOT (Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell*" OR Processes.parent_process_name="pwsh.exe" OR Processes.parent_process_name = "explorer.exe") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmdline_tool_not_executed_in_cmd_shell_filter`

[ESCU - CMLUA Or CMSTPLUA UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a potential process using COM Object like CMLUA or CMSTPLUA to bypass UAC. This technique has been used by ransomware adversaries to gain administrative privileges to its running process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects a potential process using COM Object like CMLUA or CMSTPLUA to bypass UAC. This technique has been used by ransomware adversaries to gain administrative privileges to its running process.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Legitimate windows application that are not on the list loading this dll. Filter as needed.
action.escu.creation_date = 2021-05-13
action.escu.modification_date = 2021-05-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - CMLUA Or CMSTPLUA UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DarkSide Ransomware", "Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CMLUA Or CMSTPLUA UAC Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a potential process using COM Object like CMLUA or CMSTPLUA to bypass UAC. This technique has been used by ransomware adversaries to gain administrative privileges to its running process.
action.notable.param.rule_title = CMLUA Or CMSTPLUA UAC Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7  ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by dest Image ImageLoaded process_name EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter`

[ESCU - Cobalt Strike Named Pipes - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice. \
Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice. \
Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some of the named pipes identified and added may cause false positives. Filter by process name or pipe name to reduce false positives.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cobalt Strike Named Pipes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Trickbot", "DarkSide Ransomware", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ accessing known suspicious named pipes related to Cobalt Strike.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cobalt Strike Named Pipes - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "DarkSide Ransomware", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice. \
Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications.
action.notable.param.rule_title = Cobalt Strike Named Pipes
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=17 OR EventID=18 PipeName IN (\\msagent_*, \\DserNamePipe*, \\srvsvc_*, \\postex_*, \\status_*, \\MSSE-*, \\spoolss_*, \\win_svc*, \\ntsvcs*, \\winsock*, \\UIA_PIPE*)  | stats count min(_time) as firstTime max(_time) as lastTime by dest, process_name, process_id process_path, PipeName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cobalt_strike_named_pipes_filter`

[ESCU - Common Ransomware Extensions - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects Searches for file modifications that commonly occur with Ransomware to detect modifications to files with extensions that are commonly used by Ransomware. The detection is made by searches for changes in the datamodel=Endpoint.Filesystem, specifically modifications to file extensions that match those commonly used by Ransomware. The detection is important because it suggests that an attacker is attempting to encrypt or otherwise modify files in the environment using malware, potentially leading to data loss that can cause significant damage to an organization's data and systems. False positives might occur so the SOC must investigate the affected system to determine the source of the modification and take appropriate action to contain and remediate the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects Searches for file modifications that commonly occur with Ransomware to detect modifications to files with extensions that are commonly used by Ransomware. The detection is made by searches for changes in the datamodel=Endpoint.Filesystem, specifically modifications to file extensions that match those commonly used by Ransomware. The detection is important because it suggests that an attacker is attempting to encrypt or otherwise modify files in the environment using malware, potentially leading to data loss that can cause significant damage to an organization's data and systems. False positives might occur so the SOC must investigate the affected system to determine the source of the modification and take appropriate action to contain and remediate the attack.
action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data model node. To see the additional metadata, add the following fields, if not already present, please review the detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.
action.escu.creation_date = 2022-11-10
action.escu.modification_date = 2022-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware", "Clop Ransomware", "Prestige Ransomware", "LockBit Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Common Ransomware Extensions - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware", "Clop Ransomware", "Prestige Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1485"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime count latest(Filesystem.user) as user values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest _time span=1h | `drop_dm_object_name(Filesystem)` | rex field=file_name "(?<file_extension>\.[^\.]+)$" | rex field=file_path "(?<true_file_path>([^\\\]*\\\)*).*" | stats min(firstTime) as firstTime max(lastTime) as lastTime latest(user) as user dc(true_file_path) as path_count dc(file_name) as file_count latest(file_name) as file_name latest(true_file_path) as file_path by dest file_extension | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_extensions` | where path_count > 1 OR file_count > 20 | `common_ransomware_extensions_filter`

[ESCU - Common Ransomware Notes - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back.
action.escu.how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
action.escu.known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files.
action.escu.creation_date = 2020-11-09
action.escu.modification_date = 2020-11-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Common Ransomware Notes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware", "Ransomware", "Ryuk Ransomware", "Clop Ransomware", "Chaos Ransomware", "LockBit Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Common Ransomware Notes - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ransomware", "Ryuk Ransomware", "Clop Ransomware", "Chaos Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1485"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter`

[ESCU - Conti Common Exec parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects the suspicious commandline argument of revil ransomware to encrypt specific or all local drive and network shares of the compromised machine or host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search detects the suspicious commandline argument of revil ransomware to encrypt specific or all local drive and network shares of the compromised machine or host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = 3rd party tool may have commandline parameter that can trigger this detection.
action.escu.creation_date = 2021-06-02
action.escu.modification_date = 2021-06-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Conti Common Exec parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing specific Conti Ransomware related parameters.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Conti Common Exec parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1204"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects the suspicious commandline argument of revil ransomware to encrypt specific or all local drive and network shares of the compromised machine or host.
action.notable.param.rule_title = Conti Common Exec parameter
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `conti_common_exec_parameter_filter`

[ESCU - Control Loading from World Writable Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies control.exe loading either a .cpl or .inf from a writable directory. This is related to CVE-2021-40444. During triage, review parallel processes, parent and child, for further suspicious behaviors. In addition, capture file modifications and analyze.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies control.exe loading either a .cpl or .inf from a writable directory. This is related to CVE-2021-40444. During triage, review parallel processes, parent and child, for further suspicious behaviors. In addition, capture file modifications and analyze.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives will be present as control.exe does not natively load from writable paths as defined. One may add .cpl or .inf to the command-line if there is any false positives. Tune as needed.
action.escu.creation_date = 2021-09-08
action.escu.modification_date = 2021-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Control Loading from World Writable Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Microsoft MSHTML Remote Code Execution CVE-2021-40444", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Control Loading from World Writable Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Microsoft MSHTML Remote Code Execution CVE-2021-40444", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-40444"], "impact": 80, "mitre_attack": ["T1218", "T1218.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies control.exe loading either a .cpl or .inf from a writable directory. This is related to CVE-2021-40444. During triage, review parallel processes, parent and child, for further suspicious behaviors. In addition, capture file modifications and analyze.
action.notable.param.rule_title = Control Loading from World Writable Directory
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=control.exe OR Processes.original_file_name=CONTROL.EXE) AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `control_loading_from_world_writable_directory_filter`

[ESCU - Create local admin accounts using net exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of local administrator accounts using the net.exe command to mitigate the risks associated with unauthorized access and prevent further damage to the environment by responding to potential threats earlier and taking appropriate actions to protect the organization's systems and data. This detection is made by a Splunk query to search for processes with the name net.exe or net1.exe that include the "/add" parameter and have specific keywords related to administrator accounts in their process name. This detection is important because the creation of unauthorized local administrator accounts might indicate that an attacker has successfully created a new administrator account and is trying to gain persistent access to a system or escalate their privileges for data theft, or other malicious activities. False positives might occur since there might be legitimate uses of the net.exe command and the creation of administrator accounts in certain circumstances. You must consider the context of the activity and other indicators of compromise before taking any action. For next steps, review the details of the identified process, including the user, parent process, and parent process name. Examine any relevant on-disk artifacts and look for concurrent processes to determine the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of local administrator accounts using the net.exe command to mitigate the risks associated with unauthorized access and prevent further damage to the environment by responding to potential threats earlier and taking appropriate actions to protect the organization's systems and data. This detection is made by a Splunk query to search for processes with the name net.exe or net1.exe that include the "/add" parameter and have specific keywords related to administrator accounts in their process name. This detection is important because the creation of unauthorized local administrator accounts might indicate that an attacker has successfully created a new administrator account and is trying to gain persistent access to a system or escalate their privileges for data theft, or other malicious activities. False positives might occur since there might be legitimate uses of the net.exe command and the creation of administrator accounts in certain circumstances. You must consider the context of the activity and other indicators of compromise before taking any action. For next steps, review the details of the identified process, including the user, parent process, and parent process name. Examine any relevant on-disk artifacts and look for concurrent processes to determine the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators often leverage net.exe to create admin accounts.
action.escu.creation_date = 2021-09-08
action.escu.modification_date = 2021-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Create local admin accounts using net exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DHS Report TA18-074A", "Azorult", "CISA AA22-257A"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a user to the local Administrators group.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Create local admin accounts using net exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "Azorult", "CISA AA22-257A"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of local administrator accounts using the net.exe command to mitigate the risks associated with unauthorized access and prevent further damage to the environment by responding to potential threats earlier and taking appropriate actions to protect the organization's systems and data. This detection is made by a Splunk query to search for processes with the name net.exe or net1.exe that include the "/add" parameter and have specific keywords related to administrator accounts in their process name. This detection is important because the creation of unauthorized local administrator accounts might indicate that an attacker has successfully created a new administrator account and is trying to gain persistent access to a system or escalate their privileges for data theft, or other malicious activities. False positives might occur since there might be legitimate uses of the net.exe command and the creation of administrator accounts in certain circumstances. You must consider the context of the activity and other indicators of compromise before taking any action. For next steps, review the details of the identified process, including the user, parent process, and parent process name. Examine any relevant on-disk artifacts and look for concurrent processes to determine the source of the attack.
action.notable.param.rule_title = Create local admin accounts using net exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process values(parent_process_name) as parent_process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND Processes.process=*/add* AND (Processes.process=*administrators* OR Processes.process=*administratoren* OR Processes.process=*administrateurs* OR Processes.process=*administrador* OR Processes.process=*amministratori* OR Processes.process=*administratorer*) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_local_admin_accounts_using_net_exe_filter`

[ESCU - Create or delete windows shares using net exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation or deletion of hidden shares using the net.exe command for prompt response and mitigation to enhance the overall security posture of the organization and protect against potential data breaches, malware infections, and other damaging outcomes. This detection is made by searching for processes that involve the use of net.exe and filters for actions related to creation or deletion of shares. This detection is important because it suggests that an attacker is attempting to manipulate or exploit the network by creating or deleting hidden shares. The creation or deletion of hidden shares can indicate malicious activity since attackers might use hidden shares to exfiltrate data, distribute malware, or establish persistence within a network. The impact of such an attack can vary, but it often involves unauthorized access to sensitive information, disruption of services, or the introduction of malware. False positives might occur since legitimate actions can also involve the use of net.exe. An extensive triage and investigation is necessary to determine the intent and nature of the detected activity. Next steps include reviewing the details of the process involving the net.exe command, including the user, parent process, and timestamps during the triage. Additionally, capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070", "T1070.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation or deletion of hidden shares using the net.exe command for prompt response and mitigation to enhance the overall security posture of the organization and protect against potential data breaches, malware infections, and other damaging outcomes. This detection is made by searching for processes that involve the use of net.exe and filters for actions related to creation or deletion of shares. This detection is important because it suggests that an attacker is attempting to manipulate or exploit the network by creating or deleting hidden shares. The creation or deletion of hidden shares can indicate malicious activity since attackers might use hidden shares to exfiltrate data, distribute malware, or establish persistence within a network. The impact of such an attack can vary, but it often involves unauthorized access to sensitive information, disruption of services, or the introduction of malware. False positives might occur since legitimate actions can also involve the use of net.exe. An extensive triage and investigation is necessary to determine the intent and nature of the detected activity. Next steps include reviewing the details of the process involving the net.exe command, including the user, parent process, and timestamps during the triage. Additionally, capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate.
action.escu.creation_date = 2020-09-16
action.escu.modification_date = 2020-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Create or delete windows shares using net exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hidden Cobra Malware", "CISA AA22-277A", "Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ enumerating Windows file shares.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Create or delete windows shares using net exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "CISA AA22-277A", "Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1070", "T1070.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation or deletion of hidden shares using the net.exe command for prompt response and mitigation to enhance the overall security posture of the organization and protect against potential data breaches, malware infections, and other damaging outcomes. This detection is made by searching for processes that involve the use of net.exe and filters for actions related to creation or deletion of shares. This detection is important because it suggests that an attacker is attempting to manipulate or exploit the network by creating or deleting hidden shares. The creation or deletion of hidden shares can indicate malicious activity since attackers might use hidden shares to exfiltrate data, distribute malware, or establish persistence within a network. The impact of such an attack can vary, but it often involves unauthorized access to sensitive information, disruption of services, or the introduction of malware. False positives might occur since legitimate actions can also involve the use of net.exe. An extensive triage and investigation is necessary to determine the intent and nature of the detected activity. Next steps include reviewing the details of the process involving the net.exe command, including the user, parent process, and timestamps during the triage. Additionally, capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the source of the attack.
action.notable.param.rule_title = Create or delete windows shares using net exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` by Processes.process Processes.process_name  Processes.parent_process_name Processes.original_file_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` 

[ESCU - Create Remote Thread In Shell Application - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect suspicious process injection in command shell. This technique was seen in IcedID where it execute cmd.exe process to inject its shellcode as part of its execution as banking trojan. It is really uncommon to have a create remote thread execution in the following application.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect suspicious process injection in command shell. This technique was seen in IcedID where it execute cmd.exe process to inject its shellcode as part of its execution as banking trojan. It is really uncommon to have a create remote thread execution in the following application.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-04
action.escu.modification_date = 2021-08-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Create Remote Thread In Shell Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID", "Qakbot", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ create a remote thread to shell app process $TargetImage$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"threat_object_field": "SourceImage", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Create Remote Thread In Shell Application - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Qakbot", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect suspicious process injection in command shell. This technique was seen in IcedID where it execute cmd.exe process to inject its shellcode as part of its execution as banking trojan. It is really uncommon to have a create remote thread execution in the following application.
action.notable.param.rule_title = Create Remote Thread In Shell Application
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*") | stats count min(_time) as firstTime max(_time) as lastTime by  TargetImage TargetProcessId SourceProcessId  EventCode StartAddress SourceImage dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_remote_thread_in_shell_application_filter`

[ESCU - Create Remote Thread into LSASS - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of a remote thread in the Local Security Authority Subsystem Service (LSASS), which is a common tactic used by adversaries to steal user authentication credentials, known as credential dumping. The detection is made by leveraging Sysmon Event ID 8 logs and searches for processes that create remote threads in lsass.exe. This is an unusual activity that is generally linked to credential theft or credential dumping, which is a significant threat to network security. The detection is important because it helps to detect potential credential dumping attacks, which can result in significant damage to an organization's security. False positives might occur though the confidence level of this alert is high. There might be cases where legitimate tools can access LSASS and generate similar logs. Therefore, you must understand the broader context of such events and differentiate between legitimate activities and possible threats.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the creation of a remote thread in the Local Security Authority Subsystem Service (LSASS), which is a common tactic used by adversaries to steal user authentication credentials, known as credential dumping. The detection is made by leveraging Sysmon Event ID 8 logs and searches for processes that create remote threads in lsass.exe. This is an unusual activity that is generally linked to credential theft or credential dumping, which is a significant threat to network security. The detection is important because it helps to detect potential credential dumping attacks, which can result in significant damage to an organization's security. False positives might occur though the confidence level of this alert is high. There might be cases where legitimate tools can access LSASS and generate similar logs. Therefore, you must understand the broader context of such events and differentiate between legitimate activities and possible threats.
action.escu.how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise.
action.escu.creation_date = 2019-12-06
action.escu.modification_date = 2019-12-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Create Remote Thread into LSASS - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = A process has created a remote thread into $TargetImage$ on $dest$. This behavior is indicative of credential dumping and should be investigated.
action.risk.param._risk = [{"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Create Remote Thread into LSASS - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of a remote thread in the Local Security Authority Subsystem Service (LSASS), which is a common tactic used by adversaries to steal user authentication credentials, known as credential dumping. The detection is made by leveraging Sysmon Event ID 8 logs and searches for processes that create remote threads in lsass.exe. This is an unusual activity that is generally linked to credential theft or credential dumping, which is a significant threat to network security. The detection is important because it helps to detect potential credential dumping attacks, which can result in significant damage to an organization's security. False positives might occur though the confidence level of this alert is high. There might be cases where legitimate tools can access LSASS and generate similar logs. Therefore, you must understand the broader context of such events and differentiate between legitimate activities and possible threats.
action.notable.param.rule_title = Create Remote Thread into LSASS
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, EventCode, TargetImage, TargetProcessId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter`

[ESCU - Creation of lsass Dump with Taskmgr - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect the hands on keyboard behavior of Windows Task Manager creating a process dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect the hands on keyboard behavior of Windows Task Manager creating a process dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp.
action.escu.how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
action.escu.creation_date = 2020-02-03
action.escu.modification_date = 2020-02-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Creation of lsass Dump with Taskmgr - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping", "CISA AA22-257A"]
action.risk = 1
action.risk.param._risk_message = $process_name$ was identified on endpoint $dest$ writing $TargetFilename$ to disk. This behavior is related to dumping credentials via Task Manager.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Creation of lsass Dump with Taskmgr - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "CISA AA22-257A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Detect the hands on keyboard behavior of Windows Task Manager creating a process dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp.
action.notable.param.rule_title = Creation of lsass Dump with Taskmgr
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | stats count min(_time) as firstTime max(_time) as lastTime by dest, object_category, process_name, TargetFilename  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter`

[ESCU - Creation of Shadow Copy - Rule]
action.escu = 0
action.escu.enabled = 1
description = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate administrator usage of Vssadmin or Wmic will create false positives.
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Creation of Shadow Copy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform offline password cracking.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "parent_process", "risk_object_type": "other", "risk_score": 81}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Creation of Shadow Copy - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.
action.notable.param.rule_title = Creation of Shadow Copy
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter`

[ESCU - Creation of Shadow Copy with wmic and powershell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the use of two specific tools, wmic and Powershell, to create a shadow copy to identify potential threats earlier and take appropriate actions to mitigate the risks. This detection is made by a Splunk query that searches for processes in the Endpoint.Processes data model where either the process name contains "wmic" or "Powershell" and the process command contains "shadowcopy" and "create". This detection is important because it suggests that an attacker is attempting to manipulate or access data in an unauthorized manner, which can lead to data theft, data manipulation, or other malicious activities. Attackers might use shadow copies to backup and exfiltrate sensitive data or to hide their tracks by restoring files to a previous state after an attack. Next steps include reviewing the user associated with the process, the process name, the original file name, the process command, and the destination of the process. Additionally, examine any relevant on-disk artifacts and review other concurrent processes to determine the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the use of two specific tools, wmic and Powershell, to create a shadow copy to identify potential threats earlier and take appropriate actions to mitigate the risks. This detection is made by a Splunk query that searches for processes in the Endpoint.Processes data model where either the process name contains "wmic" or "Powershell" and the process command contains "shadowcopy" and "create". This detection is important because it suggests that an attacker is attempting to manipulate or access data in an unauthorized manner, which can lead to data theft, data manipulation, or other malicious activities. Attackers might use shadow copies to backup and exfiltrate sensitive data or to hide their tracks by restoring files to a previous state after an attack. Next steps include reviewing the user associated with the process, the process name, the original file name, the process command, and the destination of the process. Additionally, examine any relevant on-disk artifacts and review other concurrent processes to determine the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legtimate administrator usage of wmic to create a shadow copy.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Creation of Shadow Copy with wmic and powershell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "Living Off The Land", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform offline password cracking.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Creation of Shadow Copy with wmic and powershell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Living Off The Land", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the use of two specific tools, wmic and Powershell, to create a shadow copy to identify potential threats earlier and take appropriate actions to mitigate the risks. This detection is made by a Splunk query that searches for processes in the Endpoint.Processes data model where either the process name contains "wmic" or "Powershell" and the process command contains "shadowcopy" and "create". This detection is important because it suggests that an attacker is attempting to manipulate or access data in an unauthorized manner, which can lead to data theft, data manipulation, or other malicious activities. Attackers might use shadow copies to backup and exfiltrate sensitive data or to hide their tracks by restoring files to a previous state after an attack. Next steps include reviewing the user associated with the process, the process name, the original file name, the process command, and the destination of the process. Additionally, examine any relevant on-disk artifacts and review other concurrent processes to determine the source of the attack.
action.notable.param.rule_title = Creation of Shadow Copy with wmic and powershell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` OR `process_powershell` Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter`

[ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the use of the copy command to dump credentials from a shadow copy so that you can detect potential threats earlier and mitigate the risks associated with credential dumping. The detection is made by using a Splunk query to search for specific processes that indicate credential dumping activity. The query looks for processes with command lines that include references to certain files, such as "sam", "security", "system", and "ntds.dit", located in system directories like "system32" or "windows". The detection is important because it suggests that an attacker is attempting to extract credentials from a shadow copy. Credential dumping is a common technique used by attackers to obtain sensitive login information and gain unauthorized access to systems to escalate privileges, move laterally within the network, or gain unauthorized access to sensitive data. False positives might occur since legitimate processes might also reference these files. During triage, it is crucial to review the process details, including the source and the command that is run. Additionally, you must capture and analyze any relevant on-disk artifacts and investigate concurrent processes to determine the source of the attack
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the use of the copy command to dump credentials from a shadow copy so that you can detect potential threats earlier and mitigate the risks associated with credential dumping. The detection is made by using a Splunk query to search for specific processes that indicate credential dumping activity. The query looks for processes with command lines that include references to certain files, such as "sam", "security", "system", and "ntds.dit", located in system directories like "system32" or "windows". The detection is important because it suggests that an attacker is attempting to extract credentials from a shadow copy. Credential dumping is a common technique used by attackers to obtain sensitive login information and gain unauthorized access to systems to escalate privileges, move laterally within the network, or gain unauthorized access to sensitive data. False positives might occur since legitimate processes might also reference these files. During triage, it is crucial to review the process details, including the source and the command that is run. Additionally, you must capture and analyze any relevant on-disk artifacts and investigate concurrent processes to determine the source of the attack
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to copy SAM and NTDS.dit for offline password cracking.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the use of the copy command to dump credentials from a shadow copy so that you can detect potential threats earlier and mitigate the risks associated with credential dumping. The detection is made by using a Splunk query to search for specific processes that indicate credential dumping activity. The query looks for processes with command lines that include references to certain files, such as "sam", "security", "system", and "ntds.dit", located in system directories like "system32" or "windows". The detection is important because it suggests that an attacker is attempting to extract credentials from a shadow copy. Credential dumping is a common technique used by attackers to obtain sensitive login information and gain unauthorized access to systems to escalate privileges, move laterally within the network, or gain unauthorized access to sensitive data. False positives might occur since legitimate processes might also reference these files. During triage, it is crucial to review the process details, including the source and the command that is run. Additionally, you must capture and analyze any relevant on-disk artifacts and investigate concurrent processes to determine the source of the attack
action.notable.param.rule_title = Credential Dumping via Copy Command from Shadow Copy
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.original_file_name Processes.process_id Processes.parent_process_id  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` 

[ESCU - Credential Dumping via Symlink to Shadow Copy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of a symlink to a shadow copy to identify potential threats earlier and mitigate the risks associated with symlink creation to shadow copies.  The detection is made by using a Splunk query that searches for processes with commands containing "mklink" and "HarddiskVolumeShadowCopy". This analytic retrieves information such as the destination, user, process name, process ID, parent process, original file name, and parent process ID from the Endpoint.Processes data model.  The detection is important because it indicates potential malicious activity since attackers might use this technique to manipulate or delete shadow copies, which are used for system backup and recovery. This detection helps to determine if an attacker is attempting to cover their tracks or prevent data recovery in the event of an incident. The impact of such an attack can be significant since it can hinder incident response efforts, prevent data restoration, and potentially lead to data loss or compromise. Next steps include reviewing the details of the process, such as the destination and the user responsible for creating the symlink. Additionally, you must examine the parent process, any relevant on-disk artifacts, and concurrent processes to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of a symlink to a shadow copy to identify potential threats earlier and mitigate the risks associated with symlink creation to shadow copies.  The detection is made by using a Splunk query that searches for processes with commands containing "mklink" and "HarddiskVolumeShadowCopy". This analytic retrieves information such as the destination, user, process name, process ID, parent process, original file name, and parent process ID from the Endpoint.Processes data model.  The detection is important because it indicates potential malicious activity since attackers might use this technique to manipulate or delete shadow copies, which are used for system backup and recovery. This detection helps to determine if an attacker is attempting to cover their tracks or prevent data recovery in the event of an incident. The impact of such an attack can be significant since it can hinder incident response efforts, prevent data restoration, and potentially lead to data loss or compromise. Next steps include reviewing the details of the process, such as the destination and the user responsible for creating the symlink. Additionally, you must examine the parent process, any relevant on-disk artifacts, and concurrent processes to identify the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Credential Dumping via Symlink to Shadow Copy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create symlink to a shadow copy to grab credentials.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Credential Dumping via Symlink to Shadow Copy - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of a symlink to a shadow copy to identify potential threats earlier and mitigate the risks associated with symlink creation to shadow copies.  The detection is made by using a Splunk query that searches for processes with commands containing "mklink" and "HarddiskVolumeShadowCopy". This analytic retrieves information such as the destination, user, process name, process ID, parent process, original file name, and parent process ID from the Endpoint.Processes data model.  The detection is important because it indicates potential malicious activity since attackers might use this technique to manipulate or delete shadow copies, which are used for system backup and recovery. This detection helps to determine if an attacker is attempting to cover their tracks or prevent data recovery in the event of an incident. The impact of such an attack can be significant since it can hinder incident response efforts, prevent data restoration, and potentially lead to data loss or compromise. Next steps include reviewing the details of the process, such as the destination and the user responsible for creating the symlink. Additionally, you must examine the parent process, any relevant on-disk artifacts, and concurrent processes to identify the source of the attack.
action.notable.param.rule_title = Credential Dumping via Symlink to Shadow Copy
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.parent_process_name Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter`

[ESCU - CSC Net On The Fly Compilation - Rule]
action.escu = 0
action.escu.enabled = 1
description = this analytic is to detect a suspicious compile before delivery approach of .net compiler csc.exe. This technique was seen in several adversaries, malware and even in red teams to take advantage the csc.exe .net compiler tool to compile on the fly a malicious .net code to evade detection from security product. This is a good hunting query to check further the file or process created after this event and check the file path that passed to csc.exe which is the .net code. Aside from that, powershell is capable of using this compiler in executing .net code in a powershell script so filter on that case is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027.004", "T1027"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this analytic is to detect a suspicious compile before delivery approach of .net compiler csc.exe. This technique was seen in several adversaries, malware and even in red teams to take advantage the csc.exe .net compiler tool to compile on the fly a malicious .net code to evade detection from security product. This is a good hunting query to check further the file or process created after this event and check the file path that passed to csc.exe which is the .net code. Aside from that, powershell is capable of using this compiler in executing .net code in a powershell script so filter on that case is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = A network operator or systems administrator may utilize an automated powershell script taht execute .net code that may generate false positive. filter is needed.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - CSC Net On The Fly Compilation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - CSC Net On The Fly Compilation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1027.004", "T1027"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_csc` Processes.process = "*/noconfig*" Processes.process = "*/fullpaths*" Processes.process = "*@*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `csc_net_on_the_fly_compilation_filter`

[ESCU - Curl Download and Bash Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of curl on Linux or MacOS attempting to download a file from a remote source and pipe it to bash. This is typically found with coinminers and most recently with CVE-2021-44228, a vulnerability in Log4j.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of curl on Linux or MacOS attempting to download a file from a remote source and pipe it to bash. This is typically found with coinminers and most recently with CVE-2021-44228, a vulnerability in Log4j.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, however filtering may be required.
action.escu.creation_date = 2021-12-10
action.escu.modification_date = 2021-12-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Curl Download and Bash Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ingress Tool Transfer", "Log4Shell CVE-2021-44228", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ attempting to download a remote file and run it with bash.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Curl Download and Bash Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "Log4Shell CVE-2021-44228", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-44228"], "impact": 80, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of curl on Linux or MacOS attempting to download a file from a remote source and pipe it to bash. This is typically found with coinminers and most recently with CVE-2021-44228, a vulnerability in Log4j.
action.notable.param.rule_title = Curl Download and Bash Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl (Processes.process="*-s *") OR (Processes.process="*|*" AND Processes.process="*bash*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `curl_download_and_bash_execution_filter`

[ESCU - Delete ShadowCopy With PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This following analytic detects PowerShell command to delete shadow copy using the WMIC PowerShell module. This technique was seen used by a recent adversary to deploy DarkSide Ransomware where it executed a child process of PowerShell to execute a hex encoded command to delete shadow copy. This hex encoded command was able to be decrypted by PowerShell log.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This following analytic detects PowerShell command to delete shadow copy using the WMIC PowerShell module. This technique was seen used by a recent adversary to deploy DarkSide Ransomware where it executed a child process of PowerShell to execute a hex encoded command to delete shadow copy. This hex encoded command was able to be decrypted by PowerShell log.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Delete ShadowCopy With PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["DarkSide Ransomware", "Ransomware", "Revil Ransomware"]
action.risk = 1
action.risk.param._risk_message = An attempt to delete ShadowCopy was performed using PowerShell on $Computer$ by $User$.
action.risk.param._risk = [{"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Delete ShadowCopy With PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware", "Revil Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This following analytic detects PowerShell command to delete shadow copy using the WMIC PowerShell module. This technique was seen used by a recent adversary to deploy DarkSide Ransomware where it executed a child process of PowerShell to execute a hex encoded command to delete shadow copy. This hex encoded command was able to be decrypted by PowerShell log.
action.notable.param.rule_title = Delete ShadowCopy With PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText= "*ShadowCopy*" (ScriptBlockText = "*Delete*" OR ScriptBlockText = "*Remove*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `delete_shadowcopy_with_powershell_filter`

[ESCU - Deleting Of Net Users - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1531"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = System administrators or scripts may delete user accounts via this technique. Filter as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Deleting Of Net Users - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to delete accounts.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deleting Of Net Users - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1531"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after.
action.notable.param.rule_title = Deleting Of Net Users
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="*user*" AND  Processes.process="*/delete*" by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `deleting_of_net_users_filter`

[ESCU - Deleting Shadow Copies - Rule]
action.escu = 0
action.escu.enabled = 1
description = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service.  Wmic is an interface to the Windows Management Instrumentation.  This search looks for either of these tools being used to delete shadow copies.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service.  Wmic is an interface to the Windows Management Instrumentation.  This search looks for either of these tools being used to delete shadow copies.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare.
action.escu.creation_date = 2020-11-09
action.escu.modification_date = 2020-11-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Deleting Shadow Copies - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware", "Clop Ransomware", "CISA AA22-264A", "Prestige Ransomware", "Chaos Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to delete shadow copies.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 81}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deleting Shadow Copies - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware", "Clop Ransomware", "CISA AA22-264A", "Prestige Ransomware", "Chaos Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service.  Wmic is an interface to the Windows Management Instrumentation.  This search looks for either of these tools being used to delete shadow copies.
action.notable.param.rule_title = Deleting Shadow Copies
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter`

[ESCU - Detect AzureHound Command-Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the common command-line argument used by AzureHound `Invoke-AzureHound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the common command-line argument used by AzureHound `Invoke-AzureHound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown.
action.escu.creation_date = 2021-06-01
action.escu.modification_date = 2021-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AzureHound Command-Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Discovery Techniques"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ using AzureHound to enumerate AzureAD.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect AzureHound Command-Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the common command-line argument used by AzureHound `Invoke-AzureHound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
action.notable.param.rule_title = Detect AzureHound Command-Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_command_line_arguments_filter`

[ESCU - Detect AzureHound File Modifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.
action.escu.creation_date = 2021-06-01
action.escu.modification_date = 2021-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect AzureHound File Modifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Discovery Techniques"]
action.risk = 1
action.risk.param._risk_message = A file - $file_name$ was written to disk that is related to AzureHound, a AzureAD enumeration utility, has occurred on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "file_name", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect AzureHound File Modifications - Rule
action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip.
action.notable.param.rule_title = Detect AzureHound File Modifications
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.user | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_file_modifications_filter`

[ESCU - Detect Baron Samedit CVE-2021-3156 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a specific type of vulnerability known as a heap-based buffer overflow in the sudoedit command, commonly referred to as Baron Samedit CVE-2021-3156. The detection is made by a Splunk query that searches for instances of the sudoedit command with the "-s" flag followed by a double quote. This combination of parameters is indicative of the vulnerability being exploited. The detection is important because it suggests that an attacker is attempting to exploit the Baron Samedit vulnerability. The Baron Samedit vulnerability allows an attacker to gain elevated privileges on a Linux system and run arbitrary code with root privileges, potentially leading to complete control over the affected system. The impact of a successful attack can be severe since it allows the attacker to bypass security measures and gain unauthorized access to sensitive data or systems. This can result in data breaches, unauthorized modifications, or even complete system compromise. Next steps include being aware of this vulnerability and actively monitoring any attempts to exploit it. By detecting and responding to such attacks in a timely manner, you can prevent or minimize the potential damage caused by the heap-based buffer overflow of sudoedit.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects a specific type of vulnerability known as a heap-based buffer overflow in the sudoedit command, commonly referred to as Baron Samedit CVE-2021-3156. The detection is made by a Splunk query that searches for instances of the sudoedit command with the "-s" flag followed by a double quote. This combination of parameters is indicative of the vulnerability being exploited. The detection is important because it suggests that an attacker is attempting to exploit the Baron Samedit vulnerability. The Baron Samedit vulnerability allows an attacker to gain elevated privileges on a Linux system and run arbitrary code with root privileges, potentially leading to complete control over the affected system. The impact of a successful attack can be severe since it allows the attacker to bypass security measures and gain unauthorized access to sensitive data or systems. This can result in data breaches, unauthorized modifications, or even complete system compromise. Next steps include being aware of this vulnerability and actively monitoring any attempts to exploit it. By detecting and responding to such attacks in a timely manner, you can prevent or minimize the potential damage caused by the heap-based buffer overflow of sudoedit.
action.escu.how_to_implement = Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-01-27
action.escu.modification_date = 2021-01-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Baron Samedit CVE-2021-3156 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Baron Samedit CVE-2021-3156"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Baron Samedit CVE-2021-3156 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Baron Samedit CVE-2021-3156"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-3156"], "impact": 50, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a specific type of vulnerability known as a heap-based buffer overflow in the sudoedit command, commonly referred to as Baron Samedit CVE-2021-3156. The detection is made by a Splunk query that searches for instances of the sudoedit command with the "-s" flag followed by a double quote. This combination of parameters is indicative of the vulnerability being exploited. The detection is important because it suggests that an attacker is attempting to exploit the Baron Samedit vulnerability. The Baron Samedit vulnerability allows an attacker to gain elevated privileges on a Linux system and run arbitrary code with root privileges, potentially leading to complete control over the affected system. The impact of a successful attack can be severe since it allows the attacker to bypass security measures and gain unauthorized access to sensitive data or systems. This can result in data breaches, unauthorized modifications, or even complete system compromise. Next steps include being aware of this vulnerability and actively monitoring any attempts to exploit it. By detecting and responding to such attacks in a timely manner, you can prevent or minimize the potential damage caused by the heap-based buffer overflow of sudoedit.
action.notable.param.rule_title = Detect Baron Samedit CVE-2021-3156
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `linux_hosts` "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter`

[ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the occurrence of a heap-based buffer overflow in sudoedit.The detection is made by using a Splunk query to identify Linux hosts where the terms "sudoedit" and "segfault" appear in the logs. The detection is important because the heap-based buffer overflow vulnerability in sudoedit can be exploited by attackers to gain elevated root privileges on a vulnerable system, which might lead to the compromise of sensitive data, unauthorized access, and other malicious activities. False positives might occur. Therefore, you must review the logs and investigate further before taking any action.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the occurrence of a heap-based buffer overflow in sudoedit.The detection is made by using a Splunk query to identify Linux hosts where the terms "sudoedit" and "segfault" appear in the logs. The detection is important because the heap-based buffer overflow vulnerability in sudoedit can be exploited by attackers to gain elevated root privileges on a vulnerable system, which might lead to the compromise of sensitive data, unauthorized access, and other malicious activities. False positives might occur. Therefore, you must review the logs and investigate further before taking any action.
action.escu.how_to_implement = Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged.  This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host
action.escu.known_false_positives = If sudoedit is throwing segfaults for other reasons this will pick those up too.
action.escu.creation_date = 2021-01-29
action.escu.modification_date = 2021-01-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Baron Samedit CVE-2021-3156"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule
action.correlationsearch.annotations = {"analytic_story": ["Baron Samedit CVE-2021-3156"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-3156"], "impact": 50, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the occurrence of a heap-based buffer overflow in sudoedit.The detection is made by using a Splunk query to identify Linux hosts where the terms "sudoedit" and "segfault" appear in the logs. The detection is important because the heap-based buffer overflow vulnerability in sudoedit can be exploited by attackers to gain elevated root privileges on a vulnerable system, which might lead to the compromise of sensitive data, unauthorized access, and other malicious activities. False positives might occur. Therefore, you must review the logs and investigate further before taking any action.
action.notable.param.rule_title = Detect Baron Samedit CVE-2021-3156 Segfault
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `linux_hosts` TERM(sudoedit) TERM(segfault) | stats count min(_time) as firstTime max(_time) as lastTime by host | where count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter`

[ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the heap-based buffer overflow for the sudoedit command and identifies instances where the command "sudoedit -s *" is run using the osquery_process data source. This indicates that the sudoedit command is used with the "-s" flag, which is associated with the heap-based buffer overflow vulnerability. The detection is important because it indicates a potential security vulnerability, specifically Baron Samedit CVE-2021-3156, which helps to identify and respond to potential heap-based buffer overflow attacks to enhance the security posture of the organization. This vulnerability allows an attacker to escalate privileges and potentially gain unauthorized access to the system. If the attack is successful, the attacker can gain full control of the system, run arbitrary code, or access sensitive data. Such attacks can lead to data breaches, unauthorized access, and potential disruption of critical systems. False positives might occur since the legitimate use of the sudoedit command with the "-s" flag can also trigger this detection. You must carefully review and validate the findings before taking any action. Next steps include investigating all true positive detections promptly, reviewing the associated processes, gather relevant artifacts, identifying the source of the attack to contain the threat, mitigate the risks, and prevent further damage to the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the heap-based buffer overflow for the sudoedit command and identifies instances where the command "sudoedit -s *" is run using the osquery_process data source. This indicates that the sudoedit command is used with the "-s" flag, which is associated with the heap-based buffer overflow vulnerability. The detection is important because it indicates a potential security vulnerability, specifically Baron Samedit CVE-2021-3156, which helps to identify and respond to potential heap-based buffer overflow attacks to enhance the security posture of the organization. This vulnerability allows an attacker to escalate privileges and potentially gain unauthorized access to the system. If the attack is successful, the attacker can gain full control of the system, run arbitrary code, or access sensitive data. Such attacks can lead to data breaches, unauthorized access, and potential disruption of critical systems. False positives might occur since the legitimate use of the sudoedit command with the "-s" flag can also trigger this detection. You must carefully review and validate the findings before taking any action. Next steps include investigating all true positive detections promptly, reviewing the associated processes, gather relevant artifacts, identifying the source of the attack to contain the threat, mitigate the risks, and prevent further damage to the environment.
action.escu.how_to_implement = OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-01-28
action.escu.modification_date = 2021-01-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Baron Samedit CVE-2021-3156"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Baron Samedit CVE-2021-3156"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-3156"], "impact": 50, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the heap-based buffer overflow for the sudoedit command and identifies instances where the command "sudoedit -s *" is run using the osquery_process data source. This indicates that the sudoedit command is used with the "-s" flag, which is associated with the heap-based buffer overflow vulnerability. The detection is important because it indicates a potential security vulnerability, specifically Baron Samedit CVE-2021-3156, which helps to identify and respond to potential heap-based buffer overflow attacks to enhance the security posture of the organization. This vulnerability allows an attacker to escalate privileges and potentially gain unauthorized access to the system. If the attack is successful, the attacker can gain full control of the system, run arbitrary code, or access sensitive data. Such attacks can lead to data breaches, unauthorized access, and potential disruption of critical systems. False positives might occur since the legitimate use of the sudoedit command with the "-s" flag can also trigger this detection. You must carefully review and validate the findings before taking any action. Next steps include investigating all true positive detections promptly, reviewing the associated processes, gather relevant artifacts, identifying the source of the attack to contain the threat, mitigate the risks, and prevent further damage to the environment.
action.notable.param.rule_title = Detect Baron Samedit CVE-2021-3156 via OSQuery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter`

[ESCU - Detect Certify Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when the attacker tool Certify or Certipy are used to enumerate Active Directory Certificate Services (AD CS) environments. The default command line arguments of these tools are similar and perform near identical enumeration or exploitation functions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649", "T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies when the attacker tool Certify or Certipy are used to enumerate Active Directory Certificate Services (AD CS) environments. The default command line arguments of these tools are similar and perform near identical enumeration or exploitation functions.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2023-06-25
action.escu.modification_date = 2023-06-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Certify Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Certificate Services", "Ingress Tool Transfer"]
action.risk = 1
action.risk.param._risk_message = Certify/Certipy arguments detected on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"threat_object_field": "process", "threat_object_type": "process"}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Certify Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services", "Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1649", "T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when the attacker tool Certify or Certipy are used to enumerate Active Directory Certificate Services (AD CS) environments. The default command line arguments of these tools are similar and perform near identical enumeration or exploitation functions.
action.notable.param.rule_title = Detect Certify Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("* find *","* auth *","* request *","* req *","* download *",) AND Processes.process IN ("* /vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*","* /ca*", "* -username *","* -u *") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_certify_command_line_arguments_filter`

[ESCU - Detect Certify With PowerShell Script Block Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when the attacker tool  Certify is used through an in-memory PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments. The default command line arguments for the binary version of this tools are similar to PowerShell calls and perform near identical enumeration or exploitation functions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649", "T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when the attacker tool  Certify is used through an in-memory PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments. The default command line arguments for the binary version of this tools are similar to PowerShell calls and perform near identical enumeration or exploitation functions.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell..
action.escu.known_false_positives = Unknown, partial script block matches.
action.escu.creation_date = 2023-06-25
action.escu.modification_date = 2023-06-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Certify With PowerShell Script Block Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services", "Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = Certify arguments through PowerShell detected on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"threat_object_field": "command", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Certify With PowerShell Script Block Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services", "Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1649", "T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when the attacker tool  Certify is used through an in-memory PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments. The default command line arguments for the binary version of this tools are similar to PowerShell calls and perform near identical enumeration or exploitation functions.
action.notable.param.rule_title = Detect Certify With PowerShell Script Block Logging
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText IN ("*find *") AND ScriptBlockText IN ("* /vulnerable*","* -vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*")) OR (ScriptBlockText IN (,"*auth *","*req *",) AND ScriptBlockText IN ("* -ca *","* -username *","* -u *")) OR (ScriptBlockText IN ("*request *","*download *") AND ScriptBlockText IN ("* /ca:*")) | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command Values(OpCode) as reason values(Path) as file_name values(UserID) as user by _time Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval file_name = case(isnotnull(file_name),file_name,true(),"unknown") | eval signature = substr(command,0,256) | rename Computer as dest,EventCode as signature_id | `detect_certify_with_powershell_script_block_logging_filter`

[ESCU - Detect Certipy File Modifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when the attacker tool Certipy is used to enumerate Active Directory Certificate Services (AD CS) environments. The default behavior of this toolkit drops a number of file uniquely named files or file extensions related to it's information gathering and exfiltration process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649", "T1560"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies when the attacker tool Certipy is used to enumerate Active Directory Certificate Services (AD CS) environments. The default behavior of this toolkit drops a number of file uniquely named files or file extensions related to it's information gathering and exfiltration process.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints as well as file creation or deletion events.
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2023-06-25
action.escu.modification_date = 2023-06-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Certipy File Modifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Certificate Services", "Data Exfiltration", "Ingress Tool Transfer"]
action.risk = 1
action.risk.param._risk_message = Suspicious files $file_name$ related to Certipy detected on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 45}, {"threat_object_field": "file_name", "threat_object_type": "file name"}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Certipy File Modifications - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services", "Data Exfiltration", "Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1649", "T1560"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when the attacker tool Certipy is used to enumerate Active Directory Certificate Services (AD CS) environments. The default behavior of this toolkit drops a number of file uniquely named files or file extensions related to it's information gathering and exfiltration process.
action.notable.param.rule_title = Detect Certipy File Modifications
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime values(Processes.process_current_directory) as process_current_directory FROM datamodel=Endpoint.Processes where Processes.action="allowed" BY _time span=1h Processes.user Processes.dest Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.parent_process_name Processes.parent_process Processes.process_guid Processes.action |`drop_dm_object_name(Processes)` | join max=0 dest process_guid [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*_certipy.zip", "*_certipy.txt", "*_certipy.json", "*.ccache") by Filesystem.file_create_time Filesystem.process_id Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` ] | fields firstTime lastTime user dest file_create_time file_name file_path parent_process_name parent_process process_name process_path process_current_directory process process_guid process_id | where isnotnull(file_name) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_certipy_file_modifications_filter`

[ESCU - Detect Computer Changed with Anonymous Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.
action.escu.how_to_implement = This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = None thus far found
action.escu.creation_date = 2020-09-18
action.escu.modification_date = 2020-09-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Computer Changed with Anonymous Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Detect Zerologon Attack"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Computer Changed with Anonymous Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Detect Zerologon Attack"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2020-1472"], "impact": 70, "mitre_attack": ["T1210"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter`

[ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `copy` or `[System.IO.File]::Copy` being used to capture the SAM, SYSTEM or SECURITY hives identified in script block. This will catch the most basic use cases for credentials being taken for offline cracking. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `copy` or `[System.IO.File]::Copy` being used to capture the SAM, SYSTEM or SECURITY hives identified in script block. This will catch the most basic use cases for credentials being taken for offline cracking. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Limited false positives as the scope is limited to SAM, SYSTEM and SECURITY hives.
action.escu.creation_date = 2021-07-21
action.escu.modification_date = 2021-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = PowerShell was identified running a script to capture the SAM hive on endpoint $ComputerName$ by user $User$.
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-36934"], "impact": 80, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `copy` or `[System.IO.File]::Copy` being used to capture the SAM, SYSTEM or SECURITY hives identified in script block. This will catch the most basic use cases for credentials being taken for offline cracking. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Detect Copy of ShadowCopy with Script Block Logging
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 Message IN ("*copy*","*[System.IO.File]::Copy*") AND Message IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_copy_of_shadowcopy_with_script_block_logging_filter`

[ESCU - Detect Credential Dumping through LSASS access - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the reading of lsass memory, which is consistent with credential dumping. Reading lsass memory is a common technique used by attackers to steal credentials from the Windows operating system. The detection is made by monitoring the sysmon events and filtering for specific access permissions (0x1010 and 0x1410) on the lsass.exe process helps identify potential instances of credential dumping.The detection is important because it suggests that an attacker is attempting to extract credentials from the lsass memory, which can lead to unauthorized access, data breaches, and compromise of sensitive information. Credential dumping is often a precursor to further attacks, such as lateral movement, privilege escalation, or data exfiltration. False positives can occur due to legitimate actions that involve accessing lsass memory. Therefore, extensive triage and investigation are necessary to differentiate between malicious and benign activities.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the reading of lsass memory, which is consistent with credential dumping. Reading lsass memory is a common technique used by attackers to steal credentials from the Windows operating system. The detection is made by monitoring the sysmon events and filtering for specific access permissions (0x1010 and 0x1410) on the lsass.exe process helps identify potential instances of credential dumping.The detection is important because it suggests that an attacker is attempting to extract credentials from the lsass memory, which can lead to unauthorized access, data breaches, and compromise of sensitive information. Credential dumping is often a precursor to further attacks, such as lateral movement, privilege escalation, or data exfiltration. False positives can occur due to legitimate actions that involve accessing lsass memory. Therefore, extensive triage and investigation are necessary to differentiate between malicious and benign activities.
action.escu.how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.
action.escu.creation_date = 2019-12-03
action.escu.modification_date = 2019-12-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Credential Dumping through LSASS access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping", "Detect Zerologon Attack"]
action.risk = 1
action.risk.param._risk_message = The $SourceImage$ has attempted access to read $TargetImage$ was identified on endpoint $dest$, this is indicative of credential dumping and should be investigated.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Credential Dumping through LSASS access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Detect Zerologon Attack"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the reading of lsass memory, which is consistent with credential dumping. Reading lsass memory is a common technique used by attackers to steal credentials from the Windows operating system. The detection is made by monitoring the sysmon events and filtering for specific access permissions (0x1010 and 0x1410) on the lsass.exe process helps identify potential instances of credential dumping.The detection is important because it suggests that an attacker is attempting to extract credentials from the lsass memory, which can lead to unauthorized access, data breaches, and compromise of sensitive information. Credential dumping is often a precursor to further attacks, such as lateral movement, privilege escalation, or data exfiltration. False positives can occur due to legitimate actions that involve accessing lsass memory. Therefore, extensive triage and investigation are necessary to differentiate between malicious and benign activities.
action.notable.param.rule_title = Detect Credential Dumping through LSASS access
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` 

[ESCU - Detect Empire with PowerShell Script Block Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the common PowerShell stager used by PowerShell-Empire. Each stager that may use PowerShell all uses the same pattern. The initial HTTP will be base64 encoded and use `system.net.webclient`. Note that some obfuscation may evade the analytic. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the common PowerShell stager used by PowerShell-Empire. Each stager that may use PowerShell all uses the same pattern. The initial HTTP will be base64 encoded and use `system.net.webclient`. Note that some obfuscation may evade the analytic. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may only pertain to it not being related to Empire, but another framework. Filter as needed if any applications use the same pattern.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Empire with PowerShell Script Block Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = The following behavior was identified and typically related to PowerShell-Empire on $Computer$ by $UserID$.
action.risk.param._risk = [{"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Empire with PowerShell Script Block Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the common PowerShell stager used by PowerShell-Empire. Each stager that may use PowerShell all uses the same pattern. The initial HTTP will be base64 encoded and use `system.net.webclient`. Note that some obfuscation may evade the analytic. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Detect Empire with PowerShell Script Block Logging
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104  (ScriptBlockText=*system.net.webclient* AND ScriptBlockText=*frombase64string*) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_empire_with_powershell_script_block_logging_filter`

[ESCU - Detect Excessive Account Lockouts From Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.
action.escu.how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \
 **Splunk>Phantom Playbook Integration**\
If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\

action.escu.known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Excessive Account Lockouts From Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.risk = 1
action.risk.param._risk_message = Multiple accounts have been locked out. Review $dest$ and results related to $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Excessive Account Lockouts From Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where All_Changes.result="*locked out*" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`

[ESCU - Detect Excessive User Account Lockouts - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects user accounts that have been locked out a relatively high number of times in a short period.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search detects user accounts that have been locked out a relatively high number of times in a short period.
action.escu.how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.
action.escu.known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Excessive User Account Lockouts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.risk = 1
action.risk.param._risk_message = Excessive user account lockouts for $user$ in a short period of time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Excessive User Account Lockouts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1078", "T1078.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where  All_Changes.result="*locked out*" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter`

[ESCU - Detect Exchange Web Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell and ProxyNotShell. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.003", "T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell and ProxyNotShell. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
action.escu.known_false_positives = The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Exchange Web Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["ProxyNotShell", "ProxyShell", "CISA AA22-257A", "HAFNIUM Group", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = A file - $file_name$ was written to disk that is related to IIS exploitation previously performed by HAFNIUM. Review further file modifications on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "file_name", "risk_object_type": "other", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Exchange Web Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyNotShell", "ProxyShell", "CISA AA22-257A", "HAFNIUM Group", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1505", "T1505.003", "T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell and ProxyNotShell. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.
action.notable.param.rule_title = Detect Exchange Web Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name IN( "*.aspx", "*.ashx") by _time span=1h Filesystem.user Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `detect_exchange_web_shell_filter`

[ESCU - Detect HTML Help Renamed - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed.
action.escu.creation_date = 2022-04-07
action.escu.modification_date = 2022-04-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect HTML Help Renamed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Compiled HTML Activity", "Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect HTML Help Renamed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=hh.exe AND Processes.original_file_name=HH.EXE by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter`

[ESCU - Detect HTML Help Spawn Child Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed.
action.escu.creation_date = 2021-02-11
action.escu.modification_date = 2021-02-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect HTML Help Spawn Child Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Compiled HTML Activity", "Living Off The Land", "AgentTesla"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ spawning a child process, typically not normal behavior.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect HTML Help Spawn Child Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity", "Living Off The Land", "AgentTesla"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Detect HTML Help Spawn Child Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter`

[ESCU - Detect HTML Help URL in Command Line - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may  contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may  contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect HTML Help URL in Command Line - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Compiled HTML Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ contacting a remote destination to potentally download a malicious payload.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect HTML Help URL in Command Line - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may  contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Detect HTML Help URL in Command Line
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*http* by Processes.dest Processes.user  Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter`

[ESCU - Detect HTML Help Using InfoTech Storage Handlers - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect HTML Help Using InfoTech Storage Handlers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Compiled HTML Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = $process_name$ has been identified using Infotech Storage Handlers to load a specific file within a CHM on $dest$ under user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect HTML Help Using InfoTech Storage Handlers - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1218", "T1218.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Detect HTML Help Using InfoTech Storage Handlers
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter`

[ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies common Mimikatz functions that may be identified in the script block, including `mimikatz`. This will catch the most basic use cases for Pass the Ticket, Pass the Hash and `-DumprCreds`. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies common Mimikatz functions that may be identified in the script block, including `mimikatz`. This will catch the most basic use cases for Pass the Ticket, Pass the Hash and `-DumprCreds`. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited as the commands being identifies are quite specific to EventCode 4104 and Mimikatz. Filter as needed.
action.escu.creation_date = 2022-02-24
action.escu.modification_date = 2022-02-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Hermetic Wiper", "Malicious PowerShell", "CISA AA22-264A", "CISA AA22-320A", "Sandworm Tools", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = The following behavior was identified and typically related to MimiKatz being loaded within the context of PowerShell on $Computer$ by $UserID$.
action.risk.param._risk = [{"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Malicious PowerShell", "CISA AA22-264A", "CISA AA22-320A", "Sandworm Tools", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1003", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies common Mimikatz functions that may be identified in the script block, including `mimikatz`. This will catch the most basic use cases for Pass the Ticket, Pass the Hash and `-DumprCreds`. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Detect Mimikatz With PowerShell Script Block Logging
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*, *kerberos::ptt*, *kerberos::golden*) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_mimikatz_with_powershell_script_block_logging_filter`

[ESCU - Detect mshta inline hta execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect mshta inline hta execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious MSHTA Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing with inline HTA, indicative of defense evasion.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect mshta inline hta execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process.
action.notable.param.rule_title = Detect mshta inline hta execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter`

[ESCU - Detect mshta renamed - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive.
action.escu.creation_date = 2022-04-07
action.escu.modification_date = 2022-04-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect mshta renamed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious MSHTA Activity", "Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect mshta renamed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=mshta.exe AND Processes.original_file_name=MSHTA.EXE by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_mshta_renamed_filter`

[ESCU - Detect MSHTA Url in Command Line - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible legitimate applications may perform this behavior and will need to be filtered.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect MSHTA Url in Command Line - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious MSHTA Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $est$ by user $user$ attempting to access a remote destination to download an additional payload.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect MSHTA Url in Command Line - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
action.notable.param.rule_title = Detect MSHTA Url in Command Line
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter`

[ESCU - Detect New Local Admin account - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of new accounts that have been elevated to local administrators so that you can take immediate action to mitigate the risks and prevent further unauthorized access or malicious activities. This detection is made by using the Splunk query `wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) to search for relevant security events in the Windows event log. When a new account is created or an existing account is added to the Administrators group, this analytic identifies this behavior by looking for EventCode 4720 (A user account was created) or EventCode 4732 (A member was added to a security-enabled global group). This analytic specifically focuses on events where the Group_Name is set to Administrators. This detection is important because it suggests that an attacker has gained elevated privileges and can perform malicious actions with administrative access. This can lead to significant impact, such as unauthorized access to sensitive data, unauthorized modifications to systems or configurations, and potential disruption of critical services. identifying this behavior is crucial for a Security Operations Center (SOC). Next steps include reviewing the details of the security event, including the user account that was created or added to the Administrators group. Also, examine the time span between the first and last occurrence of the event to determine if the behavior is ongoing. Additionally, consider any contextual information, such as the destination where the account was created or added to understand the scope and potential impact of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the creation of new accounts that have been elevated to local administrators so that you can take immediate action to mitigate the risks and prevent further unauthorized access or malicious activities. This detection is made by using the Splunk query `wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) to search for relevant security events in the Windows event log. When a new account is created or an existing account is added to the Administrators group, this analytic identifies this behavior by looking for EventCode 4720 (A user account was created) or EventCode 4732 (A member was added to a security-enabled global group). This analytic specifically focuses on events where the Group_Name is set to Administrators. This detection is important because it suggests that an attacker has gained elevated privileges and can perform malicious actions with administrative access. This can lead to significant impact, such as unauthorized access to sensitive data, unauthorized modifications to systems or configurations, and potential disruption of critical services. identifying this behavior is crucial for a Security Operations Center (SOC). Next steps include reviewing the details of the security event, including the user account that was created or added to the Administrators group. Also, examine the time span between the first and last occurrence of the event to determine if the behavior is ongoing. Additionally, consider any contextual information, such as the destination where the account was created or added to understand the scope and potential impact of the attack.
action.escu.how_to_implement = You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732
action.escu.known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives
action.escu.creation_date = 2020-07-08
action.escu.modification_date = 2020-07-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Local Admin account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["DHS Report TA18-074A", "HAFNIUM Group", "CISA AA22-257A"]
action.risk = 1
action.risk.param._risk_message = A $user$ on $dest$ was added recently. Identify if this was legitimate behavior or not.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect New Local Admin account - Rule
action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "HAFNIUM Group", "CISA AA22-257A"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of new accounts that have been elevated to local administrators so that you can take immediate action to mitigate the risks and prevent further unauthorized access or malicious activities. This detection is made by using the Splunk query `wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) to search for relevant security events in the Windows event log. When a new account is created or an existing account is added to the Administrators group, this analytic identifies this behavior by looking for EventCode 4720 (A user account was created) or EventCode 4732 (A member was added to a security-enabled global group). This analytic specifically focuses on events where the Group_Name is set to Administrators. This detection is important because it suggests that an attacker has gained elevated privileges and can perform malicious actions with administrative access. This can lead to significant impact, such as unauthorized access to sensitive data, unauthorized modifications to systems or configurations, and potential disruption of critical services. identifying this behavior is crucial for a Security Operations Center (SOC). Next steps include reviewing the details of the security event, including the user account that was created or added to the Administrators group. Also, examine the time span between the first and last occurrence of the event to determine if the behavior is ongoing. Additionally, consider any contextual information, such as the destination where the account was created or added to understand the scope and potential impact of the attack.
action.notable.param.rule_title = Detect New Local Admin account
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) | transaction member_id connected=false maxspan=180m | rename member_id as user | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter`

[ESCU - Detect Outlook exe writing a zip file - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
action.escu.how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
action.escu.known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk.
action.escu.creation_date = 2023-2-07
action.escu.modification_date = 2023-2-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Outlook exe writing a zip file - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Outlook exe writing a zip file - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
action.notable.param.rule_title = Detect Outlook exe writing a zip file
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly`  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name  FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*.zip*   OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by  _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name  file_path | where file_name != "" | `detect_outlook_exe_writing_a_zip_file_filter` 

[ESCU - Detect Path Interception By Creation Of program exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.009", "T1574"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. 
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-07-03
action.escu.modification_date = 2020-07-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Path Interception By Creation Of program exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to perform privilege escalation by using unquoted service paths.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Path Interception By Creation Of program exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1574.009", "T1574"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. 
action.notable.param.rule_title = Detect Path Interception By Creation Of program exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?<service_process>[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter`

[ESCU - Detect processes used for System Network Configuration Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for fast execution of processes used for system network configuration discovery on the endpoint.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1016"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for fast execution of processes used for system network configuration discovery on the endpoint.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives.
action.escu.creation_date = 2020-11-10
action.escu.modification_date = 2020-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect processes used for System Network Configuration Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning multiple $process_name$ was identified on endpoint $dest$ by user $user$ typically not a normal behavior of the process.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 32}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 32}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 32}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 32}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect processes used for System Network Configuration Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 80, "impact": 40, "mitre_attack": ["T1016"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for fast execution of processes used for system network configuration discovery on the endpoint.
action.notable.param.rule_title = Detect processes used for System Network Configuration Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT Processes.user IN ("","unknown") by Processes.dest Processes.process_name Processes.parent_process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process parent_process_name eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter`

[ESCU - Detect Prohibited Applications Spawning cmd exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.
action.escu.creation_date = 2020-11-10
action.escu.modification_date = 2020-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Zoom Child Processes", "NOBELIUM Group"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Zoom Child Processes", "NOBELIUM Group"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1059", "T1059.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` by Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter`

[ESCU - Detect PsExec With accepteula Flag - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Active Directory Lateral Movement", "CISA AA22-320A", "Sandworm Tools", "Volt Typhoon", "IcedID", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running the utility for possibly the first time.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect PsExec With accepteula Flag - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Active Directory Lateral Movement", "CISA AA22-320A", "Sandworm Tools", "Volt Typhoon", "IcedID", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1021", "T1021.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line.
action.notable.param.rule_title = Detect PsExec With accepteula Flag
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_psexec` Processes.process=*accepteula* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter`

[ESCU - Detect Rare Executables - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will return a table of processes in the a given window, remove process names which are in the allowed list and list out the top 30 rare processes discovered on different hosts.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search will return a table of processes in the a given window, remove process names which are in the allowed list and list out the top 30 rare processes discovered on different hosts.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results.
action.escu.creation_date = 2022-11-10
action.escu.modification_date = 2022-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Rare Executables - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Rare Executables - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | `filter_rare_process_allow_list` | sort count | head 30 | rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rare_executables_filter` 

[ESCU - Detect RClone Command-Line Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies commonly used command-line arguments used by `rclone.exe` to initiate a file transfer. Some arguments were negated as they are specific to the configuration used by adversaries. In particular, an adversary may list the files or directories of the remote file share using `ls` or `lsd`, which is not indicative of malicious behavior. During triage, at this stage of a ransomware event, exfiltration is about to occur or has already. Isolate the endpoint and continue investigating by review file modifications and parallel processes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1020"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies commonly used command-line arguments used by `rclone.exe` to initiate a file transfer. Some arguments were negated as they are specific to the configuration used by adversaries. In particular, an adversary may list the files or directories of the remote file share using `ls` or `lsd`, which is not indicative of malicious behavior. During triage, at this stage of a ransomware event, exfiltration is about to occur or has already. Isolate the endpoint and continue investigating by review file modifications and parallel processes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as this is restricted to the Rclone process name. Filter or tune the analytic as needed.
action.escu.creation_date = 2021-11-29
action.escu.modification_date = 2021-11-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect RClone Command-Line Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkSide Ransomware", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to connect to a remote cloud service to move files or folders.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect RClone Command-Line Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1020"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies commonly used command-line arguments used by `rclone.exe` to initiate a file transfer. Some arguments were negated as they are specific to the configuration used by adversaries. In particular, an adversary may list the files or directories of the remote file share using `ls` or `lsd`, which is not indicative of malicious behavior. During triage, at this stage of a ransomware event, exfiltration is about to occur or has already. Isolate the endpoint and continue investigating by review file modifications and parallel processes.
action.notable.param.rule_title = Detect RClone Command-Line Usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rclone` Processes.process IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*", "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*")  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |  `security_content_ctime(lastTime)` | `detect_rclone_command_line_usage_filter`

[ESCU - Detect Regasm Spawning a Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
action.escu.creation_date = 2021-02-12
action.escu.modification_date = 2021-02-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regasm Spawning a Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ spawning a child process, typically not normal behavior for $parent_process_name$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regasm Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.notable.param.rule_title = Detect Regasm Spawning a Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter`

[ESCU - Detect Regasm with Network Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote Command And Control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote Command And Control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
action.escu.creation_date = 2022-02-18
action.escu.modification_date = 2022-02-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regasm with Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ contacting a remote destination was identified on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regasm with Network Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote Command And Control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.notable.param.rule_title = Detect Regasm with Network Connection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe  | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process_name, src_ip, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter`

[ESCU - Detect Regasm with no Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in `C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe` and `C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe`.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in `C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe` and `C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe`.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
action.escu.creation_date = 2022-03-15
action.escu.modification_date = 2022-03-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regasm with no Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regasm with no Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in `C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe` and `C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe`.
action.notable.param.rule_title = Detect Regasm with no Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_regasm` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(regasm\.exe.{0,4}$)" | `detect_regasm_with_no_command_line_arguments_filter`

[ESCU - Detect Regsvcs Spawning a Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
action.escu.creation_date = 2021-02-12
action.escu.modification_date = 2021-02-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regsvcs Spawning a Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$  was identified on endpoint $dest$ by user $user$ typically not normal for this process.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regsvcs Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.notable.param.rule_title = Detect Regsvcs Spawning a Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter`

[ESCU - Detect Regsvcs with Network Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote Command And Control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote Command And Control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
action.escu.creation_date = 2022-02-18
action.escu.modification_date = 2022-02-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regsvcs with Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ contacting a remote destination was identified on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regsvcs with Network Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote Command And Control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.notable.param.rule_title = Detect Regsvcs with Network Connection
action.notable.param.security_domain = Endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe  | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process_name, src_ip, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter`

[ESCU - Detect Regsvcs with No Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
action.escu.creation_date = 2022-03-15
action.escu.modification_date = 2022-03-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regsvcs with No Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regsvcs with No Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1218", "T1218.009"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
action.notable.param.rule_title = Detect Regsvcs with No Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_regsvcs` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(regsvcs\.exe.{0,4}$)"| `detect_regsvcs_with_no_command_line_arguments_filter`

[ESCU - Detect Regsvr32 Application Control Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \
Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. 
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \
Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. 
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives related to third party software registering .DLL's.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Regsvr32 Application Control Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack", "Suspicious Regsvr32 Activity"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ in an attempt to bypass detection and preventative controls was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Regsvr32 Application Control Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack", "Suspicious Regsvr32 Activity"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \
Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. 
action.notable.param.rule_title = Detect Regsvr32 Application Control Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter`

[ESCU - Detect Renamed 7-Zip - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies renamed 7-Zip usage using Sysmon. At this stage of an attack, review parallel processes and file modifications for data that is staged or potentially have been exfiltrated. This analytic utilizes the OriginalFileName to capture the renamed process. During triage, validate this is the legitimate version of `7zip` by reviewing the PE metadata. In addition, review parallel processes for further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies renamed 7-Zip usage using Sysmon. At this stage of an attack, review parallel processes and file modifications for data that is staged or potentially have been exfiltrated. This analytic utilizes the OriginalFileName to capture the renamed process. During triage, validate this is the legitimate version of `7zip` by reviewing the PE metadata. In addition, review parallel processes for further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives, however this analytic will need to be modified for each environment if Sysmon is not used.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Renamed 7-Zip - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Collection and Staging"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Renamed 7-Zip - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 10"], "confidence": 90, "impact": 30, "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=7z*.exe AND Processes.process_name!=7z*.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_7_zip_filter`

[ESCU - Detect Renamed PSExec - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies renamed instances of `PsExec.exe` being utilized on an endpoint. Most instances, it is highly probable to capture `Psexec.exe` or other SysInternal utility usage with the command-line argument of `-accepteula`. During triage, validate this is the legitimate version of `PsExec` by reviewing the PE metadata. In addition, review parallel processes for further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies renamed instances of `PsExec.exe` being utilized on an endpoint. Most instances, it is highly probable to capture `Psexec.exe` or other SysInternal utility usage with the command-line argument of `-accepteula`. During triage, validate this is the legitimate version of `PsExec` by reviewing the PE metadata. In addition, review parallel processes for further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present. It is possible some third party applications may use older versions of PsExec, filter as needed.
action.escu.creation_date = 2022-04-07
action.escu.modification_date = 2022-04-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Renamed PSExec - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Active Directory Lateral Movement", "CISA AA22-320A", "Sandworm Tools", "BlackByte Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Renamed PSExec - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Active Directory Lateral Movement", "CISA AA22-320A", "Sandworm Tools", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 30, "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name!=psexec.exe OR Processes.process_name!=psexec64.exe) AND Processes.original_file_name=psexec.c by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_psexec_filter`

[ESCU - Detect Renamed RClone - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the usage of `rclone.exe`, renamed, being used to exfiltrate data to a remote destination. RClone has been used by multiple ransomware groups to exfiltrate data. In many instances, it will be downloaded from the legitimate site and executed accordingly. During triage, isolate the endpoint and begin to review parallel processes for additional behavior. At this stage, the adversary may have staged data to be exfiltrated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1020"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the usage of `rclone.exe`, renamed, being used to exfiltrate data to a remote destination. RClone has been used by multiple ransomware groups to exfiltrate data. In many instances, it will be downloaded from the legitimate site and executed accordingly. During triage, isolate the endpoint and begin to review parallel processes for additional behavior. At this stage, the adversary may have staged data to be exfiltrated.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as this analytic identifies renamed instances of `rclone.exe`. Filter as needed if there is a legitimate business use case.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Renamed RClone - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkSide Ransomware", "Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Renamed RClone - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 30, "mitre_attack": ["T1020"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=rclone.exe AND Processes.process_name!=rclone.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_rclone_filter`

[ESCU - Detect Renamed WinRAR - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analtyic identifies renamed instances of `WinRAR.exe`. In most cases, it is not common for WinRAR to be used renamed, however it is common to be installed by a third party application and executed from a non-standard path. During triage, validate additional metadata from the binary that this is `WinRAR`. Review parallel processes and file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analtyic identifies renamed instances of `WinRAR.exe`. In most cases, it is not common for WinRAR to be used renamed, however it is common to be installed by a third party application and executed from a non-standard path. During triage, validate additional metadata from the binary that this is `WinRAR`. Review parallel processes and file modifications.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. It is possible third party applications use renamed instances of WinRAR.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Renamed WinRAR - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Collection and Staging", "CISA AA22-277A"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Renamed WinRAR - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging", "CISA AA22-277A"], "cis20": ["CIS 10"], "confidence": 90, "impact": 30, "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=WinRAR.exe (Processes.process_name!=rar.exe OR Processes.process_name!=winrar.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_winrar_filter`

[ESCU - Detect RTLO In File Name - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is used to detect the abuse of the right-to-left override (RTLO or RLO) character (U+202E) RTLO. This technique is used by adversaries to disguise a string and/or file name to make it appear benign. The RTLO character is a non-printing Unicode character that causes the text that follows it to be displayed in reverse.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036.002", "T1036"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is used to detect the abuse of the right-to-left override (RTLO or RLO) character (U+202E) RTLO. This technique is used by adversaries to disguise a string and/or file name to make it appear benign. The RTLO character is a non-printing Unicode character that causes the text that follows it to be displayed in reverse.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that includes the full command line of the process being launched on your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Implementation in regions that use right to left in native language.
action.escu.creation_date = 2023-04-26
action.escu.modification_date = 2023-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect RTLO In File Name - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = Suspicious RTLO detected in $file_name$ on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect RTLO In File Name - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1036.002", "T1036"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to detect the abuse of the right-to-left override (RTLO or RLO) character (U+202E) RTLO. This technique is used by adversaries to disguise a string and/or file name to make it appear benign. The RTLO character is a non-printing Unicode character that causes the text that follows it to be displayed in reverse.
action.notable.param.rule_title = Detect RTLO In File Name
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_create_time) as file_create_time from datamodel=Endpoint.Filesystem where Filesystem.file_name!=unknown by Filesystem.dest Filesystem.user Filesystem.process_id Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex file_name =  "\\x{202E}" | rex field=file_name "(?<RTLO_file_1>.+)(?<RTLO_exist_file>\\x{202E})(?<RTLO_file_2>.+)" | eval file_name_with_RTLO=file_name | eval file_name=RTLO_file_1.RTLO_file_2 | fields - RTLO* | `detect_rtlo_in_file_name_filter`

[ESCU - Detect RTLO In Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is used to detect the abuse of the right-to-left override (RTLO or RLO) character (U+202E) RTLO. This technique is used by adversaries to disguise a string and/or file name to make it appear benign. The RTLO character is a non-printing Unicode character that causes the text that follows it to be displayed in reverse.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036.002", "T1036"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is used to detect the abuse of the right-to-left override (RTLO or RLO) character (U+202E) RTLO. This technique is used by adversaries to disguise a string and/or file name to make it appear benign. The RTLO character is a non-printing Unicode character that causes the text that follows it to be displayed in reverse.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Implementation in regions that use right to left in native language.
action.escu.creation_date = 2023-04-26
action.escu.modification_date = 2023-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect RTLO In Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = Suspicious RTLO detected in $process_name$ on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect RTLO In Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1036.002", "T1036"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to detect the abuse of the right-to-left override (RTLO or RLO) character (U+202E) RTLO. This technique is used by adversaries to disguise a string and/or file name to make it appear benign. The RTLO character is a non-printing Unicode character that causes the text that follows it to be displayed in reverse.
action.notable.param.rule_title = Detect RTLO In Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process!=unknown AND Processes.action=allowed by Processes.dest Processes.user Processes.original_file_name Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | regex process="\\x{202E}" | rex field=process "(?<RTLO_command_1>.+)(?<RTLO_exist_process>\\x{202E})(?<RTLO_command_2>.+)" | eval process_with_RTLO=process | eval process=RTLO_command_1.RTLO_command_2 | fields - RTLO* | `detect_rtlo_in_process_filter`

[ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive.
action.escu.creation_date = 2021-02-04
action.escu.modification_date = 2021-02-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.notable.param.rule_title = Detect Rundll32 Application Control Bypass - advpack
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*advpack* by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter`

[ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use setupapi triggering a false positive.
action.escu.creation_date = 2021-02-04
action.escu.modification_date = 2021-02-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.notable.param.rule_title = Detect Rundll32 Application Control Bypass - setupapi
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*setupapi* by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter`

[ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive.
action.escu.creation_date = 2021-02-04
action.escu.modification_date = 2021-02-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ loading syssetup.dll by calling the LaunchINFSection function on the command line was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
action.notable.param.rule_title = Detect Rundll32 Application Control Bypass - syssetup
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*syssetup* by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter`

[ESCU - Detect Rundll32 Inline HTA Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
action.escu.creation_date = 2021-01-20
action.escu.modification_date = 2021-01-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Rundll32 Inline HTA Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious MSHTA Activity", "NOBELIUM Group", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = Suspicious rundll32.exe inline HTA execution on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Rundll32 Inline HTA Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "NOBELIUM Group", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
action.notable.param.rule_title = Detect Rundll32 Inline HTA Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter`

[ESCU - Detect SharpHound Command-Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies common command-line arguments used by SharpHound `-collectionMethod` and `invoke-bloodhound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies common command-line arguments used by SharpHound `-collectionMethod` and `invoke-bloodhound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as the arguments used are specific to SharpHound. Filter as needed or add more command-line arguments as needed.
action.escu.creation_date = 2021-06-01
action.escu.modification_date = 2021-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect SharpHound Command-Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Discovery Techniques", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Possible SharpHound command-Line arguments identified on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect SharpHound Command-Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques", "Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies common command-line arguments used by SharpHound `-collectionMethod` and `invoke-bloodhound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
action.notable.param.rule_title = Detect SharpHound Command-Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_command_line_arguments_filter`

[ESCU - Detect SharpHound File Modifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.
action.escu.creation_date = 2022-10-09
action.escu.modification_date = 2022-10-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect SharpHound File Modifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Discovery Techniques", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Potential SharpHound file modifications identified on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect SharpHound File Modifications - Rule
action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques", "Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell.
action.notable.param.rule_title = Detect SharpHound File Modifications
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", "*_ous.json", "*_containers.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.user| `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_file_modifications_filter`

[ESCU - Detect SharpHound Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies SharpHound binary usage by using the original filena,e. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic looks for the original_file_name of `SharpHound.exe` and the process name. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies SharpHound binary usage by using the original filena,e. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic looks for the original_file_name of `SharpHound.exe` and the process name. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as this is specific to a file attribute not used by anything else. Filter as needed.
action.escu.creation_date = 2021-05-27
action.escu.modification_date = 2021-05-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect SharpHound Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Discovery Techniques", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Potential SharpHound binary identified on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect SharpHound Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques", "Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies SharpHound binary usage by using the original filena,e. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic looks for the original_file_name of `SharpHound.exe` and the process name. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary.
action.notable.param.rule_title = Detect SharpHound Usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sharphound.exe OR Processes.original_file_name=SharpHound.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_usage_filter`

[ESCU - Detect suspicious processnames using pretrained model in DSDL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses a pre-trained Deep Learning model to predict whether a processname is suspicious or not. Malwares and malicious programs such as ransomware often use tactics, techniques, and procedures (TTPs) such as copying malicious files to the local machine to propagate themselves across the network. A key indicator of compromise is that after a successful execution of the malware, it copies itself as an executable file with a randomly generated filename and places this file in one of the directories. Such techniques are seen in several malwares such as TrickBot. We develop machine learning model that uses a Recurrent Neural Network (RNN) to distinguish between malicious and benign processnames. The model is trained independently and is then made available for download. We use a character level RNN to classify malicious vs. benign processnames. The higher is_malicious_prob, the more likely is the processname to be suspicious (between [0,1]). The threshold for flagging a processname as suspicious is set as 0.5.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic uses a pre-trained Deep Learning model to predict whether a processname is suspicious or not. Malwares and malicious programs such as ransomware often use tactics, techniques, and procedures (TTPs) such as copying malicious files to the local machine to propagate themselves across the network. A key indicator of compromise is that after a successful execution of the malware, it copies itself as an executable file with a randomly generated filename and places this file in one of the directories. Such techniques are seen in several malwares such as TrickBot. We develop machine learning model that uses a Recurrent Neural Network (RNN) to distinguish between malicious and benign processnames. The model is trained independently and is then made available for download. We use a character level RNN to classify malicious vs. benign processnames. The higher is_malicious_prob, the more likely is the processname to be suspicious (between [0,1]). The threshold for flagging a processname as suspicious is set as 0.5.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present if a suspicious processname is similar to a benign processname.
action.escu.creation_date = 2023-01-23
action.escu.modification_date = 2023-01-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect suspicious processnames using pretrained model in DSDL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Command-Line Executions"]
action.risk = 1
action.risk.param._risk_message = The process $process$ is running from an unusual place by $user$ on $dest$ with a processname that appears to be randomly generated.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect suspicious processnames using pretrained model in DSDL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name Processes.parent_process_name Processes.process Processes.user Processes.dest | `drop_dm_object_name(Processes)` | rename process_name as text | fields text, parent_process_name, process, user, dest | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl | rename predicted_label as is_suspicious_score | rename text as process_name | where is_suspicious_score > 0.5 | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`

[ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate applications may exhibit this behavior.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Command-Line Executions", "Azorult"]
action.risk = 1
action.risk.param._risk_message = cmd.exe launching script interpreters on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Command-Line Executions", "Azorult"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1059", "T1059.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine
action.notable.param.rule_title = Detect Use of cmd exe to Launch Script Interpreters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`

[ESCU - Detect Webshell Exploit Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is used to detect the abuse of web applications by adversaries. Adversaries may install a backdoor or script onto web servers by exploiting known vulnerabilities or misconfigruations. Web shells are used to establish persistent access to systems and provide a set of executable functions or a command-line interface on the system hosting the Web server.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is used to detect the abuse of web applications by adversaries. Adversaries may install a backdoor or script onto web servers by exploiting known vulnerabilities or misconfigruations. Web shells are used to establish persistent access to systems and provide a set of executable functions or a command-line interface on the system hosting the Web server.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate OS functions called by vendor applications, baseline the environment and filter before enabling. Recommend throttle by dest/process_name
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Webshell Exploit Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["ProxyNotShell", "ProxyShell", "CISA AA22-257A", "HAFNIUM Group", "BlackByte Ransomware", "CISA AA22-264A", "Citrix ShareFile RCE CVE-2023-24489", "Flax Typhoon", "WS FTP Server Critical Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Webshell Exploit Behavior - $parent_process_name$ spawned $process_name$ on $dest$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Webshell Exploit Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyNotShell", "ProxyShell", "CISA AA22-257A", "HAFNIUM Group", "BlackByte Ransomware", "CISA AA22-264A", "Citrix ShareFile RCE CVE-2023-24489", "Flax Typhoon", "WS FTP Server Critical Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1505", "T1505.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to detect the abuse of web applications by adversaries. Adversaries may install a backdoor or script onto web servers by exploiting known vulnerabilities or misconfigruations. Web shells are used to establish persistent access to systems and provide a set of executable functions or a command-line interface on the system hosting the Web server.
action.notable.param.rule_title = Detect Webshell Exploit Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("arp.exe","at.exe","bash.exe","bitsadmin.exe","certutil.exe","cmd.exe","cscript.exe", "dsget.exe","dsquery.exe","find.exe","findstr.exe","fsutil.exe","hostname.exe","ipconfig.exe","ksh.exe","nbstat.exe", "net.exe","net1.exe","netdom.exe","netsh.exe","netstat.exe","nltest.exe","nslookup.exe","ntdsutil.exe","pathping.exe", "ping.exe","powershell.exe","pwsh.exe","qprocess.exe","query.exe","qwinsta.exe","reg.exe","rundll32.exe","sc.exe", "scrcons.exe","schtasks.exe","sh.exe","systeminfo.exe","tasklist.exe","tracert.exe","ver.exe","vssadmin.exe", "wevtutil.exe","whoami.exe","wmic.exe","wscript.exe","wusa.exe","zsh.exe") AND Processes.parent_process_name IN ("w3wp.exe", "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe","tomcat*.exe")) by Processes.dest,Processes.user,Processes.parent_process,Processes.parent_process_name,Processes.process,Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_webshell_exploit_behavior_filter`

[ESCU - Detect WMI Event Subscription Persistence - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID equals 19 \
1. Consumer - An action to take upon triggering the filter. EventID equals 20 \
1. Binding - Registers a filter to a consumer. EventID equals 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.003", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID equals 19 \
1. Consumer - An action to take upon triggering the filter. EventID equals 20 \
1. Binding - Registers a filter to a consumer. EventID equals 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with that provide WMI Event Subscription from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID 19, 20 and 21. Tune and filter known good to limit the volume.
action.escu.known_false_positives = It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage.
action.escu.creation_date = 2021-06-16
action.escu.modification_date = 2021-06-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect WMI Event Subscription Persistence - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = Possible malicious WMI Subscription created on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect WMI Event Subscription Persistence - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1546.003", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID equals 19 \
1. Consumer - An action to take upon triggering the filter. EventID equals 20 \
1. Binding - Registers a filter to a consumer. EventID equals 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.
action.notable.param.rule_title = Detect WMI Event Subscription Persistence
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=20 | stats count min(_time) as firstTime max(_time) as lastTime by Computer User Destination | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_wmi_event_subscription_persistence_filter`

[ESCU - Detection of tools built by NirSoft - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1072"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detection of tools built by NirSoft - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A "]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detection of tools built by NirSoft - Rule
action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware  DHS Report TA18-201A "], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1072"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers.
action.notable.param.rule_title = Detection of tools built by NirSoft
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter`

[ESCU - Disable AMSI Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to identify modification in registry to disable AMSI windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to identify modification in registry to disable AMSI windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = network operator may disable this feature of windows but not so common.
action.escu.creation_date = 2023-04-11
action.escu.modification_date = 2023-04-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable AMSI Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Disable AMSI Through Registry on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable AMSI Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to identify modification in registry to disable AMSI windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.
action.notable.param.rule_title = Disable AMSI Through Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable" Registry.registry_value_data = "0x00000000") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.dest Registry.user | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_amsi_through_registry_filter`

[ESCU - Disable Defender AntiVirus Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This particular behavior is typically executed when an adversary or malware gains access to an endpoint and begins to perform execution and to evade detections. Usually, a batch (.bat) file will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This particular behavior is typically executed when an adversary or malware gains access to an endpoint and begins to perform execution and to evade detections. Usually, a batch (.bat) file will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable windows defender product
action.escu.creation_date = 2023-04-11
action.escu.modification_date = 2023-04-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Defender AntiVirus Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Defender AntiVirus Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This particular behavior is typically executed when an adversary or malware gains access to an endpoint and begins to perform execution and to evade detections. Usually, a batch (.bat) file will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.notable.param.rule_title = Disable Defender AntiVirus Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender*" Registry.registry_value_name IN ("DisableAntiSpyware","DisableAntiVirus") Registry.registry_value_data = 0x00000001) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.user Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_antivirus_registry_filter`

[ESCU - Disable Defender BlockAtFirstSeen Feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is intended to detect a suspicious modification of the Windows registry to disable a Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV, specifically the BlockAtFirstSeen feature where it blocks suspicious files the first time seen on the host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is intended to detect a suspicious modification of the Windows registry to disable a Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV, specifically the BlockAtFirstSeen feature where it blocks suspicious files the first time seen on the host.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable windows defender product
action.escu.creation_date = 2023-04-11
action.escu.modification_date = 2023-04-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Defender BlockAtFirstSeen Feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse", "Azorult"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Defender BlockAtFirstSeen Feature - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse", "Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is intended to detect a suspicious modification of the Windows registry to disable a Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV, specifically the BlockAtFirstSeen feature where it blocks suspicious files the first time seen on the host.
action.notable.param.rule_title = Disable Defender BlockAtFirstSeen Feature
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = DisableBlockAtFirstSeen Registry.registry_value_data = 0x00000001) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.user Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_blockatfirstseen_feature_filter`

[ESCU - Disable Defender Enhanced Notification - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is intended to detect a suspicious modification of registry to disable windows defender features. This technique attempts to bypass or evade detection from Windows Defender AV, specifically the Enhanced Notification feature where a user or admin would receive alerts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is intended to detect a suspicious modification of registry to disable windows defender features. This technique attempts to bypass or evade detection from Windows Defender AV, specifically the Enhanced Notification feature where a user or admin would receive alerts.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = user may choose to disable windows defender AV
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Defender Enhanced Notification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse", "Azorult"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Defender Enhanced Notification - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse", "Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is intended to detect a suspicious modification of registry to disable windows defender features. This technique attempts to bypass or evade detection from Windows Defender AV, specifically the Enhanced Notification feature where a user or admin would receive alerts.
action.notable.param.rule_title = Disable Defender Enhanced Notification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*Microsoft\\Windows Defender\\Reporting*" Registry.registry_value_name = DisableEnhancedNotifications Registry.registry_value_data = 0x00000001) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_antivirus_registry_filter`

[ESCU - Disable Defender MpEngine Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This particular behavior is typically executed when an adversary or malware gains access to an endpoint and begins to perform execution and to evade detections. Usually, a batch (.bat) file will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This particular behavior is typically executed when an adversary or malware gains access to an endpoint and begins to perform execution and to evade detections. Usually, a batch (.bat) file will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable windows defender product
action.escu.creation_date = 2023-04-11
action.escu.modification_date = 2023-04-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Defender MpEngine Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Defender MpEngine Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This particular behavior is typically executed when an adversary or malware gains access to an endpoint and begins to perform execution and to evade detections. Usually, a batch (.bat) file will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.notable.param.rule_title = Disable Defender MpEngine Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender\\MpEngine*" Registry.registry_value_name = MpEnablePus Registry.registry_value_data = 0x00000000) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.user Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_mpengine_registry_filter`

[ESCU - Disable Defender Spynet Reporting - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is intended to detect a suspicious modification of registry to disable Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV product, specifically the spynet reporting for Defender telemetry.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is intended to detect a suspicious modification of registry to disable Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV product, specifically the spynet reporting for Defender telemetry.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable windows defender product
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Defender Spynet Reporting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse", "Azorult", "Qakbot"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Defender Spynet Reporting - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse", "Azorult", "Qakbot"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is intended to detect a suspicious modification of registry to disable Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV product, specifically the spynet reporting for Defender telemetry.
action.notable.param.rule_title = Disable Defender Spynet Reporting
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = SpynetReporting Registry.registry_value_data = 0x00000000) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.user Registry.dest  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_spynet_reporting_filter`

[ESCU - Disable Defender Submit Samples Consent Feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is intended to detect a suspicious modification of the Windows registry to disable a Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV, specifically the feature that submits samples for further analysis.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is intended to detect a suspicious modification of the Windows registry to disable a Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV, specifically the feature that submits samples for further analysis.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable windows defender product
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Defender Submit Samples Consent Feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse", "Azorult"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Defender Submit Samples Consent Feature - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse", "Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is intended to detect a suspicious modification of the Windows registry to disable a Windows Defender feature. This technique is intended to bypass or evade detection from Windows Defender AV, specifically the feature that submits samples for further analysis.
action.notable.param.rule_title = Disable Defender Submit Samples Consent Feature
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = SubmitSamplesConsent Registry.registry_value_data = 0x00000000) BY _time span=1h Registry.user Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_submit_samples_consent_feature_filter`

[ESCU - Disable ETW Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identify modification in registry to disable ETW windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identify modification in registry to disable ETW windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = network operator may disable this feature of windows but not so common.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable ETW Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Disable ETW Through Registry on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable ETW Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to identify modification in registry to disable ETW windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.
action.notable.param.rule_title = Disable ETW Through Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" Registry.registry_value_data = "0x00000000") BY _time span=1h Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_etw_through_registry_filter`

[ESCU - Disable Logs Using WevtUtil - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect execution of wevtutil.exe to disable logs. This technique was seen in several ransomware to disable the event logs to evade alerts and detections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect execution of wevtutil.exe to disable logs. This technique was seen in several ransomware to disable the event logs to evade alerts and detections.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network operator may disable audit event logs for debugging purposes.
action.escu.creation_date = 2021-06-10
action.escu.modification_date = 2021-06-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Logs Using WevtUtil - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = WevtUtil.exe used to disable Event Logging on $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Logs Using WevtUtil - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect execution of wevtutil.exe to disable logs. This technique was seen in several ransomware to disable the event logs to evade alerts and detections.
action.notable.param.rule_title = Disable Logs Using WevtUtil
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe" Processes.process = "*sl*" Processes.process = "*/e:false*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_logs_using_wevtutil_filter`

[ESCU - Disable Registry Tool - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search identifies modification of registry to disable the regedit or registry tools of the windows operating system. Since registry tool is a swiss knife in analyzing registry, malware such as RAT or trojan Spy disable this application to prevent the removal of their registry entry such as persistence, file less components and defense evasion.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search identifies modification of registry to disable the regedit or registry tools of the windows operating system. Since registry tool is a swiss knife in analyzing registry, malware such as RAT or trojan Spy disable this application to prevent the removal of their registry entry such as persistence, file less components and defense evasion.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may disable this application for non technical user.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Registry Tool - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Disabled Registry Tools on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Registry Tool - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"], "cis20": ["CIS 10"], "confidence": 100, "impact": 40, "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search identifies modification of registry to disable the regedit or registry tools of the windows operating system. Since registry tool is a swiss knife in analyzing registry, malware such as RAT or trojan Spy disable this application to prevent the removal of their registry entry such as persistence, file less components and defense evasion.
action.notable.param.rule_title = Disable Registry Tool
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_data = "0x00000001") BY _time span=1h  Registry.user Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_registry_tool_filter`

[ESCU - Disable Schedule Task - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious commandline to disable existing schedule task. This technique is used by adversaries or commodity malware like IcedID to disable security application (AV products) in the targetted host to evade detections. This TTP is a good pivot to check further why and what other process run before and after this detection. check which process execute the commandline and what task is disabled. parent child process is quite valuable in this scenario too.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious commandline to disable existing schedule task. This technique is used by adversaries or commodity malware like IcedID to disable security application (AV products) in the targetted host to evade detections. This TTP is a good pivot to check further why and what other process run before and after this detection. check which process execute the commandline and what task is disabled. parent child process is quite valuable in this scenario too.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = admin may disable problematic schedule task
action.escu.creation_date = 2021-10-18
action.escu.modification_date = 2021-10-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Schedule Task - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = schtask process with commandline $process$ to disable schedule task in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Schedule Task - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious commandline to disable existing schedule task. This technique is used by adversaries or commodity malware like IcedID to disable security application (AV products) in the targetted host to evade detections. This TTP is a good pivot to check further why and what other process run before and after this detection. check which process execute the commandline and what task is disabled. parent child process is quite valuable in this scenario too.
action.notable.param.rule_title = Disable Schedule Task
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process=*/change*  Processes.process=*/disable* by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_schedule_task_filter`

[ESCU - Disable Security Logs Using MiniNt Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to disable security audit logs. This technique was shared by a researcher to disable Security logs of windows by adding this registry. The Windows will think it is WinPE and will not log any event to the Security Log
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to disable security audit logs. This technique was shared by a researcher to disable Security logs of windows by adding this registry. The Windows will think it is WinPE and will not log any event to the Security Log
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = Unknown.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Security Logs Using MiniNt Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Security Logs Using MiniNt Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious registry modification to disable security audit logs. This technique was shared by a researcher to disable Security logs of windows by adding this registry. The Windows will think it is WinPE and will not log any event to the Security Log
action.notable.param.rule_title = Disable Security Logs Using MiniNt Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Control\\MiniNt\\*") BY _time span=1h  Registry.user Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_security_logs_using_minint_registry_filter`

[ESCU - Disable Show Hidden Files - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows registry to prevent users from seeing all the files with hidden attributes. This event or techniques are known on some worm and trojan spy malware that will drop hidden files on the infected machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1564.001", "T1562.001", "T1564", "T1562", "T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows registry to prevent users from seeing all the files with hidden attributes. This event or techniques are known on some worm and trojan spy malware that will drop hidden files on the infected machine.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Show Hidden Files - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "Azorult"]
action.risk = 1
action.risk.param._risk_message = Disabled 'Show Hidden Files' on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Show Hidden Files - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "Azorult"], "cis20": ["CIS 10"], "confidence": 100, "impact": 40, "mitre_attack": ["T1564.001", "T1562.001", "T1564", "T1562", "T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" Registry.registry_value_data = "0x00000001") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" Registry.registry_value_data = "0x00000000")) BY _time span=1h Registry.user Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_show_hidden_files_filter`

[ESCU - Disable UAC Remote Restriction - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification of registry to disable UAC remote restriction. This technique was well documented in Microsoft page where attacker may modify this registry value to bypassed UAC feature of windows host. This is a good indicator that some tries to bypassed UAC to suspicious process or gain privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification of registry to disable UAC remote restriction. This technique was well documented in Microsoft page where attacker may modify this registry value to bypassed UAC feature of windows host. This is a good indicator that some tries to bypassed UAC to suspicious process or gain privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may set this policy for non-critical machine.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable UAC Remote Restriction - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable UAC Remote Restriction - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious modification of registry to disable UAC remote restriction. This technique was well documented in Microsoft page where attacker may modify this registry value to bypassed UAC feature of windows host. This is a good indicator that some tries to bypassed UAC to suspicious process or gain privilege escalation.
action.notable.param.rule_title = Disable UAC Remote Restriction
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentVersion\\Policies\\System*" Registry.registry_value_name="LocalAccountTokenFilterPolicy" Registry.registry_value_data="0x00000001"  ) BY _time span=1h Registry.user Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_uac_remote_restriction_filter`

[ESCU - Disable Windows App Hotkeys - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a suspicious registry modification to disable Windows hotkey (shortcut keys) for native Windows applications. This technique is commonly used to disable certain or several Windows applications like `taskmgr.exe` and `cmd.exe`. This technique is used to impair the analyst in analyzing and removing the attacker implant in compromised systems.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects a suspicious registry modification to disable Windows hotkey (shortcut keys) for native Windows applications. This technique is commonly used to disable certain or several Windows applications like `taskmgr.exe` and `cmd.exe`. This technique is used to impair the analyst in analyzing and removing the attacker implant in compromised systems.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Windows App Hotkeys - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Disabled 'Windows App Hotkeys' on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Windows App Hotkeys - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 40, "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a suspicious registry modification to disable Windows hotkey (shortcut keys) for native Windows applications. This technique is commonly used to disable certain or several Windows applications like `taskmgr.exe` and `cmd.exe`. This technique is used to impair the analyst in analyzing and removing the attacker implant in compromised systems.
action.notable.param.rule_title = Disable Windows App Hotkeys
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_data= "HotKey Disabled" AND Registry.registry_value_name = "Debugger") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_app_hotkeys_filter`

[ESCU - Disable Windows Behavior Monitoring - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable this windows features.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Windows Behavior Monitoring - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult", "Windows Registry Abuse", "Revil Ransomware", "RedLine Stealer", "Ransomware", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Windows Defender real time behavior monitoring disabled on $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Windows Behavior Monitoring - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult", "Windows Registry Abuse", "Revil Ransomware", "RedLine Stealer", "Ransomware", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 100, "impact": 40, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections.
action.notable.param.rule_title = Disable Windows Behavior Monitoring
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time Protection\\DisableScriptScanning" AND Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_behavior_monitoring_filter`

[ESCU - Disable Windows SmartScreen Protection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following search identifies a modification of registry to disable the smartscreen protection of windows machine. This is windows feature provide an early warning system against website that might engage in phishing attack or malware distribution. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following search identifies a modification of registry to disable the smartscreen protection of windows machine. This is windows feature provide an early warning system against website that might engage in phishing attack or malware distribution. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable this windows features.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disable Windows SmartScreen Protection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = The Windows Smartscreen was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disable Windows SmartScreen Protection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following search identifies a modification of registry to disable the smartscreen protection of windows machine. This is windows feature provide an early warning system against website that might engage in phishing attack or malware distribution. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.
action.notable.param.rule_title = Disable Windows SmartScreen Protection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled"  Registry.registry_value_data= "Off") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_filter`

[ESCU - Disabled Kerberos Pre-Authentication Discovery With Get-ADUser - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADUser` commandlet with specific parameters. `Get-ADUser` is part of the Active Directory PowerShell module used to manage Windows Active Directory networks. As the name suggests, `Get-ADUser` is used to query for domain users. With the appropiate parameters, Get-ADUser allows adversaries to discover domain accounts with Kerberos Pre Authentication disabled.\ Red Teams and adversaries alike use may abuse Get-ADUSer to enumerate these accounts and attempt to crack their passwords offline.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADUser` commandlet with specific parameters. `Get-ADUser` is part of the Active Directory PowerShell module used to manage Windows Active Directory networks. As the name suggests, `Get-ADUser` is used to query for domain users. With the appropiate parameters, Get-ADUser allows adversaries to discover domain accounts with Kerberos Pre Authentication disabled.\ Red Teams and adversaries alike use may abuse Get-ADUSer to enumerate these accounts and attempt to crack their passwords offline.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use search for accounts with Kerberos Pre Authentication disabled for legitimate purposes.
action.escu.creation_date = 2022-05-03
action.escu.modification_date = 2022-05-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabled Kerberos Pre-Authentication Discovery With Get-ADUser - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Disabled Kerberos Pre-Authentication Discovery With Get-ADUser from $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabled Kerberos Pre-Authentication Discovery With Get-ADUser - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADUser` commandlet with specific parameters. `Get-ADUser` is part of the Active Directory PowerShell module used to manage Windows Active Directory networks. As the name suggests, `Get-ADUser` is used to query for domain users. With the appropiate parameters, Get-ADUser allows adversaries to discover domain accounts with Kerberos Pre Authentication disabled.\ Red Teams and adversaries alike use may abuse Get-ADUSer to enumerate these accounts and attempt to crack their passwords offline.
action.notable.param.rule_title = Disabled Kerberos Pre-Authentication Discovery With Get-ADUser
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `powershell` EventCode=4104 (ScriptBlockText = "*Get-ADUser*" AND ScriptBlockText="*4194304*") | stats count min(_time)  as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | `security_content_ctime(firstTime)` | `disabled_kerberos_pre_authentication_discovery_with_get_aduser_filter`

[ESCU - Disabled Kerberos Pre-Authentication Discovery With PowerView - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` commandlet with specific parameters. `Get-DomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows Active Directory networks. As the name suggests, `Get-DomainUser` is used to identify domain users and combining it with `-PreauthNotRequired` allows adversaries to discover domain accounts with Kerberos Pre Authentication disabled.\ Red Teams and adversaries alike use may leverage PowerView to enumerate these accounts and attempt to crack their passwords offline.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` commandlet with specific parameters. `Get-DomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows Active Directory networks. As the name suggests, `Get-DomainUser` is used to identify domain users and combining it with `-PreauthNotRequired` allows adversaries to discover domain accounts with Kerberos Pre Authentication disabled.\ Red Teams and adversaries alike use may leverage PowerView to enumerate these accounts and attempt to crack their passwords offline.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use PowerView for troubleshooting
action.escu.creation_date = 2022-05-03
action.escu.modification_date = 2022-05-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabled Kerberos Pre-Authentication Discovery With PowerView - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Disabled Kerberos Pre-Authentication Discovery With PowerView from $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabled Kerberos Pre-Authentication Discovery With PowerView - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` commandlet with specific parameters. `Get-DomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows Active Directory networks. As the name suggests, `Get-DomainUser` is used to identify domain users and combining it with `-PreauthNotRequired` allows adversaries to discover domain accounts with Kerberos Pre Authentication disabled.\ Red Teams and adversaries alike use may leverage PowerView to enumerate these accounts and attempt to crack their passwords offline.
action.notable.param.rule_title = Disabled Kerberos Pre-Authentication Discovery With PowerView
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainUser*" AND ScriptBlockText="*PreauthNotRequired*") | stats count min(_time)  as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | `security_content_ctime(firstTime)` | `disabled_kerberos_pre_authentication_discovery_with_powerview_filter`

[ESCU - Disabling CMD Application - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identify modification in registry to disable cmd prompt application. This technique is commonly seen in RAT, Trojan or WORM to prevent triaging or deleting there samples through cmd application which is one of the tool of analyst to traverse on directory and files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identify modification in registry to disable cmd prompt application. This technique is commonly seen in RAT, Trojan or WORM to prevent triaging or deleting there samples through cmd application which is one of the tool of analyst to traverse on directory and files.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may disable this application for non technical user.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling CMD Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = The Windows command prompt was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling CMD Application - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to identify modification in registry to disable cmd prompt application. This technique is commonly seen in RAT, Trojan or WORM to prevent triaging or deleting there samples through cmd application which is one of the tool of analyst to traverse on directory and files.
action.notable.param.rule_title = Disabling CMD Application
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_cmd_application_filter`

[ESCU - Disabling ControlPanel - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to identify registry modification to disable control panel window. This technique is commonly seen in malware to prevent their artifacts , persistence removed on the infected machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to identify registry modification to disable control panel window. This technique is commonly seen in malware to prevent their artifacts , persistence removed on the infected machine.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may disable this application for non technical user.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling ControlPanel - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = The Windows Control Panel was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling ControlPanel - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to identify registry modification to disable control panel window. This technique is commonly seen in malware to prevent their artifacts , persistence removed on the infected machine.
action.notable.param.rule_title = Disabling ControlPanel
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_controlpanel_filter`

[ESCU - Disabling Defender Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This particular behavior is typically executed when an adversaries or malware gains access to an endpoint and beings to perform execution and to evade detections. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This particular behavior is typically executed when an adversaries or malware gains access to an endpoint and beings to perform execution and to evade detections. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable windows defender product
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling Defender Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Windows Registry Abuse", "RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling Defender Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Registry Abuse", "RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This particular behavior is typically executed when an adversaries or malware gains access to an endpoint and beings to perform execution and to evade detections. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.notable.param.rule_title = Disabling Defender Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\System\\CurrentControlSet\\Services\\*" AND (Registry.registry_path IN("*WdBoot*", "*WdFilter*", "*WdNisDrv*", "*WdNisSvc*","*WinDefend*", "*SecurityHealthService*")) AND Registry.registry_value_name = Start Registry.registry_value_data = 0x00000004) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_defender_services_filter`

[ESCU - Disabling Firewall with Netsh - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identifies suspicious firewall disabling using netsh application. this technique is commonly seen in malware that tries to communicate or download its component or other payload to its C2 server.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identifies suspicious firewall disabling using netsh application. this technique is commonly seen in malware that tries to communicate or download its component or other payload to its C2 server.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = admin may disable firewall during testing or fixing network problem.
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling Firewall with Netsh - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = The Windows Firewall was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling Firewall with Netsh - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= "*firewall*" (Processes.process= "*off*" OR  Processes.process= "*disable*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_firewall_with_netsh_filter`

[ESCU - Disabling FolderOptions Windows Feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identify registry modification to disable folder options feature of windows to show hidden files, file extension and etc. This technique used by malware in combination if disabling show hidden files feature to hide their files and also to hide the file extension to lure the user base on file icons or fake file extensions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identify registry modification to disable folder options feature of windows to show hidden files, file extension and etc. This technique used by malware in combination if disabling show hidden files feature to hide their files and also to hide the file extension to lure the user base on file icons or fake file extensions.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may disable this application for non technical user.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling FolderOptions Windows Feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = The Windows Folder Options, to hide files, was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling FolderOptions Windows Feature - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to identify registry modification to disable folder options feature of windows to show hidden files, file extension and etc. This technique used by malware in combination if disabling show hidden files feature to hide their files and also to hide the file extension to lure the user base on file icons or fake file extensions.
action.notable.param.rule_title = Disabling FolderOptions Windows Feature
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_folderoptions_windows_feature_filter`

[ESCU - Disabling Net User Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify a suspicious command-line that disables a user account using the `net.exe` utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1531"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify a suspicious command-line that disables a user account using the `net.exe` utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-05-04
action.escu.modification_date = 2021-05-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling Net User Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified disabling a user account on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 42}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling Net User Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1531"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify a suspicious command-line that disables a user account using the `net.exe` utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act.
action.notable.param.rule_title = Disabling Net User Account
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="*user*" AND Processes.process="*/active:no*" by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_net_user_account_filter`

[ESCU - Disabling NoRun Windows App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identify modification of registry to disable run application in window start menu. this application is known to be a helpful shortcut to windows OS user to run known application and also to execute some reg or batch script. This technique is used malware to make cleaning of its infection more harder by preventing known application run easily through run shortcut.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identify modification of registry to disable run application in window start menu. this application is known to be a helpful shortcut to windows OS user to run known application and also to execute some reg or batch script. This technique is used malware to make cleaning of its infection more harder by preventing known application run easily through run shortcut.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may disable this application for non technical user.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling NoRun Windows App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = The Windows registry was modified to disable run application in window start menu on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling NoRun Windows App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562", "T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to identify modification of registry to disable run application in window start menu. this application is known to be a helpful shortcut to windows OS user to run known application and also to execute some reg or batch script. This technique is used malware to make cleaning of its infection more harder by preventing known application run easily through run shortcut.
action.notable.param.rule_title = Disabling NoRun Windows App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_norun_windows_app_filter`

[ESCU - Disabling Remote User Account Control - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.
action.escu.known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence.
action.escu.creation_date = 2020-11-18
action.escu.modification_date = 2020-11-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling Remote User Account Control - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Remcos", "Windows Registry Abuse", "Azorult", "AgentTesla"]
action.risk = 1
action.risk.param._risk_message = The Windows registry keys that control the enforcement of Windows User Account Control (UAC) were modified on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling Remote User Account Control - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Remcos", "Windows Registry Abuse", "Azorult", "AgentTesla"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).
action.notable.param.rule_title = Disabling Remote User Account Control
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_data="0x00000000" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter`

[ESCU - Disabling SystemRestore In Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following search identifies the modification of registry related in disabling the system restore of a machine. This event or behavior are seen in some RAT malware to make the restore of the infected machine  difficult and keep their infection on the box.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following search identifies the modification of registry related in disabling the system restore of a machine. This event or behavior are seen in some RAT malware to make the restore of the infected machine  difficult and keep their infection on the box.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = in some cases admin can disable systemrestore on a machine.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling SystemRestore In Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = The Windows registry was modified to disable system restore on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling SystemRestore In Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following search identifies the modification of registry related in disabling the system restore of a machine. This event or behavior are seen in some RAT malware to make the restore of the infected machine  difficult and keep their infection on the box.
action.notable.param.rule_title = Disabling SystemRestore In Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows NT\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows NT\\SystemRestore\\DisableConfig" Registry.registry_value_data = "0x00000001") BY _time span=1h  Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_systemrestore_in_registry_filter`

[ESCU - Disabling Task Manager - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to identifies modification of registry to disable the task manager of windows operating system. this event or technique are commonly seen in malware such as RAT, Trojan, TrojanSpy or worm to prevent the user to terminate their process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to identifies modification of registry to disable the task manager of windows operating system. this event or technique are commonly seen in malware such as RAT, Trojan, TrojanSpy or worm to prevent the user to terminate their process.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin may disable this application for non technical user.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling Task Manager - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = The Windows Task Manager was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling Task Manager - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse", "NjRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to identifies modification of registry to disable the task manager of windows operating system. this event or technique are commonly seen in malware such as RAT, Trojan, TrojanSpy or worm to prevent the user to terminate their process.
action.notable.param.rule_title = Disabling Task Manager
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_task_manager_filter`

[ESCU - Disabling Windows Local Security Authority Defences via Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection looks for the deletion of registry keys which disable LSA protection and MS Defender Device Guard.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1556"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This detection looks for the deletion of registry keys which disable LSA protection and MS Defender Device Guard.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Potential to be triggered by an administrator disabling protections for troubleshooting purposes.
action.escu.creation_date = 2022-09-09
action.escu.modification_date = 2022-09-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Disabling Windows Local Security Authority Defences via Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defence Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = An attempt to disable Windows LSA defences was detected on $dest$. The reg key $registry_path$ was deleted by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 60}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Disabling Windows Local Security Authority Defences via Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defence Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 60, "mitre_attack": ["T1556"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection looks for the deletion of registry keys which disable LSA protection and MS Defender Device Guard.
action.notable.param.rule_title = Disabling Windows Local Security Authority Defences via Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\SYSTEM\\CurrentControlSet\\Control\\Lsa\\LsaCfgFlags", "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\DeviceGuard\\*", "*\\SYSTEM\\CurrentControlSet\\Control\\Lsa\\RunAsPPL") Registry.action IN (deleted, unknown) by Registry.action Registry.registry_path Registry.process_guid | `drop_dm_object_name(Registry)` | join type=outer process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`] | table _time action dest user parent_process_name parent_process process_name process process_guid registry_path | `disabling_windows_local_security_authority_defences_via_registry_filter`

[ESCU - DLLHost with no Command Line Arguments with Network - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate third party applications may use a moved copy of dllhost, triggering a false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - DLLHost with no Command Line Arguments with Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $src$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_image", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - DLLHost with no Command Line Arguments with Network - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = DLLHost with no Command Line Arguments with Network
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(dllhost\.exe.{0,4}$)" | rename dest as src | join host process_id [| tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `dllhost_with_no_command_line_arguments_with_network_filter`

[ESCU - DNS Exfiltration Using Nslookup App - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type, TXT, A, AAAA, that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1048"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type, TXT, A, AAAA, that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = admin nslookup usage
action.escu.creation_date = 2021-04-15
action.escu.modification_date = 2021-04-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - DNS Exfiltration Using Nslookup App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious DNS Traffic", "Dynamic DNS", "Data Exfiltration", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing activity related to DNS exfiltration.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 72}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - DNS Exfiltration Using Nslookup App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious DNS Traffic", "Dynamic DNS", "Data Exfiltration", "Command And Control"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1048"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type, TXT, A, AAAA, that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.
action.notable.param.rule_title = DNS Exfiltration Using Nslookup App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="-type=*" OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process_name  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_filter`

[ESCU - Domain Account Discovery with Dsquery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `dsquery.exe` with command-line arguments utilized to discover  domain users. The `user` argument returns a list of all users registered in the domain. Red Teams and adversaries alike engage in remote system discovery for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `dsquery.exe` with command-line arguments utilized to discover  domain users. The `user` argument returns a list of all users registered in the domain. Red Teams and adversaries alike engage in remote system discovery for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Account Discovery with Dsquery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Account Discovery with Dsquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="dsquery.exe" AND Processes.process = "*user*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_dsquery_filter`

[ESCU - Domain Account Discovery With Net App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to query for domain users. Red Teams and adversaries alike may use net.exe to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to query for domain users. Red Teams and adversaries alike may use net.exe to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Account Discovery With Net App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = an instance of process $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Account Discovery With Net App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to query for domain users. Red Teams and adversaries alike may use net.exe to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Domain Account Discovery With Net App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process = "* user*" AND Processes.process = "*/do*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_net_app_filter`

[ESCU - Domain Account Discovery with Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for domain users. Red Teams and adversaries alike use wmic.exe to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for domain users. Red Teams and adversaries alike use wmic.exe to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Account Discovery with Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = an instance of process $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Account Discovery with Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for domain users. Red Teams and adversaries alike use wmic.exe to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Domain Account Discovery with Wmic
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="wmic.exe" AND Processes.process = "*/NAMESPACE:\\\\root\\directory\\ldap*" AND Processes.process = "*ds_user*" AND Processes.process = "*GET*" AND Processes.process = "*ds_samaccountname*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_wmic_filter`

[ESCU - Domain Controller Discovery with Nltest - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `nltest.exe` with command-line arguments utilized to discover remote systems. The arguments `/dclist:` and '/dsgetdc:', can be used to return a list of all domain controllers. Red Teams and adversaries alike may use nltest.exe to identify domain controllers in a Windows Domain for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `nltest.exe` with command-line arguments utilized to discover remote systems. The arguments `/dclist:` and '/dsgetdc:', can be used to return a list of all domain controllers. Red Teams and adversaries alike may use nltest.exe to identify domain controllers in a Windows Domain for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-30
action.escu.modification_date = 2021-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Controller Discovery with Nltest - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain controller discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 21}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Controller Discovery with Nltest - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `nltest.exe` with command-line arguments utilized to discover remote systems. The arguments `/dclist:` and '/dsgetdc:', can be used to return a list of all domain controllers. Red Teams and adversaries alike may use nltest.exe to identify domain controllers in a Windows Domain for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Domain Controller Discovery with Nltest
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="nltest.exe") (Processes.process="*/dclist:*" OR Processes.process="*/dsgetdc:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_nltest_filter`

[ESCU - Domain Controller Discovery with Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to discover remote systems. The arguments utilized in this command line return a list of all domain controllers in a Windows domain. Red Teams and adversaries alike use *.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to discover remote systems. The arguments utilized in this command line return a list of all domain controllers in a Windows domain. Red Teams and adversaries alike use *.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Controller Discovery with Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Controller Discovery with Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process="" OR Processes.process="*DomainControllerAddress*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_wmic_filter`

[ESCU - Domain Group Discovery with Adsisearcher - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use Adsisearcher for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Group Discovery with Adsisearcher - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain group discovery enumeration using PowerShell on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Group Discovery with Adsisearcher - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 60, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Domain Group Discovery with Adsisearcher
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (Message = "*[adsisearcher]*" AND Message = "*(objectcategory=group)*" AND Message = "*findAll()*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | rename ComputerName as dest |rename User as user | `security_content_ctime(firstTime)` | `domain_group_discovery_with_adsisearcher_filter`

[ESCU - Domain Group Discovery With Dsquery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `dsquery.exe` with command-line arguments utilized to query for domain groups. The argument `group`, returns a list of all domain groups. Red Teams and adversaries alike use may leverage dsquery.exe to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `dsquery.exe` with command-line arguments utilized to query for domain groups. The argument `group`, returns a list of all domain groups. Red Teams and adversaries alike use may leverage dsquery.exe to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Group Discovery With Dsquery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Group Discovery With Dsquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe") (Processes.process="*group*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_dsquery_filter`

[ESCU - Domain Group Discovery With Net - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` with command-line arguments utilized to query for domain groups. The argument `group /domain`, returns a list of all domain groups. Red Teams and adversaries alike use net.exe to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` with command-line arguments utilized to query for domain groups. The argument `group /domain`, returns a list of all domain groups. Red Teams and adversaries alike use net.exe to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Group Discovery With Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Active Directory Discovery", "Prestige Ransomware", "Graceful Wipe Out Attack"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Group Discovery With Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Active Directory Discovery", "Prestige Ransomware", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process=*group* AND Processes.process=*/do*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_net_filter`

[ESCU - Domain Group Discovery With Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for domain groups. The arguments utilized in this command return a list of all domain groups. Red Teams and adversaries alike use wmic.exe to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for domain groups. The arguments utilized in this command return a list of all domain groups. Red Teams and adversaries alike use wmic.exe to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Domain Group Discovery With Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Domain Group Discovery With Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_group* AND Processes.process="*GET ds_samaccountname*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_wmic_filter`

[ESCU - Download Files Using Telegram - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic will identify a suspicious download by the Telegram application on a Windows system. This behavior was identified on a honeypot where the adversary gained access, installed Telegram and followed through with downloading different network scanners (port, bruteforcer, masscan) to the system and later used to mapped the whole network and further move laterally.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic will identify a suspicious download by the Telegram application on a Windows system. This behavior was identified on a honeypot where the adversary gained access, installed Telegram and followed through with downloading different network scanners (port, bruteforcer, masscan) to the system and later used to mapped the whole network and further move laterally.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and TargetFilename from your endpoints or Events that monitor filestream events which is happened when process download something. (EventCode 15) If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = normal download of file in telegram app. (if it was a common app in network)
action.escu.creation_date = 2021-05-06
action.escu.modification_date = 2021-05-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Download Files Using Telegram - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = Suspicious files were downloaded with the Telegram application on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Download Files Using Telegram - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic will identify a suspicious download by the Telegram application on a Windows system. This behavior was identified on a honeypot where the adversary gained access, installed Telegram and followed through with downloading different network scanners (port, bruteforcer, masscan) to the system and later used to mapped the whole network and further move laterally.
action.notable.param.rule_title = Download Files Using Telegram
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" |stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode process_name process_id TargetFilename Hash | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `download_files_using_telegram_filter`

[ESCU - Drop IcedID License dat - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-07-30
action.escu.modification_date = 2021-07-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Drop IcedID License dat - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Drop IcedID License dat - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1204", "T1204.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode= 11  TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter`

[ESCU - DSQuery Domain Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\
Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\
The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\
DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\
The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\
In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\
Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\
The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\
DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\
The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\
In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives. If there is a true false positive, filter based on command-line or parent process.
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - DSQuery Domain Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Domain Trust Discovery", "Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified performing domain discovery on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 72}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - DSQuery Domain Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Domain Trust Discovery", "Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\
Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\
The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\
DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\
The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\
In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used.
action.notable.param.rule_title = DSQuery Domain Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dsquery.exe Processes.process=*trustedDomain* by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dsquery_domain_discovery_filter`

[ESCU - Dump LSASS via comsvcs DLL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the behavior of dumping credentials from memory, a tactic commonly used by adversaries to exploit the Local Security Authority Subsystem Service (LSASS) in Windows, which manages system-level authentication. The detection is made by monitoring logs with process information from endpoints and identifying instances where the rundll32 process is used in conjunction with the comsvcs.dll and MiniDump. This indicates potential LSASS dumping attempts used by threat actors to obtain valuable credentials. The detection is important because credential theft can lead to broader system compromise, persistence, lateral movement, and escalated privileges. No legitimate use of this technique has been identified yet. This behavior is often part of more extensive attack campaigns and is associated with numerous threat groups that use the stolen credentials to access sensitive information or systems, leading to data theft, ransomware attacks, or other damaging outcomes. False positives can occur since legitimate uses of the LSASS process can cause benign activities to be flagged. Next steps include reviewing the processes involved in the LSASS dumping attempt after triage and inspecting any relevant on-disk artifacts and concurrent processes to identify the attack source.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the behavior of dumping credentials from memory, a tactic commonly used by adversaries to exploit the Local Security Authority Subsystem Service (LSASS) in Windows, which manages system-level authentication. The detection is made by monitoring logs with process information from endpoints and identifying instances where the rundll32 process is used in conjunction with the comsvcs.dll and MiniDump. This indicates potential LSASS dumping attempts used by threat actors to obtain valuable credentials. The detection is important because credential theft can lead to broader system compromise, persistence, lateral movement, and escalated privileges. No legitimate use of this technique has been identified yet. This behavior is often part of more extensive attack campaigns and is associated with numerous threat groups that use the stolen credentials to access sensitive information or systems, leading to data theft, ransomware attacks, or other damaging outcomes. False positives can occur since legitimate uses of the LSASS process can cause benign activities to be flagged. Next steps include reviewing the processes involved in the LSASS dumping attempt after triage and inspecting any relevant on-disk artifacts and concurrent processes to identify the attack source.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Dump LSASS via comsvcs DLL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Industroyer2", "HAFNIUM Group", "CISA AA22-264A", "Prestige Ransomware", "Credential Dumping", "CISA AA22-257A", "Living Off The Land", "Suspicious Rundll32 Activity", "Data Destruction", "Volt Typhoon", "Flax Typhoon"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified accessing credentials using comsvcs.dll on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Dump LSASS via comsvcs DLL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Industroyer2", "HAFNIUM Group", "CISA AA22-264A", "Prestige Ransomware", "Credential Dumping", "CISA AA22-257A", "Living Off The Land", "Suspicious Rundll32 Activity", "Data Destruction", "Volt Typhoon", "Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the behavior of dumping credentials from memory, a tactic commonly used by adversaries to exploit the Local Security Authority Subsystem Service (LSASS) in Windows, which manages system-level authentication. The detection is made by monitoring logs with process information from endpoints and identifying instances where the rundll32 process is used in conjunction with the comsvcs.dll and MiniDump. This indicates potential LSASS dumping attempts used by threat actors to obtain valuable credentials. The detection is important because credential theft can lead to broader system compromise, persistence, lateral movement, and escalated privileges. No legitimate use of this technique has been identified yet. This behavior is often part of more extensive attack campaigns and is associated with numerous threat groups that use the stolen credentials to access sensitive information or systems, leading to data theft, ransomware attacks, or other damaging outcomes. False positives can occur since legitimate uses of the LSASS process can cause benign activities to be flagged. Next steps include reviewing the processes involved in the LSASS dumping attempt after triage and inspecting any relevant on-disk artifacts and concurrent processes to identify the attack source.
action.notable.param.rule_title = Dump LSASS via comsvcs DLL
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter`

[ESCU - Dump LSASS via procdump - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (original_file_name=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (original_file_name=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2022-08-31
action.escu.modification_date = 2022-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Dump LSASS via procdump - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "HAFNIUM Group", "CISA AA22-257A"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified attempting to dump lsass.exe on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Dump LSASS via procdump - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group", "CISA AA22-257A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (original_file_name=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
action.notable.param.rule_title = Dump LSASS via procdump
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_procdump` (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter`

[ESCU - Elevated Group Discovery With Net - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` or `net1.exe`  with command-line arguments utilized to query for specific elevated domain groups. Red Teams and adversaries alike use net.exe to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` or `net1.exe`  with command-line arguments utilized to query for specific elevated domain groups. Red Teams and adversaries alike use net.exe to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Elevated Group Discovery With Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Elevated domain group discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 21}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Elevated Group Discovery With Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `net.exe` or `net1.exe`  with command-line arguments utilized to query for specific elevated domain groups. Red Teams and adversaries alike use net.exe to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.notable.param.rule_title = Elevated Group Discovery With Net
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process="*group*" AND Processes.process="*/do*") (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns Admins*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_net_filter`

[ESCU - Elevated Group Discovery with PowerView - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainGroupMember` commandlet. `Get-DomainGroupMember` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. As the name suggests, `Get-DomainGroupMember` is used to list the members of an specific domain group. Red Teams and adversaries alike use PowerView to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainGroupMember` commandlet. `Get-DomainGroupMember` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. As the name suggests, `Get-DomainGroupMember` is used to list the members of an specific domain group. Red Teams and adversaries alike use PowerView to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerView for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Elevated Group Discovery with PowerView - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Elevated Group Discovery with PowerView - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (Message = "*Get-DomainGroupMember*") AND Message IN ("*Domain Admins*","*Enterprise Admins*", "*Schema Admins*", "*Account Operators*" , "*Server Operators*", "*Protected Users*",  "*Dns Admins*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | rename ComputerName as dest, User as user | `security_content_ctime(firstTime)` | `elevated_group_discovery_with_powerview_filter`

[ESCU - Elevated Group Discovery With Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for specific domain groups. Red Teams and adversaries alike use net.exe to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for specific domain groups. Red Teams and adversaries alike use net.exe to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Elevated Group Discovery With Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Elevated domain group discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 21}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Elevated Group Discovery With Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for specific domain groups. Red Teams and adversaries alike use net.exe to enumerate elevated domain groups for situational awareness and Active Directory Discovery to identify high privileged users.
action.notable.param.rule_title = Elevated Group Discovery With Wmic
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap*) (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns Admins*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_wmic_filter`

[ESCU - Enable RDP In Other Port Number - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a modification to registry to enable rdp to a machine with different port number. This technique was seen in some atttacker tries to do lateral movement and remote access to a compromised machine to gain control of it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a modification to registry to enable rdp to a machine with different port number. This technique was seen in some atttacker tries to do lateral movement and remote access to a compromised machine to gain control of it.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Enable RDP In Other Port Number - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = RDP was moved to a non-standard port on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Enable RDP In Other Port Number - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a modification to registry to enable rdp to a machine with different port number. This technique was seen in some atttacker tries to do lateral movement and remote access to a compromised machine to gain control of it.
action.notable.param.rule_title = Enable RDP In Other Port Number
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enable_rdp_in_other_port_number_filter`

[ESCU - Enable WDigest UseLogonCredential Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to enable plain text credential feature of windows. This technique was used by several malware and also by mimikatz to be able to dumpe the a plain text credential to the compromised or target host. This TTP is really a good indicator that someone wants to dump the crendential of the host so it must be a good pivot for credential dumping techniques.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to enable plain text credential feature of windows. This technique was used by several malware and also by mimikatz to be able to dumpe the a plain text credential to the compromised or target host. This TTP is really a good indicator that someone wants to dump the crendential of the host so it must be a good pivot for credential dumping techniques.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Enable WDigest UseLogonCredential Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "Windows Registry Abuse", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = wdigest registry $registry_path$ was modified in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Enable WDigest UseLogonCredential Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Windows Registry Abuse", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1112", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious registry modification to enable plain text credential feature of windows. This technique was used by several malware and also by mimikatz to be able to dumpe the a plain text credential to the compromised or target host. This TTP is really a good indicator that someone wants to dump the crendential of the host so it must be a good pivot for credential dumping techniques.
action.notable.param.rule_title = Enable WDigest UseLogonCredential Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SecurityProviders\\WDigest\\*" Registry.registry_value_name = "UseLogonCredential" Registry.registry_value_data=0x00000001) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enable_wdigest_uselogoncredential_registry_filter`

[ESCU - Enumerate Users Local Group Using Telegram - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-05-06
action.escu.modification_date = 2021-05-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Enumerate Users Local Group Using Telegram - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = The Telegram application has been identified enumerating local groups on $ComputerName$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Enumerate Users Local Group Using Telegram - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device.
action.notable.param.rule_title = Enumerate Users Local Group Using Telegram
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4798  Process_Name = "*\\telegram.exe" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Process_Name  Process_ID Account_Name Account_Domain Logon_ID Security_ID Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enumerate_users_local_group_using_telegram_filter`

[ESCU - Esentutl SAM Copy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the process - `esentutl.exe` - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the process - `esentutl.exe` - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited. Filter as needed.
action.escu.creation_date = 2021-08-18
action.escu.modification_date = 2021-08-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Esentutl SAM Copy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Esentutl SAM Copy - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_esentutl` Processes.process IN ("*ntds*", "*SAM*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esentutl_sam_copy_filter`

[ESCU - ETW Registry Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a registry modification to disable ETW feature of windows. This technique is to evade EDR appliance to evade detections and hide its execution from audit logs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.006", "T1127", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a registry modification to disable ETW feature of windows. This technique is to evade EDR appliance to evade detections and hide its execution from audit logs.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - ETW Registry Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hermetic Wiper", "Windows Privilege Escalation", "Windows Persistence Techniques", "Windows Registry Abuse", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ETW Registry Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Windows Privilege Escalation", "Windows Persistence Techniques", "Windows Registry Abuse", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1562.006", "T1127", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a registry modification to disable ETW feature of windows. This technique is to evade EDR appliance to evade detections and hide its execution from audit logs.
action.notable.param.rule_title = ETW Registry Disabled
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\Microsoft\\.NETFramework*" Registry.registry_value_name = ETWEnabled Registry.registry_value_data=0x00000000) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `etw_registry_disabled_filter`

[ESCU - Eventvwr UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some false positives may be present and will need to be filtered.
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Eventvwr UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "IcedID", "Living Off The Land", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Registry values were modified to bypass UAC using Event Viewer on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Eventvwr UAC Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "IcedID", "Living Off The Land", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary.
action.notable.param.rule_title = Eventvwr UAC Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*mscfile\\shell\\open\\command\\*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `eventvwr_uac_bypass_filter`

[ESCU - Excel Spawning PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, but if any are present, filter as needed.
action.escu.creation_date = 2021-04-12
action.escu.modification_date = 2021-04-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excel Spawning PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$, indicating potential suspicious macro execution.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excel Spawning PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
action.notable.param.rule_title = Excel Spawning PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" `process_powershell` by Processes.parent_process Processes.process_name Processes.user Processes.dest Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_powershell_filter`

[ESCU - Excel Spawning Windows Script Host - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies Microsoft Excel spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64`. `cscript.exe` or `wscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies Microsoft Excel spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64`. `cscript.exe` or `wscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, but if any are present, filter as needed. In some instances, `cscript.exe` is used for legitimate business practices.
action.escu.creation_date = 2021-04-12
action.escu.modification_date = 2021-04-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excel Spawning Windows Script Host - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$, indicating potential suspicious macro execution.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excel Spawning Windows Script Host - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies Microsoft Excel spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64`. `cscript.exe` or `wscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
action.notable.param.rule_title = Excel Spawning Windows Script Host
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe")  by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_windows_script_host_filter`

[ESCU - Excessive Attempt To Disable Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-05-04
action.escu.modification_date = 2021-05-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Attempt To Disable Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Azorult"]
action.risk = 1
action.risk.param._risk_message = An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Attempt To Disable Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Azorult"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter`

[ESCU - Excessive distinct processes from Windows Temp - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious series of process executions.  We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective.  This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify suspicious series of process executions.  We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective.  This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold.  Filter as needed.
action.escu.creation_date = 2022-02-28
action.escu.modification_date = 2022-02-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive distinct processes from Windows Temp - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Meterpreter"]
action.risk = 1
action.risk.param._risk_message = Multiple processes were executed out of windows\temp within a short amount of time on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive distinct processes from Windows Temp - Rule
action.correlationsearch.annotations = {"analytic_story": ["Meterpreter"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user  _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`

[ESCU - Excessive File Deletion In WinDefender Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify excessive file deletion events in the Windows Defender folder. This technique was seen in the WhisperGate malware campaign in which adversaries abused Nirsofts advancedrun.exe to gain administrative privilege to then execute PowerShell commands to delete files within the Windows Defender application folder. This behavior is a good indicator the offending process is trying to corrupt a Windows Defender installation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will identify excessive file deletion events in the Windows Defender folder. This technique was seen in the WhisperGate malware campaign in which adversaries abused Nirsofts advancedrun.exe to gain administrative privilege to then execute PowerShell commands to delete files within the Windows Defender application folder. This behavior is a good indicator the offending process is trying to corrupt a Windows Defender installation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, TargetFilename, and ProcessID executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Windows Defender AV updates may cause this alert. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive File Deletion In WinDefender Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "WhisperGate", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = High frequency file deletion activity detected on host $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "deleted_files", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive File Deletion In WinDefender Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "WhisperGate", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify excessive file deletion events in the Windows Defender folder. This technique was seen in the WhisperGate malware campaign in which adversaries abused Nirsofts advancedrun.exe to gain administrative privilege to then execute PowerShell commands to delete files within the Windows Defender application folder. This behavior is a good indicator the offending process is trying to corrupt a Windows Defender installation.
action.notable.param.rule_title = Excessive File Deletion In WinDefender Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=23 TargetFilename = "*\\ProgramData\\Microsoft\\Windows Defender*" | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by user EventCode Image ProcessID dest |where count >=50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_file_deletion_in_windefender_folder_filter`

[ESCU - Excessive number of service control start as disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection targets behaviors observed when threat actors have used sc.exe to modify services. We observed malware in a honey pot spawning numerous sc.exe processes in a short period of time, presumably to impair defenses, possibly to block others from compromising the same machine.  This detection will alert when we see both an excessive number of sc.exe processes launched with specific commandline arguments to disable the start of certain services.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This detection targets behaviors observed when threat actors have used sc.exe to modify services. We observed malware in a honey pot spawning numerous sc.exe processes in a short period of time, presumably to impair defenses, possibly to block others from compromising the same machine.  This detection will alert when we see both an excessive number of sc.exe processes launched with specific commandline arguments to disable the start of certain services.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate programs and administrators will execute sc.exe with the start disabled flag.  It is possible, but unlikely from the telemetry of normal Windows operation we observed, that sc.exe will be called more than seven times in a short period of time.
action.escu.creation_date = 2021-06-25
action.escu.modification_date = 2021-06-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive number of service control start as disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive number of service control start as disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_cmdlines values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_id, _time span=30m | where distinct_cmdlines >= 8 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_service_control_start_as_disabled_filter`

[ESCU - Excessive number of taskhost processes - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection targets behaviors observed in post exploit kits like Meterpreter and Koadic that are run in memory.  We have observed that these tools must invoke an excessive number of taskhost.exe and taskhostex.exe processes to complete various actions (discovery, lateral movement, etc.).  It is extremely uncommon in the course of normal operations to see so many distinct taskhost and taskhostex processes running concurrently in a short time frame.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This detection targets behaviors observed in post exploit kits like Meterpreter and Koadic that are run in memory.  We have observed that these tools must invoke an excessive number of taskhost.exe and taskhostex.exe processes to complete various actions (discovery, lateral movement, etc.).  It is extremely uncommon in the course of normal operations to see so many distinct taskhost and taskhostex processes running concurrently in a short time frame.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators, administrative actions or certain applications may run many instances of taskhost and taskhostex concurrently.  Filter as needed.
action.escu.creation_date = 2021-06-07
action.escu.modification_date = 2021-06-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive number of taskhost processes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Meterpreter"]
action.risk = 1
action.risk.param._risk_message = An excessive amount of $process_name$ was executed on $dest$ indicative of suspicious behavior.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive number of taskhost processes - Rule
action.correlationsearch.annotations = {"analytic_story": ["Meterpreter"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_ids  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" BY Processes.dest Processes.process_name _time span=1h | `drop_dm_object_name(Processes)` | eval pid_count=mvcount(process_ids) | eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) | eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count by _time, dest, firstTime, lastTime | where taskhost_count > 10 and taskhostex_count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_taskhost_processes_filter`

[ESCU - Excessive Service Stop Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious series of attempt to kill multiple services on a system using either `net.exe` or `sc.exe`. This technique is use by adversaries to terminate security services or other related services to continue there objective and evade detections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies suspicious series of attempt to kill multiple services on a system using either `net.exe` or `sc.exe`. This technique is use by adversaries to terminate security services or other related services to continue there objective and evade detections.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-05-04
action.escu.modification_date = 2021-05-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Service Stop Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Ransomware", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Service Stop Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Ransomware", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name = "sc.exe" OR  Processes.process_name = "net1.exe" AND Processes.process="*stop*" OR Processes.process="*delete*" by Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_service_stop_attempt_filter`

[ESCU - Excessive Usage Of Cacls App - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe` or `icacls.exe` application to change file or folder permission. This behavior is commonly seen where the adversary attempts to impair some users from deleting or accessing its malware components or artifact from the compromised system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe` or `icacls.exe` application to change file or folder permission. This behavior is commonly seen where the adversary attempts to impair some users from deleting or accessing its malware components or artifact from the compromised system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or administrative scripts may use this application. Filter as needed.
action.escu.creation_date = 2021-05-07
action.escu.modification_date = 2021-05-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Usage Of Cacls App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Azorult", "Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = An excessive amount of $process_name$ was executed on $dest$ attempting to modify permissions.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Usage Of Cacls App - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Azorult", "Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1222"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id  values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_cacls_app_filter`

[ESCU - Excessive Usage Of Net App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies excessive usage of `net.exe` or `net1.exe` within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1531"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies excessive usage of `net.exe` or `net1.exe` within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown. Filter as needed. Modify the time span as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Usage Of Net App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Prestige Ransomware", "Graceful Wipe Out Attack", "XMRig", "Windows Post-Exploitation", "Azorult", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 28}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Usage Of Net App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prestige Ransomware", "Graceful Wipe Out Attack", "XMRig", "Windows Post-Exploitation", "Azorult", "Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1531"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` by Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_net_app_filter`

[ESCU - Excessive Usage of NSLOOKUP App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type (TXT, A, AAAA) that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1048"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type (TXT, A, AAAA) that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of nslookup.exe may be used.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-06-03
action.escu.modification_date = 2022-06-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Usage of NSLOOKUP App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious DNS Traffic", "Dynamic DNS", "Data Exfiltration", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = Excessive usage of nslookup.exe has been detected on $dest$. This detection is triggered as as it violates the dynamic threshold
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Usage of NSLOOKUP App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious DNS Traffic", "Dynamic DNS", "Data Exfiltration", "Command And Control"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1048"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode = 1 process_name = "nslookup.exe" | bucket _time span=1m | stats count as numNsLookup by dest, _time | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by dest | eval upperThreshold=(avgNsLookup + stdNsLookup *3) | eval isOutlier=if(numNsLookup > 20 and numNsLookup >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_nslookup_app_filter`

[ESCU - Excessive Usage Of SC Service Utility - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious excessive usage of sc.exe in a host machine. This technique was seen in several ransomware , xmrig and other malware to create, modify, delete or disable a service may related to security application or to gain privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious excessive usage of sc.exe in a host machine. This technique was seen in several ransomware , xmrig and other malware to create, modify, delete or disable a service may related to security application or to gain privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used.
action.escu.known_false_positives = excessive execution of sc.exe is quite suspicious since it can modify or execute app in high privilege permission.
action.escu.creation_date = 2021-06-24
action.escu.modification_date = 2021-06-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Usage Of SC Service Utility - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ransomware", "Azorult"]
action.risk = 1
action.risk.param._risk_message = Excessive Usage Of SC Service Utility
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Usage Of SC Service Utility - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Azorult"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode = 1 process_name = "sc.exe" |  bucket _time span=15m | stats values(process) as process count as numScExe by dest, _time |  eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by dest |  eval upperThreshold=(avgScExe + stdScExe *3) |  eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0) |  search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_sc_service_utility_filter`

[ESCU - Excessive Usage Of Taskkill - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies excessive usage of `taskkill.exe` application. This application is commonly used by adversaries to evade detections by killing security product processes or even other processes to evade detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies excessive usage of `taskkill.exe` application. This application is commonly used by adversaries to evade detections by killing security product processes or even other processes to evade detection.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. Filter as needed.
action.escu.creation_date = 2021-05-04
action.escu.modification_date = 2021-05-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive Usage Of Taskkill - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Azorult", "CISA AA22-264A", "AgentTesla", "CISA AA22-277A", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Excessive usage  of taskkill.exe with process id $process_id$ (more than 10 within 1m) has been detected on $dest$ with a parent process of $parent_process_name$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}, {"threat_object_field": "parent_process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive Usage Of Taskkill - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Azorult", "CISA AA22-264A", "AgentTesla", "CISA AA22-277A", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe"  by Processes.parent_process_name Processes.process_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_taskkill_filter`

[ESCU - Exchange PowerShell Abuse via SSRF - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious behavior related to ProxyShell against on-premise Microsoft Exchange servers. This analytic has been replaced by GUID d436f9e7-0ee7-4a47-864b-6dea2c4e2752 which utilizes the Web Datamodel. \
Modification of this analytic is requried to ensure fields are mapped accordingly. \
A suspicious event will have `PowerShell`, the method `POST` and `autodiscover.json`. This is indicative of accessing PowerShell on the back end of Exchange with SSRF. \
An event will look similar to `POST /autodiscover/autodiscover.json a=dsxvu@fnsso.flq/powershell/?X-Rps-CAT=VgEAVAdXaW5kb3d...` (abbreviated) \
Review the source attempting to perform this activity against your environment. In addition, review PowerShell logs and access recently granted to Exchange roles.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies suspicious behavior related to ProxyShell against on-premise Microsoft Exchange servers. This analytic has been replaced by GUID d436f9e7-0ee7-4a47-864b-6dea2c4e2752 which utilizes the Web Datamodel. \
Modification of this analytic is requried to ensure fields are mapped accordingly. \
A suspicious event will have `PowerShell`, the method `POST` and `autodiscover.json`. This is indicative of accessing PowerShell on the back end of Exchange with SSRF. \
An event will look similar to `POST /autodiscover/autodiscover.json a=dsxvu@fnsso.flq/powershell/?X-Rps-CAT=VgEAVAdXaW5kb3d...` (abbreviated) \
Review the source attempting to perform this activity against your environment. In addition, review PowerShell logs and access recently granted to Exchange roles.
action.escu.how_to_implement = The following analytic requires on-premise Exchange to be logging to Splunk using the TA - https://splunkbase.splunk.com/app/3225. Ensure logs are parsed correctly, or tune the analytic for your environment.
action.escu.known_false_positives = Limited false positives, however, tune as needed.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Exchange PowerShell Abuse via SSRF - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"]
action.risk = 1
action.risk.param._risk_message = Activity related to ProxyShell has been identified on $dest$. Review events and take action accordingly.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Exchange PowerShell Abuse via SSRF - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies suspicious behavior related to ProxyShell against on-premise Microsoft Exchange servers. This analytic has been replaced by GUID d436f9e7-0ee7-4a47-864b-6dea2c4e2752 which utilizes the Web Datamodel. \
Modification of this analytic is requried to ensure fields are mapped accordingly. \
A suspicious event will have `PowerShell`, the method `POST` and `autodiscover.json`. This is indicative of accessing PowerShell on the back end of Exchange with SSRF. \
An event will look similar to `POST /autodiscover/autodiscover.json a=dsxvu@fnsso.flq/powershell/?X-Rps-CAT=VgEAVAdXaW5kb3d...` (abbreviated) \
Review the source attempting to perform this activity against your environment. In addition, review PowerShell logs and access recently granted to Exchange roles.
action.notable.param.rule_title = Exchange PowerShell Abuse via SSRF
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `exchange` c_uri="*//autodiscover*" cs_uri_query="*PowerShell*" cs_method="POST" | stats count min(_time) as firstTime max(_time) as lastTime by dest, cs_uri_query, cs_method, c_uri | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exchange_powershell_abuse_via_ssrf_filter`

[ESCU - Exchange PowerShell Module Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the usage of Exchange PowerShell modules that were recently used for a proof of concept related to ProxyShell. Adversaries may abuse a limited set of PwSh Modules related to Exchange once gained access via ProxyShell or ProxyNotShell.\
Inherently, the usage of the modules is not malicious, but reviewing parallel processes, and user, of the session will assist with determining the intent. \
Module - New-MailboxExportRequest will begin the process of exporting contents of a primary mailbox or archive to a .pst file. \
Module - New-managementroleassignment can assign a management role to a management role group, management role assignment policy, user, or universal security group (USG). \
Module - New-MailboxSearch cmdlet to create a mailbox search and either get an estimate of search results, place search results on In-Place Hold or copy them to a Discovery mailbox. You can also place all contents in a mailbox on hold by not specifying a search query, which accomplishes similar results as Litigation Hold. \ Module - Get-Recipient cmdlet to view existing recipient objects in your organization. This cmdlet returns all mail-enabled objects (for example, mailboxes, mail users, mail contacts, and distribution groups).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the usage of Exchange PowerShell modules that were recently used for a proof of concept related to ProxyShell. Adversaries may abuse a limited set of PwSh Modules related to Exchange once gained access via ProxyShell or ProxyNotShell.\
Inherently, the usage of the modules is not malicious, but reviewing parallel processes, and user, of the session will assist with determining the intent. \
Module - New-MailboxExportRequest will begin the process of exporting contents of a primary mailbox or archive to a .pst file. \
Module - New-managementroleassignment can assign a management role to a management role group, management role assignment policy, user, or universal security group (USG). \
Module - New-MailboxSearch cmdlet to create a mailbox search and either get an estimate of search results, place search results on In-Place Hold or copy them to a Discovery mailbox. You can also place all contents in a mailbox on hold by not specifying a search query, which accomplishes similar results as Litigation Hold. \ Module - Get-Recipient cmdlet to view existing recipient objects in your organization. This cmdlet returns all mail-enabled objects (for example, mailboxes, mail users, mail contacts, and distribution groups).
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Exchange PowerShell Module Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["ProxyNotShell", "CISA AA22-277A", "ProxyShell", "BlackByte Ransomware", "CISA AA22-264A"]
action.risk = 1
action.risk.param._risk_message = Suspicious Exchange PowerShell module usaged was identified on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 32}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Exchange PowerShell Module Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyNotShell", "CISA AA22-277A", "ProxyShell", "BlackByte Ransomware", "CISA AA22-264A"], "cis20": ["CIS 10"], "confidence": 80, "impact": 40, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the usage of Exchange PowerShell modules that were recently used for a proof of concept related to ProxyShell. Adversaries may abuse a limited set of PwSh Modules related to Exchange once gained access via ProxyShell or ProxyNotShell.\
Inherently, the usage of the modules is not malicious, but reviewing parallel processes, and user, of the session will assist with determining the intent. \
Module - New-MailboxExportRequest will begin the process of exporting contents of a primary mailbox or archive to a .pst file. \
Module - New-managementroleassignment can assign a management role to a management role group, management role assignment policy, user, or universal security group (USG). \
Module - New-MailboxSearch cmdlet to create a mailbox search and either get an estimate of search results, place search results on In-Place Hold or copy them to a Discovery mailbox. You can also place all contents in a mailbox on hold by not specifying a search query, which accomplishes similar results as Litigation Hold. \ Module - Get-Recipient cmdlet to view existing recipient objects in your organization. This cmdlet returns all mail-enabled objects (for example, mailboxes, mail users, mail contacts, and distribution groups).
action.notable.param.rule_title = Exchange PowerShell Module Usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "Search-Mailbox") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exchange_powershell_module_usage_filter`

[ESCU - Executable File Written in Administrative SMB Share - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The Trickbot malware family also implements this behavior to try to infect other machines in the infected network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The Trickbot malware family also implements this behavior to try to infect other machines in the infected network.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.
action.escu.known_false_positives = System Administrators may use looks like PsExec for troubleshooting or administrations tasks. However, this will typically come only from certain users and certain systems that can be added to an allow list.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Executable File Written in Administrative SMB Share - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Prestige Ransomware", "Graceful Wipe Out Attack", "Industroyer2", "IcedID", "Data Destruction", "Hermetic Wiper", "Trickbot"]
action.risk = 1
action.risk.param._risk_message = $user$ dropped or created an executable file in known sensitive SMB share.  Share name=$Share_Name$, Target name=$Relative_Target_Name$, and Access mask=$Access_Mask$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Executable File Written in Administrative SMB Share - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Prestige Ransomware", "Graceful Wipe Out Attack", "Industroyer2", "IcedID", "Data Destruction", "Hermetic Wiper", "Trickbot"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1021", "T1021.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The Trickbot malware family also implements this behavior to try to infect other machines in the infected network.
action.notable.param.rule_title = Executable File Written in Administrative SMB Share
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.exe","*.dll") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") Access_Mask= "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode Share_Name Relative_Target_Name Object_Type Access_Mask user src_port Source_Address | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `executable_file_written_in_administrative_smb_share_filter`

[ESCU - Executables Or Script Creation In Suspicious Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies potentially malicious executables or scripts by examining a list of suspicious file paths on Windows Operating System. The purpose of this technique is to uncover files with known file extensions that could be used by adversaries to evade detection and persistence. The suspicious file paths selected for investigation are typically uncommon and uncommonly associated with executable or script files. By scrutinizing these paths, we can proactively identify potential security threats and enhance overall system security.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies potentially malicious executables or scripts by examining a list of suspicious file paths on Windows Operating System. The purpose of this technique is to uncover files with known file extensions that could be used by adversaries to evade detection and persistence. The suspicious file paths selected for investigation are typically uncommon and uncommonly associated with executable or script files. By scrutinizing these paths, we can proactively identify potential security threats and enhance overall system security.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = Administrators may allow creation of script or exe in the paths specified. Filter as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Executables Or Script Creation In Suspicious Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["WhisperGate", "XMRig", "Industroyer2", "Remcos", "Data Destruction", "Hermetic Wiper", "Azorult", "DarkCrystal RAT", "Graceful Wipe Out Attack", "IcedID", "Swift Slicer", "Qakbot", "RedLine Stealer", "Brute Ratel C4", "AsyncRAT", "LockBit Ransomware", "AgentTesla", "Double Zero Destructor", "Volt Typhoon", "Chaos Ransomware", "Trickbot", "Amadey", "BlackByte Ransomware", "Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Suspicious executable or scripts with file name $file_name$, $file_path$ and process_id $process_id$ executed in suspicious file path in Windows by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 20}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Executables Or Script Creation In Suspicious Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["WhisperGate", "XMRig", "Industroyer2", "Remcos", "Data Destruction", "Hermetic Wiper", "Azorult", "DarkCrystal RAT", "Graceful Wipe Out Attack", "IcedID", "Swift Slicer", "Qakbot", "RedLine Stealer", "Brute Ratel C4", "AsyncRAT", "LockBit Ransomware", "AgentTesla", "Double Zero Destructor", "Volt Typhoon", "Chaos Ransomware", "Trickbot", "Amadey", "BlackByte Ransomware", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1036"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name = *.js OR Filesystem.file_name = *.ps1 OR Filesystem.file_name = *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) AND ( Filesystem.file_path = *\\windows\\fonts\\* OR Filesystem.file_path = *\\windows\\temp\\* OR Filesystem.file_path = *\\users\\public\\* OR Filesystem.file_path = *\\windows\\debug\\* OR Filesystem.file_path = *\\Users\\Administrator\\Music\\* OR Filesystem.file_path = *\\Windows\\servicing\\* OR Filesystem.file_path = *\\Users\\Default\\* OR Filesystem.file_path = *Recycle.bin* OR Filesystem.file_path = *\\Windows\\Media\\* OR Filesystem.file_path = *\\Windows\\repair\\* OR Filesystem.file_path = *\\AppData\\Local\\Temp* OR Filesystem.file_path = *\\PerfLogs\\*) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `executables_or_script_creation_in_suspicious_path_filter`

[ESCU - Execute Javascript With Jscript COM CLSID - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious process of cscript.exe where it tries to execute javascript using jscript.encode CLSID (COM OBJ). This technique was seen in ransomware (reddot ransomware) where it execute javascript with this com object with combination of amsi disabling technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify suspicious process of cscript.exe where it tries to execute javascript using jscript.encode CLSID (COM OBJ). This technique was seen in ransomware (reddot ransomware) where it execute javascript with this com object with combination of amsi disabling technique.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-06-22
action.escu.modification_date = 2021-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Execute Javascript With Jscript COM CLSID - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = Suspicious process of cscript.exe with a parent process $parent_process_name$ where it tries to execute javascript using jscript.encode CLSID (COM OBJ), detected on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "parent_process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Execute Javascript With Jscript COM CLSID - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1059", "T1059.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify suspicious process of cscript.exe where it tries to execute javascript using jscript.encode CLSID (COM OBJ). This technique was seen in ransomware (reddot ransomware) where it execute javascript with this com object with combination of amsi disabling technique.
action.notable.param.rule_title = Execute Javascript With Jscript COM CLSID
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe" Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.parent_process_name Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `execute_javascript_with_jscript_com_clsid_filter`

[ESCU - Execution of File with Multiple Extensions - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1036.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2020-11-18
action.escu.modification_date = 2020-11-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Execution of File with Multiple Extensions - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities", "AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = process $process$ have double extensions in the file name is executed on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "process", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Execution of File with Multiple Extensions - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1036", "T1036.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.
action.notable.param.rule_title = Execution of File with Multiple Extensions
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.doc.exe", "*.xls.exe","*.ppt.exe", "*.htm.exe", "*.html.exe", "*.txt.exe", "*.pdf.exe", "*.docx.exe", "*.xlsx.exe", "*.pptx.exe","*.one.exe", "*.bat.exe", "*rtf.exe") by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter`

[ESCU - Extraction of Registry Hives - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of `reg.exe` exporting Windows Registry hives containing credentials. Adversaries may use this technique to export registry hives for offline credential access attacks. Typically found executed from a untrusted process or script. Upon execution, a file will be written to disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of `reg.exe` exporting Windows Registry hives containing credentials. Adversaries may use this technique to export registry hives for offline credential access attacks. Typically found executed from a untrusted process or script. Upon execution, a file will be written to disk.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible some agent based products will generate false positives. Filter as needed.
action.escu.creation_date = 2021-09-09
action.escu.modification_date = 2021-09-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Extraction of Registry Hives - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkSide Ransomware", "Credential Dumping", "CISA AA22-257A", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Suspicious use of `reg.exe` exporting Windows Registry hives containing credentials executed on $dest$ by user $user$, with a parent process of $parent_process_id$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "parent_process_id", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Extraction of Registry Hives - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Credential Dumping", "CISA AA22-257A", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of `reg.exe` exporting Windows Registry hives containing credentials. Adversaries may use this technique to export registry hives for offline credential access attacks. Typically found executed from a untrusted process or script. Upon execution, a file will be written to disk.
action.notable.param.rule_title = Extraction of Registry Hives
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` (Processes.process=*save* OR Processes.process=*export*) AND (Processes.process="*\sam *" OR Processes.process="*\system *" OR Processes.process="*\security *") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `extraction_of_registry_hives_filter`

[ESCU - File with Samsam Extension - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects file writes with extensions that are consistent with a SamSam ransomware attack to proactively detect and respond to potential SamSam ransomware attacks, minimizing the impact and reducing the likelihood of successful ransomware infections. This detection is made by a Splunk query to search for specific file extensions that are commonly associated with SamSam ransomware, such as .stubbin, .berkshire, .satoshi, .sophos, and .keyxml. This identifies file extensions in the file names of the written files. If any file write events with these extensions are found, it suggests a potential SamSam ransomware attack. This detection is important because SamSam ransomware is a highly destructive and financially motivated attack and suggests that the organization is at risk of having its files encrypted and held for ransom, which can lead to significant financial losses, operational disruptions, and reputational damage. False positives might occur since legitimate files with these extensions can exist in the environment. Therefore, next steps include conducting a careful analysis and triage to confirm the presence of a SamSam ransomware attack. Next steps include taking immediate action to contain the attack, mitigate the impact, and prevent further spread of the ransomware. This might involve isolating affected systems, restoring encrypted files from backups, and conducting a thorough investigation to identify the attack source and prevent future incidents.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects file writes with extensions that are consistent with a SamSam ransomware attack to proactively detect and respond to potential SamSam ransomware attacks, minimizing the impact and reducing the likelihood of successful ransomware infections. This detection is made by a Splunk query to search for specific file extensions that are commonly associated with SamSam ransomware, such as .stubbin, .berkshire, .satoshi, .sophos, and .keyxml. This identifies file extensions in the file names of the written files. If any file write events with these extensions are found, it suggests a potential SamSam ransomware attack. This detection is important because SamSam ransomware is a highly destructive and financially motivated attack and suggests that the organization is at risk of having its files encrypted and held for ransom, which can lead to significant financial losses, operational disruptions, and reputational damage. False positives might occur since legitimate files with these extensions can exist in the environment. Therefore, next steps include conducting a careful analysis and triage to confirm the presence of a SamSam ransomware attack. Next steps include taking immediate action to contain the attack, mitigate the impact, and prevent further spread of the ransomware. This might involve isolating affected systems, restoring encrypted files from backups, and conducting a thorough investigation to identify the attack source and prevent future incidents.
action.escu.how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
action.escu.known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results.
action.escu.creation_date = 2018-12-14
action.escu.modification_date = 2018-12-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - File with Samsam Extension - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware"]
action.risk = 1
action.risk.param._risk_message = File writes $file_name$ with extensions consistent with a SamSam ransomware attack seen on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - File with Samsam Extension - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects file writes with extensions that are consistent with a SamSam ransomware attack to proactively detect and respond to potential SamSam ransomware attacks, minimizing the impact and reducing the likelihood of successful ransomware infections. This detection is made by a Splunk query to search for specific file extensions that are commonly associated with SamSam ransomware, such as .stubbin, .berkshire, .satoshi, .sophos, and .keyxml. This identifies file extensions in the file names of the written files. If any file write events with these extensions are found, it suggests a potential SamSam ransomware attack. This detection is important because SamSam ransomware is a highly destructive and financially motivated attack and suggests that the organization is at risk of having its files encrypted and held for ransom, which can lead to significant financial losses, operational disruptions, and reputational damage. False positives might occur since legitimate files with these extensions can exist in the environment. Therefore, next steps include conducting a careful analysis and triage to confirm the presence of a SamSam ransomware attack. Next steps include taking immediate action to contain the attack, mitigate the impact, and prevent further spread of the ransomware. This might involve isolating affected systems, restoring encrypted files from backups, and conducting a thorough investigation to identify the attack source and prevent future incidents.
action.notable.param.rule_title = File with Samsam Extension
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter`

[ESCU - Firewall Allowed Program Enable - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a potential suspicious modification of firewall rule allowing to execute specific application. This technique was identified when an adversary and red teams to bypassed firewall file execution restriction in a targetted host. Take note that this event or command can run by administrator during testing or allowing legitimate tool or application.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects a potential suspicious modification of firewall rule allowing to execute specific application. This technique was identified when an adversary and red teams to bypassed firewall file execution restriction in a targetted host. Take note that this event or command can run by administrator during testing or allowing legitimate tool or application.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = A network operator or systems administrator may utilize an automated or manual execution of this firewall rule that may generate false positives. Filter as needed.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Firewall Allowed Program Enable - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Azorult", "BlackByte Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = firewall allowed program commandline $process$ of $process_name$ on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Firewall Allowed Program Enable - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Azorult", "BlackByte Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*" Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `firewall_allowed_program_enable_filter`

[ESCU - First Time Seen Child Process of Zoom - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken.
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - First Time Seen Child Process of Zoom - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Zoom Child Processes"]
action.risk = 1
action.risk.param._risk_message = Child process $process_name$ with $process_id$ spawned by zoom.exe or zoom.us which has not been previously on host $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - First Time Seen Child Process of Zoom - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Zoom Child Processes"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1068"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter`

[ESCU - First Time Seen Running Windows Service - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached.
action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
action.escu.known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - First Time Seen Running Windows Service - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Service Abuse", "Orangeworm Attack Group", "NOBELIUM Group"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - First Time Seen Running Windows Service - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Service Abuse", "Orangeworm Attack Group", "NOBELIUM Group"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter`

[ESCU - FodHelper UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \
1. `HKCU:\Software\Classes\ms-settings\shell\open\command`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\(default)`\
Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112", "T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \
1. `HKCU:\Software\Classes\ms-settings\shell\open\command`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\(default)`\
Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited to no false positives are expected.
action.escu.creation_date = 2021-03-01
action.escu.modification_date = 2021-03-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - FodHelper UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "IcedID"]
action.risk = 1
action.risk.param._risk_message = Suspcious registy keys added by process fodhelper.exe (process_id- $process_id), with a parent_process of $parent_process_name$ that has been executed on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"threat_object_field": "parent_process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - FodHelper UAC Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "IcedID"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1112", "T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \
1. `HKCU:\Software\Classes\ms-settings\shell\open\command`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\(default)`\
Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.
action.notable.param.rule_title = FodHelper UAC Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `fodhelper_uac_bypass_filter`

[ESCU - Fsutil Zeroing File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious fsutil process to zeroing a target file. This technique was seen in lockbit ransomware where it tries to zero out its malware path as part of its defense evasion after encrypting the compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious fsutil process to zeroing a target file. This technique was seen in lockbit ransomware where it tries to zero out its malware path as part of its defense evasion after encrypting the compromised host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-11
action.escu.modification_date = 2021-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Fsutil Zeroing File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = Possible file data deletion on $dest$ using $process$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Fsutil Zeroing File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious fsutil process to zeroing a target file. This technique was seen in lockbit ransomware where it tries to zero out its malware path as part of its defense evasion after encrypting the compromised host.
action.notable.param.rule_title = Fsutil Zeroing File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe Processes.process="*setzerodata*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  Processes.process Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `fsutil_zeroing_file_filter`

[ESCU - Get ADDefaultDomainPasswordPolicy with Powershell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` executing the Get-ADDefaultDomainPasswordPolicy commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` executing the Get-ADDefaultDomainPasswordPolicy commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-26
action.escu.modification_date = 2021-08-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get ADDefaultDomainPasswordPolicy with Powershell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get ADDefaultDomainPasswordPolicy with Powershell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADDefaultDomainPasswordPolicy*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_addefaultdomainpasswordpolicy_with_powershell_filter`

[ESCU - Get ADDefaultDomainPasswordPolicy with Powershell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADDefaultDomainPasswordPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADDefaultDomainPasswordPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get ADDefaultDomainPasswordPolicy with Powershell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get ADDefaultDomainPasswordPolicy with Powershell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText ="*Get-ADDefaultDomainPasswordPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user |  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_addefaultdomainpasswordpolicy_with_powershell_script_block_filter`

[ESCU - Get ADUser with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to enumerate domain users. The `Get-AdUser' commandlet returns a list of all domain users. Red Teams and adversaries alike may use this commandlet to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to enumerate domain users. The `Get-AdUser' commandlet returns a list of all domain users. Red Teams and adversaries alike may use this commandlet to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get ADUser with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get ADUser with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUser*" AND Processes.process = "*-filter*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_filter`

[ESCU - Get ADUser with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-AdGUser` commandlet. The `Get-AdUser` commandlet is used to return a list of all domain users. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-AdGUser` commandlet. The `Get-AdUser` commandlet is used to return a list of all domain users. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2022-09-13
action.escu.modification_date = 2022-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get ADUser with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get ADUser with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*get-aduser*" ScriptBlockText = "*-filter*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_script_block_filter`

[ESCU - Get ADUserResultantPasswordPolicy with Powershell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` executing the Get ADUserResultantPasswordPolicy commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` executing the Get ADUserResultantPasswordPolicy commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-26
action.escu.modification_date = 2021-08-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get ADUserResultantPasswordPolicy with Powershell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = an instance of process $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get ADUserResultantPasswordPolicy with Powershell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` executing the Get ADUserResultantPasswordPolicy commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Get ADUserResultantPasswordPolicy with Powershell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUserResultantPasswordPolicy*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduserresultantpasswordpolicy_with_powershell_filter`

[ESCU - Get ADUserResultantPasswordPolicy with Powershell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADUserResultantPasswordPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADUserResultantPasswordPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get ADUserResultantPasswordPolicy with Powershell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $ScriptBlockText$ to query domain user password policy.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 9}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get ADUserResultantPasswordPolicy with Powershell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-ADUserResultantPasswordPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Get ADUserResultantPasswordPolicy with Powershell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*Get-ADUserResultantPasswordPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduserresultantpasswordpolicy_with_powershell_script_block_filter`

[ESCU - Get DomainPolicy with Powershell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` executing the `Get-DomainPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` executing the `Get-DomainPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-26
action.escu.modification_date = 2021-08-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get DomainPolicy with Powershell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = an instance of process $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get DomainPolicy with Powershell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` executing the `Get-DomainPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Get DomainPolicy with Powershell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainPolicy*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainpolicy_with_powershell_filter`

[ESCU - Get DomainPolicy with Powershell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get DomainPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get DomainPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get DomainPolicy with Powershell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $ScriptBlockText$ to query domain policy.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get DomainPolicy with Powershell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1201"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get DomainPolicy` commandlet used to obtain the password policy in a Windows domain. Red Teams and adversaries alike may use PowerShell to enumerate domain policies for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Get DomainPolicy with Powershell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText ="*Get-DomainPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainpolicy_with_powershell_script_block_filter`

[ESCU - Get-DomainTrust with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies Get-DomainTrust from PowerView in order to gather domain trust information. Typically, this is utilized within a script being executed and used to enumerate the domain trust information. This grants the adversary an understanding of how large or small the domain is. During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies Get-DomainTrust from PowerView in order to gather domain trust information. Typically, this is utilized within a script being executed and used to enumerate the domain trust information. This grants the adversary an understanding of how large or small the domain is. During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives as this requires an active Administrator or adversary to bring in, import, and execute.
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get-DomainTrust with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get-DomainTrust with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 40, "impact": 30, "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies Get-DomainTrust from PowerView in order to gather domain trust information. Typically, this is utilized within a script being executed and used to enumerate the domain trust information. This grants the adversary an understanding of how large or small the domain is. During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity.
action.notable.param.rule_title = Get-DomainTrust with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*get-domaintrust* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_filter`

[ESCU - Get-DomainTrust with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies Get-DomainTrust from PowerView in order to gather domain trust information. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies Get-DomainTrust from PowerView in order to gather domain trust information. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible certain system management frameworks utilize this command to gather trust information.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get-DomainTrust with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get-DomainTrust with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 40, "impact": 30, "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies Get-DomainTrust from PowerView in order to gather domain trust information. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Get-DomainTrust with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*get-domaintrust*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_script_block_filter`

[ESCU - Get DomainUser with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to enumerate domain users. `Get-DomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to enumerate domain users. `Get-DomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get DomainUser with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = an instance of process $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get DomainUser with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to enumerate domain users. `Get-DomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Get DomainUser with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainUser*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainuser_with_powershell_filter`

[ESCU - Get DomainUser with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` commandlet. `GetDomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` commandlet. `GetDomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2022-11-13
action.escu.modification_date = 2022-11-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get DomainUser with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Powershell process having commandline "*Get-DomainUser*" for user enumeration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get DomainUser with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` commandlet. `GetDomainUser` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Get DomainUser with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Get-DomainUser*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainuser_with_powershell_script_block_filter`

[ESCU - Get-ForestTrust with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies Get-ForestTrust from PowerSploit in order to gather domain trust information. Typically, this is utilized within a script being executed and used to enumerate the domain trust information. This grants the adversary an understanding of how large or small the domain is. During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies Get-ForestTrust from PowerSploit in order to gather domain trust information. Typically, this is utilized within a script being executed and used to enumerate the domain trust information. This grants the adversary an understanding of how large or small the domain is. During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives as this requires an active Administrator or adversary to bring in, import, and execute.
action.escu.creation_date = 2021-09-02
action.escu.modification_date = 2021-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get-ForestTrust with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get-ForestTrust with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 40, "impact": 30, "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies Get-ForestTrust from PowerSploit in order to gather domain trust information. Typically, this is utilized within a script being executed and used to enumerate the domain trust information. This grants the adversary an understanding of how large or small the domain is. During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity.
action.notable.param.rule_title = Get-ForestTrust with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe Processes.process=*get-foresttrust* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_filter`

[ESCU - Get-ForestTrust with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies Get-ForestTrust from PowerSploit in order to gather domain trust information. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1482", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies Get-ForestTrust from PowerSploit in order to gather domain trust information. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may be present. Tune as needed.
action.escu.creation_date = 2022-02-24
action.escu.modification_date = 2022-02-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get-ForestTrust with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get-ForestTrust with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 40, "impact": 30, "mitre_attack": ["T1482", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies Get-ForestTrust from PowerSploit in order to gather domain trust information. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Get-ForestTrust with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*get-foresttrust*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_script_block_filter`

[ESCU - Get WMIObject Group Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies the use of `Get-WMIObject Win32_Group` being used with PowerShell to identify local groups on the endpoint. \ Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \ During triage, review parallel processes and identify any further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies the use of `Get-WMIObject Win32_Group` being used with PowerShell to identify local groups on the endpoint. \ Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \ During triage, review parallel processes and identify any further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present. Tune as needed.
action.escu.creation_date = 2021-09-14
action.escu.modification_date = 2021-09-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get WMIObject Group Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get WMIObject Group Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe OR processes.process_name=cmd.exe) (Processes.process="*Get-WMIObject*" AND Processes.process="*Win32_Group*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`  | `get_wmiobject_group_discovery_filter`

[ESCU - Get WMIObject Group Discovery with Script Block Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the usage of `Get-WMIObject Win32_Group`, which is typically used as a way to identify groups on the endpoint.  Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the usage of `Get-WMIObject Win32_Group`, which is typically used as a way to identify groups on the endpoint.  Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may be present. Tune as needed.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Get WMIObject Group Discovery with Script Block Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Get WMIObject Group Discovery with Script Block Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Get-WMIObject*" AND ScriptBlockText = "*Win32_Group*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_wmiobject_group_discovery_with_script_block_logging_filter`

[ESCU - GetAdComputer with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. The `Get-AdComputer' commandlet returns a list of all domain computers. Red Teams and adversaries alike may use this commandlet to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. The `Get-AdComputer' commandlet returns a list of all domain computers. Red Teams and adversaries alike may use this commandlet to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-07
action.escu.modification_date = 2021-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetAdComputer with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetAdComputer with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-AdComputer*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadcomputer_with_powershell_filter`

[ESCU - GetAdComputer with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-AdGroup` commandlet. The `Get-AdGroup` commandlet is used to return a list of all domain computers. Red Teams and adversaries may leverage this commandlet to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-AdGroup` commandlet. The `Get-AdGroup` commandlet is used to return a list of all domain computers. Red Teams and adversaries may leverage this commandlet to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetAdComputer with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery", "CISA AA22-320A"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetAdComputer with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Get-AdComputer*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getadcomputer_with_powershell_script_block_filter`

[ESCU - GetAdGroup with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. The `Get-AdGroup` commandlnet is used to return a list of all groups available in a Windows Domain. Red Teams and adversaries alike may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. The `Get-AdGroup` commandlnet is used to return a list of all groups available in a Windows Domain. Red Teams and adversaries alike may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetAdGroup with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetAdGroup with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-AdGroup*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_filter`

[ESCU - GetAdGroup with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-AdGroup` commandlet. The `Get-AdGroup` commandlet is used to return a list of all domain groups. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-AdGroup` commandlet. The `Get-AdGroup` commandlet is used to return a list of all domain groups. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetAdGroup with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetAdGroup with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Get-ADGroup*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_script_block_filter`

[ESCU - GetCurrent User with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powerhsell.exe` with command-line arguments that execute the `GetCurrent` method of the WindowsIdentity .NET class. This method returns an object that represents the current Windows user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powerhsell.exe` with command-line arguments that execute the `GetCurrent` method of the WindowsIdentity .NET class. This method returns an object that represents the current Windows user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetCurrent User with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetCurrent User with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*System.Security.Principal.WindowsIdentity* OR Processes.process=*GetCurrent()*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getcurrent_user_with_powershell_filter`

[ESCU - GetCurrent User with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `GetCurrent` method of the WindowsIdentity .NET class. This method returns an object that represents the current Windows user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `GetCurrent` method of the WindowsIdentity .NET class. This method returns an object that represents the current Windows user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetCurrent User with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetCurrent User with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*[System.Security.Principal.WindowsIdentity]*"  ScriptBlockText = "*GetCurrent()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getcurrent_user_with_powershell_script_block_filter`

[ESCU - GetDomainComputer with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. `Get-DomainComputer` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. `Get-DomainComputer` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use PowerView for troubleshooting.
action.escu.creation_date = 2021-09-07
action.escu.modification_date = 2021-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetDomainComputer with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetDomainComputer with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. `Get-DomainComputer` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetDomainComputer with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-DomainComputer*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaincomputer_with_powershell_filter`

[ESCU - GetDomainComputer with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainComputer` commandlet. `GetDomainComputer` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainComputer` commandlet. `GetDomainComputer` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use PowerView for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetDomainComputer with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery with PowerView on $Computer$ by $user$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetDomainComputer with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainComputer` commandlet. `GetDomainComputer` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain computers for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetDomainComputer with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaincomputer_with_powershell_script_block_filter`

[ESCU - GetDomainController with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. `Get-DomainController` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. `Get-DomainController` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use PowerView for troubleshooting.
action.escu.creation_date = 2021-09-07
action.escu.modification_date = 2021-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetDomainController with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetDomainController with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-DomainController*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaincontroller_with_powershell_filter`

[ESCU - GetDomainController with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainController` commandlet. `Get-DomainController` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainController` commandlet. `Get-DomainController` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetDomainController with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery with PowerView on $Computer$ by $UserID$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetDomainController with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainController` commandlet. `Get-DomainController` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may use PowerView to enumerate domain computers for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetDomainController with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainController*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaincontroller_with_powershell_script_block_filter`

[ESCU - GetDomainGroup with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. `Get-DomainGroup` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. `Get-DomainGroup` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetDomainGroup with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain group discovery with PowerView on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetDomainGroup with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. `Get-DomainGroup` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. Red Teams and adversaries alike may leverage PowerView to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetDomainGroup with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-DomainGroup*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaingroup_with_powershell_filter`

[ESCU - GetDomainGroup with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainGroup` commandlet. `Get-DomainGroup` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. As the name suggests, `Get-DomainGroup` is used to query domain groups. Red Teams and adversaries may leverage this function to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainGroup` commandlet. `Get-DomainGroup` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. As the name suggests, `Get-DomainGroup` is used to query domain groups. Red Teams and adversaries may leverage this function to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerView functions for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetDomainGroup with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain group discovery enumeration using PowerView on $Computer$ by $UserID$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetDomainGroup with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainGroup` commandlet. `Get-DomainGroup` is part of PowerView, a PowerShell tool used to perform enumeration on Windows domains. As the name suggests, `Get-DomainGroup` is used to query domain groups. Red Teams and adversaries may leverage this function to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetDomainGroup with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroup*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaingroup_with_powershell_script_block_filter`

[ESCU - GetLocalUser with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for local users. The `Get-LocalUser` commandlet is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for local users. The `Get-LocalUser` commandlet is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2021-08-23
action.escu.modification_date = 2021-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetLocalUser with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetLocalUser with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-LocalUser*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getlocaluser_with_powershell_filter`

[ESCU - GetLocalUser with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-LocalUser` commandlet. The `Get-LocalUser` commandlet is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.001", "T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-LocalUser` commandlet. The `Get-LocalUser` commandlet is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetLocalUser with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery", "Malicious PowerShell"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetLocalUser with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1087", "T1087.001", "T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Get-LocalUser*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getlocaluser_with_powershell_script_block_filter`

[ESCU - GetNetTcpconnection with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line utilized to get a listing of network connections on a compromised system. The `Get-NetTcpConnection` commandlet lists the current TCP connections. Red Teams and adversaries alike may use this commandlet for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line utilized to get a listing of network connections on a compromised system. The `Get-NetTcpConnection` commandlet lists the current TCP connections. Red Teams and adversaries alike may use this commandlet for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetNetTcpconnection with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetNetTcpconnection with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-NetTcpConnection*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getnettcpconnection_with_powershell_filter`

[ESCU - GetNetTcpconnection with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-NetTcpconnection ` commandlet. This commandlet is used to return a listing of network connections on a compromised system. Red Teams and adversaries alike may use this commandlet for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-NetTcpconnection ` commandlet. This commandlet is used to return a listing of network connections on a compromised system. Red Teams and adversaries alike may use this commandlet for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-04-02
action.escu.modification_date = 2022-04-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetNetTcpconnection with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetNetTcpconnection with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Get-NetTcpconnection*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getnettcpconnection_with_powershell_script_block_filter`

[ESCU - GetWmiObject Ds Computer with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. The `Get-WmiObject` commandlet combined with the `DS_Computer` parameter can be used to return a list of all domain computers. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. The `Get-WmiObject` commandlet combined with the `DS_Computer` parameter can be used to return a list of all domain computers. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-07
action.escu.modification_date = 2021-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject Ds Computer with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery enumeration using WMI on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 21}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject Ds Computer with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to discover remote systems. The `Get-WmiObject` commandlet combined with the `DS_Computer` parameter can be used to return a list of all domain computers. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetWmiObject Ds Computer with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" AND Processes.process="*class ds_computer*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_computer_with_powershell_filter`

[ESCU - GetWmiObject Ds Computer with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet. The `DS_Computer` class parameter leverages WMI to query for all domain computers. Red Teams and adversaries may leverage this commandlet to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet. The `DS_Computer` class parameter leverages WMI to query for all domain computers. Red Teams and adversaries may leverage this commandlet to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject Ds Computer with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery enumeration on $Computer$ by $UserID$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject Ds Computer with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet. The `DS_Computer` class parameter leverages WMI to query for all domain computers. Red Teams and adversaries may leverage this commandlet to enumerate domain computers for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetWmiObject Ds Computer with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace root\\directory\\ldap*" AND ScriptBlockText="*class ds_computer*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getwmiobject_ds_computer_with_powershell_script_block_filter`

[ESCU - GetWmiObject Ds Group with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. The `Get-WmiObject` commandlet combined with the `-class ds_group` parameter can be used to return the full list of groups in a Windows domain. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. The `Get-WmiObject` commandlet combined with the `-class ds_group` parameter can be used to return the full list of groups in a Windows domain. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-25
action.escu.modification_date = 2021-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject Ds Group with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain group discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject Ds Group with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain groups. The `Get-WmiObject` commandlet combined with the `-class ds_group` parameter can be used to return the full list of groups in a Windows domain. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetWmiObject Ds Group with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" AND Processes.process="*class ds_group*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_group_with_powershell_filter`

[ESCU - GetWmiObject Ds Group with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet used with specific parameters . The `DS_Group` parameter leverages WMI to query for all domain groups. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet used with specific parameters . The `DS_Group` parameter leverages WMI to query for all domain groups. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject Ds Group with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain group discovery enumeration using PowerShell on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject Ds Group with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet used with specific parameters . The `DS_Group` parameter leverages WMI to query for all domain groups. Red Teams and adversaries may leverage this commandlet to enumerate domain groups for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetWmiObject Ds Group with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace root\\directory\\ldap*" AND ScriptBlockText="*class ds_group*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`getwmiobject_ds_group_with_powershell_script_block_filter`

[ESCU - GetWmiObject DS User with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain users. The `Get-WmiObject` commandlet combined with the `-class ds_user` parameter can be used to return the full list of users in a Windows domain. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain users. The `Get-WmiObject` commandlet combined with the `-class ds_user` parameter can be used to return the full list of users in a Windows domain. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-24
action.escu.modification_date = 2021-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject DS User with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = an instance of process $process_name$ with commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject DS User with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query for domain users. The `Get-WmiObject` commandlet combined with the `-class ds_user` parameter can be used to return the full list of users in a Windows domain. Red Teams and adversaries alike may leverage WMI in this case, using PowerShell, to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetWmiObject DS User with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*get-wmiobject*" AND Processes.process = "*ds_user*" AND Processes.process = "*root\\directory\\ldap*" AND Processes.process = "*-namespace*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_filter`

[ESCU - GetWmiObject DS User with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet. The `DS_User` class parameter leverages WMI to query for all domain users. Red Teams and adversaries may leverage this commandlet to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet. The `DS_User` class parameter leverages WMI to query for all domain users. Red Teams and adversaries may leverage this commandlet to enumerate domain users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = he following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject DS User with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $ScriptBlockText$ for user enumeration
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject DS User with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet. The `DS_User` class parameter leverages WMI to query for all domain users. Red Teams and adversaries may leverage this commandlet to enumerate domain users for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = GetWmiObject DS User with PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*get-wmiobject*" ScriptBlockText = "*ds_user*" ScriptBlockText = "*-namespace*" ScriptBlockText = "*root\\directory\\ldap*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_script_block_filter`

[ESCU - GetWmiObject User Account with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query local users. The `Get-WmiObject` commandlet combined with the `Win32_UserAccount` parameter is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments utilized to query local users. The `Get-WmiObject` commandlet combined with the `Win32_UserAccount` parameter is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject User Account with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Winter Vivern", "Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject User Account with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern", "Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process=*Win32_UserAccount*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_user_account_with_powershell_filter`

[ESCU - GetWmiObject User Account with PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet used with specific parameters. The `Win32_UserAccount` parameter is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.001", "T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-WmiObject` commandlet used with specific parameters. The `Win32_UserAccount` parameter is used to return a list of all local users. Red Teams and adversaries may leverage this commandlet to enumerate users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - GetWmiObject User Account with PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Winter Vivern", "Active Directory Discovery", "Malicious PowerShell"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GetWmiObject User Account with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern", "Active Directory Discovery", "Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1087", "T1087.001", "T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText="*Get-WmiObject*" AND ScriptBlockText="*Win32_UserAccount*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `getwmiobject_user_account_with_powershell_script_block_filter`

[ESCU - GPUpdate with no Command Line Arguments with Network - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - GPUpdate with no Command Line Arguments with Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Process gpupdate.exe  with parent_process $parent_process_name$ is executed on $dest$ by user $user$, followed by an outbound network connection to $C2$ on port $dest_port$. This behaviour is seen with cobaltstrike.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"threat_object_field": "parent_process_name", "threat_object_type": "process name"}, {"risk_object_field": "C2", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - GPUpdate with no Command Line Arguments with Network - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = GPUpdate with no Command Line Arguments with Network
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(gpupdate\.exe.{0,4}$)"| join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 | `gpupdate_with_no_command_line_arguments_with_network_filter`

[ESCU - Headless Browser Mockbin or Mocky Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies headless browser activity accessing mockbin.org or mocky.io. Mockbin.org and mocky.io are web services that allow users to mock HTTP requests and responses. The detection is based on the presence of "--headless" and "--disable-gpu" command line arguments which are commonly used in headless browsing and the presence of mockbin.org or mocky.io in the process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1564.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies headless browser activity accessing mockbin.org or mocky.io. Mockbin.org and mocky.io are web services that allow users to mock HTTP requests and responses. The detection is based on the presence of "--headless" and "--disable-gpu" command line arguments which are commonly used in headless browsing and the presence of mockbin.org or mocky.io in the process.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives are not expected with this detection, unless within the organization there is a legitimate need for headless browsing accessing mockbin.org or mocky.io.
action.escu.creation_date = 2023-09-11
action.escu.modification_date = 2023-09-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Headless Browser Mockbin or Mocky Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Forest Blizzard"]
action.risk = 1
action.risk.param._risk_message = Headless browser activity accessing mockbin.org or mocky.io detected on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Headless Browser Mockbin or Mocky Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Forest Blizzard"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1564.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies headless browser activity accessing mockbin.org or mocky.io. Mockbin.org and mocky.io are web services that allow users to mock HTTP requests and responses. The detection is based on the presence of "--headless" and "--disable-gpu" command line arguments which are commonly used in headless browsing and the presence of mockbin.org or mocky.io in the process.
action.notable.param.rule_title = Headless Browser Mockbin or Mocky Request
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*" AND (Processes.process="*mockbin.org/*" OR Processes.process="*mocky.io/*")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `headless_browser_mockbin_or_mocky_request_filter`

[ESCU - Headless Browser Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic is designed to detect the usage of headless browsers in an organization. Headless browsers are web browsers without a graphical user interface and are operated via a command line interface or network requests. They are often used for automating tasks but can also be utilized by adversaries for malicious activities such as web scraping, automated testing, and performing actions on web pages without detection. The detection is based on the presence of "--headless" and "--disable-gpu" command line arguments which are commonly used in headless browsing.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1564.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic is designed to detect the usage of headless browsers in an organization. Headless browsers are web browsers without a graphical user interface and are operated via a command line interface or network requests. They are often used for automating tasks but can also be utilized by adversaries for malicious activities such as web scraping, automated testing, and performing actions on web pages without detection. The detection is based on the presence of "--headless" and "--disable-gpu" command line arguments which are commonly used in headless browsing.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = This hunting analytic is meant to assist with baselining and understanding headless browsing in use. Filter as needed.
action.escu.creation_date = 2023-09-08
action.escu.modification_date = 2023-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Headless Browser Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Forest Blizzard"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Headless Browser Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Forest Blizzard"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1564.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `headless_browser_usage_filter`

[ESCU - Hide User Account From Sign-In Screen - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = Unknown. Filter as needed.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Hide User Account From Sign-In Screen - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Windows Registry Abuse", "Azorult", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = Suspicious registry modification ($registry_value_name$) which is used go hide a user account on the Windows Login screen detected on $dest$ executed by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "registry_value_name", "threat_object_type": "other"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Hide User Account From Sign-In Screen - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Windows Registry Abuse", "Azorult", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine.
action.notable.param.rule_title = Hide User Account From Sign-In Screen
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_data = "0x00000000") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hide_user_account_from_sign_in_screen_filter`

[ESCU - Hiding Files And Directories With Attrib exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file.  The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222", "T1222.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file.  The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. 
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Hiding Files And Directories With Attrib exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques", "Azorult"]
action.risk = 1
action.risk.param._risk_message = Attrib.exe with +h flag to hide files on $dest$ executed by $user$ is detected.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "parent_process", "threat_object_type": "other"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Hiding Files And Directories With Attrib exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Persistence Techniques", "Azorult"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1222", "T1222.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file.  The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.
action.notable.param.rule_title = Hiding Files And Directories With Attrib exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` 

[ESCU - High Frequency Copy Of Files In Network Share - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious high frequency copying/moving of files in network share as part of information sabotage. This anomaly event can be a good indicator of insider trying to sabotage data by transfering classified or internal files within network share to exfitrate it after or to lure evidence of insider attack to other user. This behavior may catch several noise if network share is a common place for classified or internal document processing.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1537"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to detect a suspicious high frequency copying/moving of files in network share as part of information sabotage. This anomaly event can be a good indicator of insider trying to sabotage data by transfering classified or internal files within network share to exfitrate it after or to lure evidence of insider attack to other user. This behavior may catch several noise if network share is a common place for classified or internal document processing.
action.escu.how_to_implement = o successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.
action.escu.known_false_positives = this behavior may seen in normal transfer of file within network if network share is common place for sharing documents.
action.escu.creation_date = 2021-11-16
action.escu.modification_date = 2021-11-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - High Frequency Copy Of Files In Network Share - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Information Sabotage", "Insider Threat"]
action.risk = 1
action.risk.param._risk_message = high frequency copy of document in network share $Share_Name$ from $Source_Address$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - High Frequency Copy Of Files In Network Share - Rule
action.correlationsearch.annotations = {"analytic_story": ["Information Sabotage", "Insider Threat"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1537"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.doc","*.docx","*.xls","*.xlsx","*.ppt","*.pptx","*.log","*.txt","*.db","*.7z","*.zip","*.rar","*.tar","*.gz","*.jpg","*.gif","*.png","*.bmp","*.pdf","*.rtf","*.key") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") Access_Mask= "0x2" |  bucket _time span=5m | stats values(Relative_Target_Name) as valRelativeTargetName, values(Share_Name) as valShareName, values(Object_Type) as valObjectType, values(Access_Mask) as valAccessmask, values(src_port) as valSrcPort, values(Source_Address) as valSrcAddress count as numShareName by dest, _time, EventCode, user | eventstats avg(numShareName) as avgShareName, stdev(numShareName) as stdShareName, count as numSlots by dest, _time, EventCode, user |  eval upperThreshold=(avgShareName + stdShareName *3) |  eval isOutlier=if(avgShareName > 20 and avgShareName >= upperThreshold, 1, 0) |  search isOutlier=1 | `high_frequency_copy_of_files_in_network_share_filter`

[ESCU - High Process Termination Frequency - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to identify a high frequency of process termination events on a computer in a short period of time, which is a common behavior of ransomware malware before encrypting files. This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is designed to identify a high frequency of process termination events on a computer in a short period of time, which is a common behavior of ransomware malware before encrypting files. This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = admin or user tool that can terminate multiple process.
action.escu.creation_date = 2022-09-14
action.escu.modification_date = 2022-09-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - High Process Termination Frequency - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Clop Ransomware", "LockBit Ransomware", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = High frequency process termination (more than 15 processes within 3s) detected on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "proc_terminated", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - High Process Termination Frequency - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware", "LockBit Ransomware", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime  count by _time dest EventCode ProcessID | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter`

[ESCU - Hunting 3CXDesktopApp Software - Rule]
action.escu = 0
action.escu.enabled = 1
description = The hunting analytic outlined below is designed to detect any version of the 3CXDesktopApp, also known as the 3CX Desktop App, operating on either Mac or Windows systems. It is important to note that this particular analytic employs the Endpoint datamodel Processes node, which means that the file version information is not provided. Recently, 3CX has identified a vulnerability specifically in versions 18.12.407 and 18.12.416 of the desktop app.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1195.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The hunting analytic outlined below is designed to detect any version of the 3CXDesktopApp, also known as the 3CX Desktop App, operating on either Mac or Windows systems. It is important to note that this particular analytic employs the Endpoint datamodel Processes node, which means that the file version information is not provided. Recently, 3CX has identified a vulnerability specifically in versions 18.12.407 and 18.12.416 of the desktop app.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = There may be false positives generated due to the reliance on version numbers for identification purposes. Despite this limitation, the primary goal of this approach is to aid in the detection of the software within the environment.
action.escu.creation_date = 2023-03-30
action.escu.modification_date = 2023-03-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Hunting 3CXDesktopApp Software - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["3CX Supply Chain Attack"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Hunting 3CXDesktopApp Software - Rule
action.correlationsearch.annotations = {"analytic_story": ["3CX Supply Chain Attack"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-29059"], "impact": 80, "mitre_attack": ["T1195.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=3CXDesktopApp.exe OR Processes.process_name="3CX Desktop App" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hunting_3cxdesktopapp_software_filter`

[ESCU - Icacls Deny Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies instances where an adversary modifies the security permissions of a particular file or directory. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. The purpose of this behavior is to actively evade detection and impede access to their associated files. By identifying these security permission changes, we can enhance our ability to detect and respond to potential threats, mitigating the impact of malicious activities on the system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies instances where an adversary modifies the security permissions of a particular file or directory. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. The purpose of this behavior is to actively evade detection and impede access to their associated files. By identifying these security permission changes, we can enhance our ability to detect and respond to potential threats, mitigating the impact of malicious activities on the system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. It is possible some administrative scripts use ICacls. Filter as needed.
action.escu.creation_date = 2023-06-06
action.escu.modification_date = 2023-06-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Icacls Deny Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Azorult", "Sandworm Tools"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ with deny argument executed by $user$ to change security permission of a specific file or directory on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Icacls Deny Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Azorult", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1222"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies instances where an adversary modifies the security permissions of a particular file or directory. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. The purpose of this behavior is to actively evade detection and impede access to their associated files. By identifying these security permission changes, we can enhance our ability to detect and respond to potential threats, mitigating the impact of malicious activities on the system.
action.notable.param.rule_title = Icacls Deny Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/deny*", "*/D*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_deny_command_filter`

[ESCU - ICACLS Grant Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies adversaries who manipulate the security permissions of specific files or directories by granting additional access. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. The objective behind this behavior is to actively evade detection mechanisms and tightly control access to their associated files. By identifying these security permission modifications, we can improve our ability to identify and respond to potential threats, thereby minimizing the impact of malicious activities on the system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies adversaries who manipulate the security permissions of specific files or directories by granting additional access. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. The objective behind this behavior is to actively evade detection mechanisms and tightly control access to their associated files. By identifying these security permission modifications, we can improve our ability to identify and respond to potential threats, thereby minimizing the impact of malicious activities on the system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. Filter as needed.
action.escu.creation_date = 2023-06-06
action.escu.modification_date = 2023-06-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - ICACLS Grant Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ with grant argument executed by $user$ to change security permission of a specific file or directory on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ICACLS Grant Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1222"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies adversaries who manipulate the security permissions of specific files or directories by granting additional access. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. The objective behind this behavior is to actively evade detection mechanisms and tightly control access to their associated files. By identifying these security permission modifications, we can improve our ability to identify and respond to potential threats, thereby minimizing the impact of malicious activities on the system.
action.notable.param.rule_title = ICACLS Grant Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/grant*", "*/G*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_grant_command_filter`

[ESCU - IcedID Exfiltrated Archived File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious file creation namely passff.tar and cookie.tar. This files are possible archived of stolen browser information like history and cookies in a compromised machine with IcedID.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious file creation namely passff.tar and cookie.tar. This files are possible archived of stolen browser information like history and cookies in a compromised machine with IcedID.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-07-30
action.escu.modification_date = 2021-07-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - IcedID Exfiltrated Archived File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - IcedID Exfiltrated Archived File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1560.001", "T1560"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode= 11  (TargetFilename = "*\\passff.tar" OR TargetFilename = "*\\cookie.tar") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter`

[ESCU - Impacket Lateral Movement Commandline Parameters - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like `wmiexec.py`, `smbexec.py`, `dcomexec.py` and `atexec.py` used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like `wmiexec.py`, `smbexec.py`, `dcomexec.py` and `atexec.py` used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Impacket Lateral Movement Commandline Parameters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "CISA AA22-277A", "WhisperGate", "Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "Industroyer2", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Suspicious command line parameters on $dest$ may represent a lateral movement attack with Impackets tools
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Impacket Lateral Movement Commandline Parameters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "CISA AA22-277A", "WhisperGate", "Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "Industroyer2", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like `wmiexec.py`, `smbexec.py`, `dcomexec.py` and `atexec.py` used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
action.notable.param.rule_title = Impacket Lateral Movement Commandline Parameters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process = "*/Q /c * \\\\127.0.0.1\\*$*" AND Processes.process IN ("*2>&1*","*2&gt;&amp;1*")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_commandline_parameters_filter`

[ESCU - Impacket Lateral Movement smbexec CommandLine Parameters - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic focuses on identifying suspicious command-line parameters commonly associated with the use of Impacket wmiexec.py. Impacket is a set of Python classes designed for working with Microsoft network protocols, and it includes several scripts like wmiexec.py, smbexec.py, dcomexec.py, and atexec.py that enable command execution on remote endpoints. These scripts typically utilize administrative shares and hardcoded parameters, which can serve as signatures to detect their usage. Both Red Teams and adversaries may employ Impacket tools for lateral movement and remote code execution purposes. By monitoring for these specific command-line indicators, the analytic aims to detect potentially malicious activities related to Impacket tool usage.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic focuses on identifying suspicious command-line parameters commonly associated with the use of Impacket wmiexec.py. Impacket is a set of Python classes designed for working with Microsoft network protocols, and it includes several scripts like wmiexec.py, smbexec.py, dcomexec.py, and atexec.py that enable command execution on remote endpoints. These scripts typically utilize administrative shares and hardcoded parameters, which can serve as signatures to detect their usage. Both Red Teams and adversaries may employ Impacket tools for lateral movement and remote code execution purposes. By monitoring for these specific command-line indicators, the analytic aims to detect potentially malicious activities related to Impacket tool usage.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Impacket Lateral Movement smbexec CommandLine Parameters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "CISA AA22-277A", "WhisperGate", "Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "Industroyer2", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Suspicious command-line parameters on $dest$ may represent lateral movement using smbexec.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Impacket Lateral Movement smbexec CommandLine Parameters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "CISA AA22-277A", "WhisperGate", "Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "Industroyer2", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic focuses on identifying suspicious command-line parameters commonly associated with the use of Impacket wmiexec.py. Impacket is a set of Python classes designed for working with Microsoft network protocols, and it includes several scripts like wmiexec.py, smbexec.py, dcomexec.py, and atexec.py that enable command execution on remote endpoints. These scripts typically utilize administrative shares and hardcoded parameters, which can serve as signatures to detect their usage. Both Red Teams and adversaries may employ Impacket tools for lateral movement and remote code execution purposes. By monitoring for these specific command-line indicators, the analytic aims to detect potentially malicious activities related to Impacket tool usage.
action.notable.param.rule_title = Impacket Lateral Movement smbexec CommandLine Parameters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process,"(?i)echo\s+cd") AND match(process, "(?i)\\__output") AND  match(process, "(?i)C:\\\\Windows\\\\[a-zA-Z]{1,8}\\.bat")  AND match(process, "\\\\127\.0\.0\.1\\.*") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_smbexec_commandline_parameters_filter`

[ESCU - Impacket Lateral Movement WMIExec Commandline Parameters - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like `wmiexec.py`, `smbexec.py`, `dcomexec.py` and `atexec.py` used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like `wmiexec.py`, `smbexec.py`, `dcomexec.py` and `atexec.py` used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Impacket Lateral Movement WMIExec Commandline Parameters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "CISA AA22-277A", "WhisperGate", "Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "Industroyer2", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Suspicious command-line parameters on $dest$ may represent lateral movement using wmiexec.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Impacket Lateral Movement WMIExec Commandline Parameters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "CISA AA22-277A", "WhisperGate", "Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "Industroyer2", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like `wmiexec.py`, `smbexec.py`, `dcomexec.py` and `atexec.py` used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
action.notable.param.rule_title = Impacket Lateral Movement WMIExec Commandline Parameters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process, "\\\\127\.0\.0\.1\\.*") AND match(process, "__\\d{1,10}\\.\\d{1,10}") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `impacket_lateral_movement_wmiexec_commandline_parameters_filter`

[ESCU - Interactive Session on Remote Endpoint with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the usage of the `Enter-PSSession`. This commandlet can be used to open an interactive session on a remote endpoint leveraging the WinRM protocol. Red Teams and adversaries alike may abuse WinRM and `Enter-PSSession` for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the usage of the `Enter-PSSession`. This commandlet can be used to open an interactive session on a remote endpoint leveraging the WinRM protocol. Red Teams and adversaries alike may abuse WinRM and `Enter-PSSession` for lateral movement and remote code execution.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators may leverage WinRM and `Enter-PSSession` for administrative and troubleshooting tasks. This activity is usually limited to a small set of hosts or users. In certain environments, tuning may not be possible.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Interactive Session on Remote Endpoint with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = An interactive session was opened on a remote endpoint from $ComputerName
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Interactive Session on Remote Endpoint with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the usage of the `Enter-PSSession`. This commandlet can be used to open an interactive session on a remote endpoint leveraging the WinRM protocol. Red Teams and adversaries alike may abuse WinRM and `Enter-PSSession` for lateral movement and remote code execution.
action.notable.param.rule_title = Interactive Session on Remote Endpoint with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText="*Enter-PSSession*" AND ScriptBlockText="*-ComputerName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `interactive_session_on_remote_endpoint_with_powershell_filter`

[ESCU - Java Class File download by Java User Agent - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a Java user agent performing a GET request for a .class file from the remote site. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell).
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies a Java user agent performing a GET request for a .class file from the remote site. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell).
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
action.escu.known_false_positives = Filtering may be required in some instances, filter as needed.
action.escu.creation_date = 2021-12-13
action.escu.modification_date = 2021-12-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Java Class File download by Java User Agent - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = A Java user agent $http_user_agent$ was performing a $http_method$ to retrieve a remote class file.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "http_user_agent", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "http_method", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Java Class File download by Java User Agent - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2021-44228"], "impact": 80, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a Java user agent performing a GET request for a .class file from the remote site. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell).
action.notable.param.rule_title = Java Class File download by Java User Agent
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.http_user_agent="*Java*" Web.http_method="GET" Web.url="*.class*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `java_class_file_download_by_java_user_agent_filter`

[ESCU - Java Writing JSP File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the process java writing a .jsp to disk. This is potentially indicative of a web shell being written to disk. Modify and tune the analytic based on data ingested. For instance, it may be worth running a broad query for jsp file writes first before performing a join.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the process java writing a .jsp to disk. This is potentially indicative of a web shell being written to disk. Modify and tune the analytic based on data ingested. For instance, it may be worth running a broad query for jsp file writes first before performing a join.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives are possible and filtering may be required. Restrict by assets or filter known jsp files that are common for the environment.
action.escu.creation_date = 2022-06-03
action.escu.modification_date = 2022-06-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Java Writing JSP File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spring4Shell CVE-2022-22965", "Atlassian Confluence Server and Data Center CVE-2022-26134"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ writing a jsp file $file_name$ to disk, potentially indicative of exploitation.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Java Writing JSP File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spring4Shell CVE-2022-22965", "Atlassian Confluence Server and Data Center CVE-2022-26134"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2022-22965"], "impact": 60, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the process java writing a .jsp to disk. This is potentially indicative of a web shell being written to disk. Modify and tune the analytic based on data ingested. For instance, it may be worth running a broad query for jsp file writes first before performing a join.
action.notable.param.rule_title = Java Writing JSP File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("java","java.exe", "javaw.exe") by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.jsp*" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.user | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time) as lastTime by dest process_name process_guid file_name file_path file_create_time user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `java_writing_jsp_file_filter`

[ESCU - Jscript Execution Using Cscript App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a execution of jscript using cscript process. Commonly when a user run jscript file it was executed by wscript.exe application. This technique was seen in FIN7 js implant to execute its malicious script using cscript process. This behavior is uncommon and a good artifacts to check further anomalies within the network
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a execution of jscript using cscript process. Commonly when a user run jscript file it was executed by wscript.exe application. This technique was seen in FIN7 js implant to execute its malicious script using cscript process. This behavior is uncommon and a good artifacts to check further anomalies within the network
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Jscript Execution Using Cscript App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["FIN7", "Remcos"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ with commandline $process$ to execute jscript in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Jscript Execution Using Cscript App - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7", "Remcos"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a execution of jscript using cscript process. Commonly when a user run jscript file it was executed by wscript.exe application. This technique was seen in FIN7 js implant to execute its malicious script using cscript process. This behavior is uncommon and a good artifacts to check further anomalies within the network
action.notable.param.rule_title = Jscript Execution Using Cscript App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "cscript.exe" AND Processes.parent_process = "*//e:jscript*") OR (Processes.process_name = "cscript.exe" AND Processes.process = "*//e:jscript*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jscript_execution_using_cscript_app_filter`

[ESCU - Kerberoasting spn request with RC4 encryption - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain. This analytic looks for a specific combination of the Ticket_Options field based on common kerberoasting tools. Defenders should be aware that it may be possible for a Kerberoast attack to use different Ticket_Options.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain. This analytic looks for a specific combination of the Ticket_Options field based on common kerberoasting tools. Defenders should be aware that it may be possible for a Kerberoast attack to use different Ticket_Options.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = Older systems that support kerberos RC4 by default like NetApp may generate false positives. Filter as needed
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kerberoasting spn request with RC4 encryption - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation", "Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Potential kerberoasting attack via service principal name requests detected on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kerberoasting spn request with RC4 encryption - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation", "Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain. This analytic looks for a specific combination of the Ticket_Options field based on common kerberoasting tools. Defenders should be aware that it may be possible for a Kerberoast attack to use different Ticket_Options.
action.notable.param.rule_title = Kerberoasting spn request with RC4 encryption
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4769 Service_Name!="*$" (Ticket_Options=0x40810000 OR Ticket_Options=0x40800000 OR Ticket_Options=0x40810010) Ticket_Encryption_Type=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter`

[ESCU - Kerberos Pre-Authentication Flag Disabled in UserAccountControl - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Windows Security Event 4738, `A user account was changed`, to identify a change performed on a domain user object that disables Kerberos Pre-Authentication. Disabling the Pre Authentication flag in the UserAccountControl property allows an adversary to easily perform a brute force attack against the user's password offline leveraging the ASP REP Roasting technique. Red Teams and adversaries alike who have obtained privileges in an Active Directory network may use this technique as a backdoor or a way to escalate privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic leverages Windows Security Event 4738, `A user account was changed`, to identify a change performed on a domain user object that disables Kerberos Pre-Authentication. Disabling the Pre Authentication flag in the UserAccountControl property allows an adversary to easily perform a brute force attack against the user's password offline leveraging the ASP REP Roasting technique. Red Teams and adversaries alike who have obtained privileges in an Active Directory network may use this technique as a backdoor or a way to escalate privileges.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `User Account Management` within `Account Management` needs to be enabled.
action.escu.known_false_positives = Unknown.
action.escu.creation_date = 2022-02-22
action.escu.modification_date = 2022-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kerberos Pre-Authentication Flag Disabled in UserAccountControl - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Kerberos Pre Authentication was Disabled for $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "other", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kerberos Pre-Authentication Flag Disabled in UserAccountControl - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Windows Security Event 4738, `A user account was changed`, to identify a change performed on a domain user object that disables Kerberos Pre-Authentication. Disabling the Pre Authentication flag in the UserAccountControl property allows an adversary to easily perform a brute force attack against the user's password offline leveraging the ASP REP Roasting technique. Red Teams and adversaries alike who have obtained privileges in an Active Directory network may use this technique as a backdoor or a way to escalate privileges.
action.notable.param.rule_title = Kerberos Pre-Authentication Flag Disabled in UserAccountControl
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4738 MSADChangedAttributes="*Don't Require Preauth' - Enabled*" |rename Account_Name as user | table EventCode, user, dest, Security_ID, MSADChangedAttributes | `kerberos_pre_authentication_flag_disabled_in_useraccountcontrol_filter`

[ESCU - Kerberos Pre-Authentication Flag Disabled with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Set-ADAccountControl` commandlet with specific parameters. `Set-ADAccountControl` is part of the Active Directory PowerShell module used to manage Windows Active Directory networks. As the name suggests, `Set-ADAccountControl` is used to modify User Account Control values for an Active Directory domain account. With the appropiate parameters, Set-ADAccountControl allows adversaries to disable Kerberos Pre-Authentication for an account to to easily perform a brute force attack against the user's password offline leveraging the ASP REP Roasting technique. Red Teams and adversaries alike who have obtained privileges in an Active Directory network may use this technique as a backdoor or a way to escalate privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Set-ADAccountControl` commandlet with specific parameters. `Set-ADAccountControl` is part of the Active Directory PowerShell module used to manage Windows Active Directory networks. As the name suggests, `Set-ADAccountControl` is used to modify User Account Control values for an Active Directory domain account. With the appropiate parameters, Set-ADAccountControl allows adversaries to disable Kerberos Pre-Authentication for an account to to easily perform a brute force attack against the user's password offline leveraging the ASP REP Roasting technique. Red Teams and adversaries alike who have obtained privileges in an Active Directory network may use this technique as a backdoor or a way to escalate privileges.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Although unlikely, Administrators may need to set this flag for legitimate purposes.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kerberos Pre-Authentication Flag Disabled with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Kerberos Pre Authentication was Disabled using PowerShell on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kerberos Pre-Authentication Flag Disabled with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1558", "T1558.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Set-ADAccountControl` commandlet with specific parameters. `Set-ADAccountControl` is part of the Active Directory PowerShell module used to manage Windows Active Directory networks. As the name suggests, `Set-ADAccountControl` is used to modify User Account Control values for an Active Directory domain account. With the appropiate parameters, Set-ADAccountControl allows adversaries to disable Kerberos Pre-Authentication for an account to to easily perform a brute force attack against the user's password offline leveraging the ASP REP Roasting technique. Red Teams and adversaries alike who have obtained privileges in an Active Directory network may use this technique as a backdoor or a way to escalate privileges.
action.notable.param.rule_title = Kerberos Pre-Authentication Flag Disabled with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*Set-ADAccountControl*" AND ScriptBlockText="*DoesNotRequirePreAuth:$true*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kerberos_pre_authentication_flag_disabled_with_powershell_filter`

[ESCU - Kerberos Service Ticket Request Using RC4 Encryption - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential Kerberos Service Ticket request related to a Golden Ticket attack. Adversaries who have obtained the Krbtgt account NTLM password hash may forge a Kerberos Granting Ticket (TGT) to obtain unrestricted access to an Active Directory environment. Armed with a Golden Ticket, attackers can request service tickets to move laterally and execute code on remote systems. Looking for Kerberos Service Ticket requests using the legacy RC4 encryption mechanism could represent the second stage of a Golden Ticket attack. RC4 usage should be rare on a modern network since Windows Vista & Windows Sever 2008 and newer support AES Kerberos encryption.\ Defenders should note that if an attacker does not leverage the NTLM password hash but rather the AES key to create a golden ticket, this detection may be bypassed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential Kerberos Service Ticket request related to a Golden Ticket attack. Adversaries who have obtained the Krbtgt account NTLM password hash may forge a Kerberos Granting Ticket (TGT) to obtain unrestricted access to an Active Directory environment. Armed with a Golden Ticket, attackers can request service tickets to move laterally and execute code on remote systems. Looking for Kerberos Service Ticket requests using the legacy RC4 encryption mechanism could represent the second stage of a Golden Ticket attack. RC4 usage should be rare on a modern network since Windows Vista & Windows Sever 2008 and newer support AES Kerberos encryption.\ Defenders should note that if an attacker does not leverage the NTLM password hash but rather the AES key to create a golden ticket, this detection may be bypassed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = Based on Microsoft documentation, legacy systems or applications will use RC4-HMAC as the default encryption for Kerberos Service Ticket requests. Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256.
action.escu.creation_date = 2022-03-15
action.escu.modification_date = 2022-03-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kerberos Service Ticket Request Using RC4 Encryption - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = A Kerberos Service TTicket request with RC4 encryption was requested from $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kerberos Service Ticket Request Using RC4 Encryption - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1558", "T1558.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential Kerberos Service Ticket request related to a Golden Ticket attack. Adversaries who have obtained the Krbtgt account NTLM password hash may forge a Kerberos Granting Ticket (TGT) to obtain unrestricted access to an Active Directory environment. Armed with a Golden Ticket, attackers can request service tickets to move laterally and execute code on remote systems. Looking for Kerberos Service Ticket requests using the legacy RC4 encryption mechanism could represent the second stage of a Golden Ticket attack. RC4 usage should be rare on a modern network since Windows Vista & Windows Sever 2008 and newer support AES Kerberos encryption.\ Defenders should note that if an attacker does not leverage the NTLM password hash but rather the AES key to create a golden ticket, this detection may be bypassed.
action.notable.param.rule_title = Kerberos Service Ticket Request Using RC4 Encryption
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4769 Service_Name="*$" (Ticket_Options=0x40810000 OR Ticket_Options=0x40800000 OR Ticket_Options=0x40810010) Ticket_Encryption_Type=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberos_service_ticket_request_using_rc4_encryption_filter`

[ESCU - Kerberos TGT Request Using RC4 Encryption - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event 4768, A Kerberos authentication ticket (TGT) was requested, to identify a TGT request with encryption type 0x17, or RC4-HMAC. This encryption type is no longer utilized by newer systems and could represent evidence of an OverPass The Hash attack. Similar to Pass The Hash, OverPass The Hash is a form of credential theft that allows adversaries to move laterally or consume resources in a target network. Leveraging this attack, an adversary who has stolen the NTLM hash of a valid domain account is able to authenticate to the Kerberos Distribution Center(KDC) on behalf of the legitimate account and obtain a Kerberos TGT ticket. Depending on the privileges of the compromised account, this ticket may be used to obtain unauthorized access to systems and other network resources.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event 4768, A Kerberos authentication ticket (TGT) was requested, to identify a TGT request with encryption type 0x17, or RC4-HMAC. This encryption type is no longer utilized by newer systems and could represent evidence of an OverPass The Hash attack. Similar to Pass The Hash, OverPass The Hash is a form of credential theft that allows adversaries to move laterally or consume resources in a target network. Leveraging this attack, an adversary who has stolen the NTLM hash of a valid domain account is able to authenticate to the Kerberos Distribution Center(KDC) on behalf of the legitimate account and obtain a Kerberos TGT ticket. Depending on the privileges of the compromised account, this ticket may be used to obtain unauthorized access to systems and other network resources.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = Based on Microsoft documentation, legacy systems or applications will use RC4-HMAC as the default encryption for TGT requests. Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256.
action.escu.creation_date = 2022-03-04
action.escu.modification_date = 2022-03-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kerberos TGT Request Using RC4 Encryption - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = A Kerberos TGT request with RC4 encryption was requested for $Account_Name$ from $Client_Address$
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kerberos TGT Request Using RC4 Encryption - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1550"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event 4768, A Kerberos authentication ticket (TGT) was requested, to identify a TGT request with encryption type 0x17, or RC4-HMAC. This encryption type is no longer utilized by newer systems and could represent evidence of an OverPass The Hash attack. Similar to Pass The Hash, OverPass The Hash is a form of credential theft that allows adversaries to move laterally or consume resources in a target network. Leveraging this attack, an adversary who has stolen the NTLM hash of a valid domain account is able to authenticate to the Kerberos Distribution Center(KDC) on behalf of the legitimate account and obtain a Kerberos TGT ticket. Depending on the privileges of the compromised account, this ticket may be used to obtain unauthorized access to systems and other network resources.
action.notable.param.rule_title = Kerberos TGT Request Using RC4 Encryption
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4768 Ticket_Encryption_Type=0x17 Account_Name!=*$ | stats count min(_time) as firstTime max(_time) as lastTime by Account_Name Client_Address dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kerberos_tgt_request_using_rc4_encryption_filter`

[ESCU - Kerberos User Enumeration - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event Id 4768, A Kerberos authentication ticket (TGT) was requested, to identify one source endpoint trying to obtain an unusual number Kerberos TGT ticket for non existing users. This behavior could represent an adversary abusing the Kerberos protocol to perform a user enumeration attack against an Active Directory environment. When Kerberos is sent a TGT request with no preauthentication for an invalid username, it responds with KRB5KDC_ERR_C_PRINCIPAL_UNKNOWN or 0x6. Red teams and adversaries alike may abuse the Kerberos protocol to validate a list of users use them to perform further attacks.\ The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number requests. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1589", "T1589.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event Id 4768, A Kerberos authentication ticket (TGT) was requested, to identify one source endpoint trying to obtain an unusual number Kerberos TGT ticket for non existing users. This behavior could represent an adversary abusing the Kerberos protocol to perform a user enumeration attack against an Active Directory environment. When Kerberos is sent a TGT request with no preauthentication for an invalid username, it responds with KRB5KDC_ERR_C_PRINCIPAL_UNKNOWN or 0x6. Red teams and adversaries alike may abuse the Kerberos protocol to validate a list of users use them to perform further attacks.\ The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number requests. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
action.escu.creation_date = 2022-03-10
action.escu.modification_date = 2022-03-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Kerberos User Enumeration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based user enumeration attack $Client_Address$
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Kerberos User Enumeration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1589", "T1589.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4768 Result_Code=0x6 Account_Name!="*$" | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `kerberos_user_enumeration_filter`

[ESCU - Known Services Killed by Ransomware - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects a suspicioous termination of known services killed by ransomware before encrypting files in a compromised machine. This technique is commonly seen in most of ransomware now a days to avoid exception error while accessing the targetted files it wants to encrypts because of the open handle of those services to the targetted file.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects a suspicioous termination of known services killed by ransomware before encrypting files in a compromised machine. This technique is commonly seen in most of ransomware now a days to avoid exception error while accessing the targetted files it wants to encrypts because of the open handle of those services to the targetted file.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the 7036 EventCode ScManager in System audit Logs from your endpoints.
action.escu.known_false_positives = Admin activities or installing related updates may do a sudden stop to list of services we monitor.
action.escu.creation_date = 2021-06-04
action.escu.modification_date = 2021-06-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Known Services Killed by Ransomware - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ransomware", "BlackMatter Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = Known services $Message$ terminated by a potential ransomware on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "Message", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Known Services Killed by Ransomware - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "BlackMatter Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects a suspicioous termination of known services killed by ransomware before encrypting files in a compromised machine. This technique is commonly seen in most of ransomware now a days to avoid exception error while accessing the targetted files it wants to encrypts because of the open handle of those services to the targetted file.
action.notable.param.rule_title = Known Services Killed by Ransomware
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7036 Message IN ("*Volume Shadow Copy*","*VSS*", "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*", "DefWatch", "ccEvtMgr", "ccSetMgr", "SavRoam", "RTVscan", "QBFCService", "QBIDPService", "Intuit.QuickBooks.FCS", "QBCFMonitorService" "YooBackup", "YooIT", "*Veeam*", "PDVFSService", "BackupExecVSSProvider", "BackupExecAgentAccelerator", "BackupExec*", "WdBoot", "WdFilter", "WdNisDrv", "WdNisSvc", "WinDefend", "wscsvc", "Sense", "sppsvc", "SecurityHealthService") Message="*service entered the stopped state*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message dest Type | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `known_services_killed_by_ransomware_filter`

[ESCU - Linux Account Manipulation Of SSH Config and Keys - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a deletion of ssh key in a linux machine. attacker may delete or modify ssh key to impair some security features or act as defense evasion in compromised linux machine. This Anomaly can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a deletion of ssh key in a linux machine. attacker may delete or modify ssh key to impair some security features or act as defense evasion in compromised linux machine. This Anomaly can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Account Manipulation Of SSH Config and Keys - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Acidrain"]
action.risk = 1
action.risk.param._risk_message = SSH Config and keys are deleted on $dest$ by Process GUID - $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Account Manipulation Of SSH Config and Keys - Rule
action.correlationsearch.annotations = {"analytic_story": ["Acidrain"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted AND Filesystem.file_path IN ("/etc/ssh/*", "~/.ssh/*") by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_account_manipulation_of_ssh_config_and_keys_filter`

[ESCU - Linux Add Files In Known Crontab Directories - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic aims to detect unauthorized activities through suspicious file creation in recognized cron table directories, prevalent Unix-based locations for scheduling tasks. This behavior is often exploited by nefarious entities like malware or threat actors, including red teamers, to establish persistence on a targeted or compromised host. The analogy to Windows-based scheduled tasks helps explain the utility of a crontab or cron job. To enhance clarity and actionable intelligence, the anomaly query flags the anomaly, urging further investigation into the added file's details. A cybersecurity analyst should consider additional data points such as the user identity involved, the file's nature and purpose, file origin, timestamp, and any changes in system behavior post file execution. This comprehensive understanding aids in accurately determining the file's legitimacy, facilitating prompt and effective response actions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic aims to detect unauthorized activities through suspicious file creation in recognized cron table directories, prevalent Unix-based locations for scheduling tasks. This behavior is often exploited by nefarious entities like malware or threat actors, including red teamers, to establish persistence on a targeted or compromised host. The analogy to Windows-based scheduled tasks helps explain the utility of a crontab or cron job. To enhance clarity and actionable intelligence, the anomaly query flags the anomaly, urging further investigation into the added file's details. A cybersecurity analyst should consider additional data points such as the user identity involved, the file's nature and purpose, file origin, timestamp, and any changes in system behavior post file execution. This comprehensive understanding aids in accurately determining the file's legitimacy, facilitating prompt and effective response actions.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can create file in crontab folders for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-17
action.escu.modification_date = 2021-12-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Add Files In Known Crontab Directories - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = a file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Add Files In Known Crontab Directories - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/cron*", "*/var/spool/cron/*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_add_files_in_known_crontab_directories_filter`

[ESCU - Linux Add User Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for commands to create user accounts on the linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to persist on the targeted or compromised host by creating new user with an elevated privilege. This Hunting query may catch normal creation of user by administrator so filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for commands to create user accounts on the linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to persist on the targeted or compromised host by creating new user with an elevated privilege. This Hunting query may catch normal creation of user by administrator so filter is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Add User Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Add User Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name IN ("useradd", "adduser") OR Processes.process IN ("*useradd *", "*adduser *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_add_user_account_filter`

[ESCU - Linux Adding Crontab Using List Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious modifications to cron jobs on Linux systems using the crontab command with list parameters. This command line parameter can be abused by malware like Industroyer2, as well as adversaries and red teamers, to add a crontab entry for executing their malicious code on a schedule of their choice. However, it's important to note that administrators or normal users may also use this command for legitimate automation purposes, so filtering is required to minimize false positives. Identifying the modification of cron jobs using list parameters is valuable for a SOC as it indicates potential malicious activity or an attempt to establish persistence on the system. If a true positive is detected, further investigation should be conducted to analyze the added cron job, its associated command, and the impact it may have on the system. This includes examining the purpose of the job, reviewing any on-disk artifacts, and identifying any related processes or activities occurring concurrently. The impact of a true positive can range from unauthorized execution of malicious code to data destruction or other damaging outcomes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies suspicious modifications to cron jobs on Linux systems using the crontab command with list parameters. This command line parameter can be abused by malware like Industroyer2, as well as adversaries and red teamers, to add a crontab entry for executing their malicious code on a schedule of their choice. However, it's important to note that administrators or normal users may also use this command for legitimate automation purposes, so filtering is required to minimize false positives. Identifying the modification of cron jobs using list parameters is valuable for a SOC as it indicates potential malicious activity or an attempt to establish persistence on the system. If a true positive is detected, further investigation should be conducted to analyze the added cron job, its associated command, and the impact it may have on the system. This includes examining the purpose of the job, reviewing any on-disk artifacts, and identifying any related processes or activities occurring concurrently. The impact of a true positive can range from unauthorized execution of malicious code to data destruction or other damaging outcomes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Adding Crontab Using List Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Industroyer2", "Linux Privilege Escalation", "Linux Living Off The Land", "Data Destruction", "Linux Persistence Techniques", "Scheduled Tasks"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Adding Crontab Using List Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Industroyer2", "Linux Privilege Escalation", "Linux Living Off The Land", "Data Destruction", "Linux Persistence Techniques", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "crontab" Processes.process= "* -l*" by  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_adding_crontab_using_list_parameter_filter`

[ESCU - Linux apt-get Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The apt-get is a command line tool for interacting with the Advanced Package Tool (APT) library (a package management system for Linux distributions). It allows you to search for, install, manage, update, and remove software. The tool does not build software from the source code. If sudo right is given to the tool for user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The apt-get is a command line tool for interacting with the Advanced Package Tool (APT) library (a package management system for Linux distributions). It allows you to search for, install, manage, update, and remove software. The tool does not build software from the source code. If sudo right is given to the tool for user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux apt-get Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux apt-get Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt-get*" AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_apt_get_privilege_escalation_filter`

[ESCU - Linux APT Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Advanced Package Tool, more commonly known as APT, is a collection of tools used to install, update, remove, and otherwise manage software packages on Debian and its derivative operating systems, including Ubuntu and Linux Mint. If sudo right is given to the tool for user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Advanced Package Tool, more commonly known as APT, is a collection of tools used to install, update, remove, and otherwise manage software packages on Debian and its derivative operating systems, including Ubuntu and Linux Mint. If sudo right is given to the tool for user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux APT Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux APT Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt*" AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_apt_privilege_escalation_filter`

[ESCU - Linux At Allow Config File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of suspicious configuration files, /etc/at.allow or /etc/at.deny, in Linux. These files are commonly abused by malware, adversaries, or red teamers to establish persistence on compromised hosts. The configuration files determine which users are allowed to execute the "at" application, which is used for scheduling tasks in Linux. Attackers can add their user or a compromised username to these files to execute malicious code using "at." It's important to consider potential false positives as administrators or network operators may create these files for legitimate automation purposes. Adjust the filter macros to minimize false positives.\
Identifying the creation of these configuration files is valuable for a SOC as it indicates potential unauthorized activities or an attacker attempting to establish persistence. If a true positive is found, further investigation is necessary to examine the contents of the created configuration file and determine the source of creation. The impact of a true positive can vary but could result in unauthorized execution of malicious code, data theft, or other detrimental consequences. Analysts should review the file path, creation time, and associated processes to assess the extent of the attack and initiate appropriate response actions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of suspicious configuration files, /etc/at.allow or /etc/at.deny, in Linux. These files are commonly abused by malware, adversaries, or red teamers to establish persistence on compromised hosts. The configuration files determine which users are allowed to execute the "at" application, which is used for scheduling tasks in Linux. Attackers can add their user or a compromised username to these files to execute malicious code using "at." It's important to consider potential false positives as administrators or network operators may create these files for legitimate automation purposes. Adjust the filter macros to minimize false positives.\
Identifying the creation of these configuration files is valuable for a SOC as it indicates potential unauthorized activities or an attacker attempting to establish persistence. If a true positive is found, further investigation is necessary to examine the contents of the created configuration file and determine the source of creation. The impact of a true positive can vary but could result in unauthorized execution of malicious code, data theft, or other detrimental consequences. Analysts should review the file path, creation time, and associated processes to assess the extent of the attack and initiate appropriate response actions.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can create this file for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-17
action.escu.modification_date = 2021-12-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux At Allow Config File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux At Allow Config File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/at.allow", "*/etc/at.deny") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_at_allow_config_file_creation_filter`

[ESCU - Linux At Application Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. The "At" application can be used for automation purposes by administrators or network operators, so the filter macros should be updated to remove false positives. If a true positive is found, it suggests an attacker is trying to maintain access to the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. To implement this analytic, ensure you are ingesting logs with the required fields from your endpoints into the Endpoint datamodel. When a true positive is detected, it suggests that an attacker is attempting to establish persistence or deliver additional malicious payloads by leveraging the "At" application. This behavior can lead to data theft, ransomware attacks, or other damaging outcomes.\
During triage, the SOC analyst should review the context surrounding the execution of the "At" application. This includes identifying the user, the parent process responsible for invoking the application, and the specific command-line arguments used. It is important to consider whether the execution is expected behavior by an administrator or network operator for legitimate automation purposes.\
The presence of "At" application execution may indicate an attacker's attempt to maintain unauthorized access to the environment. Immediate investigation and response are necessary to mitigate further risks, identify the attacker's intentions, and prevent potential harm to the organization.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.002", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. The "At" application can be used for automation purposes by administrators or network operators, so the filter macros should be updated to remove false positives. If a true positive is found, it suggests an attacker is trying to maintain access to the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. To implement this analytic, ensure you are ingesting logs with the required fields from your endpoints into the Endpoint datamodel. When a true positive is detected, it suggests that an attacker is attempting to establish persistence or deliver additional malicious payloads by leveraging the "At" application. This behavior can lead to data theft, ransomware attacks, or other damaging outcomes.\
During triage, the SOC analyst should review the context surrounding the execution of the "At" application. This includes identifying the user, the parent process responsible for invoking the application, and the specific command-line arguments used. It is important to consider whether the execution is expected behavior by an administrator or network operator for legitimate automation purposes.\
The presence of "At" application execution may indicate an attacker's attempt to maintain unauthorized access to the environment. Immediate investigation and response are necessary to mitigate further risks, identify the attacker's intentions, and prevent potential harm to the organization.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux At Application Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = At application was executed in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux At Application Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1053.002", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where  Processes.process_name IN ("at", "atd") OR Processes.parent_process_name IN ("at", "atd") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_at_application_execution_filter`

[ESCU - Linux AWK Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Awk is mostly used for processing and scanning patterns. It checks one or more files to determine whether any lines fit the specified patterns, and if so, it does the appropriate action. If sudo right is given to AWK binary for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Awk is mostly used for processing and scanning patterns. It checks one or more files to determine whether any lines fit the specified patterns, and if so, it does the appropriate action. If sudo right is given to AWK binary for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-07-31
action.escu.modification_date = 2022-07-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux AWK Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux AWK Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*" AND Processes.process="*awk*"  AND Processes.process="*BEGIN*system*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_awk_privilege_escalation_filter`

[ESCU - Linux Busybox Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = BusyBox combines tiny versions of many common UNIX utilities into a single small executable. It provides minimalist replacements for most of the utilities you usually find in GNU coreutils, util-linux, etc. If sudo right is given to BusyBox application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = BusyBox combines tiny versions of many common UNIX utilities into a single small executable. It provides minimalist replacements for most of the utilities you usually find in GNU coreutils, util-linux, etc. If sudo right is given to BusyBox application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Busybox Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Busybox Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*busybox*" AND Processes.process="*sh*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_busybox_privilege_escalation_filter`

[ESCU - Linux c89 Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The c89 and cc commands compile, assemble, and link-edit C programs; the cxx or c++ command does the same for C++ programs. The c89 command should be used when compiling C programs that are written according to Standard C. If sudo right is given to c89 application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The c89 and cc commands compile, assemble, and link-edit C programs; the cxx or c++ command does the same for C++ programs. The c89 command should be used when compiling C programs that are written according to Standard C. If sudo right is given to c89 application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux c89 Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux c89 Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*c89*" AND Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_c89_privilege_escalation_filter`

[ESCU - Linux c99 Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The c99 utility is an interface to the standard C compilation system; it shall accept source code conforming to the ISO C standard. The system conceptually consists of a compiler and link editor. If sudo right is given to ruby application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The c99 utility is an interface to the standard C compilation system; it shall accept source code conforming to the ISO C standard. The system conceptually consists of a compiler and link editor. If sudo right is given to ruby application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux c99 Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux c99 Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*c99*" AND Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_c99_privilege_escalation_filter`

[ESCU - Linux Change File Owner To Root - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for a commandline that change the file owner to root using chown utility tool. This technique is commonly abuse by adversaries, malware author and red teamers to escalate privilege to the targeted or compromised host by changing the owner of their malicious file to root. This event is not so common in corporate network except from the administrator doing normal task that needs high privilege.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222.002", "T1222"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for a commandline that change the file owner to root using chown utility tool. This technique is commonly abuse by adversaries, malware author and red teamers to escalate privilege to the targeted or compromised host by changing the owner of their malicious file to root. This event is not so common in corporate network except from the administrator doing normal task that needs high privilege.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Change File Owner To Root - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may change ownership to root on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Change File Owner To Root - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1222.002", "T1222"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = chown OR Processes.process = "*chown *") AND Processes.process = "* root *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_change_file_owner_to_root_filter`

[ESCU - Linux Clipboard Data Copy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Linux Xclip copying data out of the clipboard. Adversaries have utilized this technique to capture passwords, IP addresses, or store payloads.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1115"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Linux Xclip copying data out of the clipboard. Adversaries have utilized this technique to capture passwords, IP addresses, or store payloads.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present on Linux desktop as it may commonly be used by administrators or end users. Filter as needed.
action.escu.creation_date = 2022-07-28
action.escu.modification_date = 2022-07-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Clipboard Data Copy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ by user $user$ adding or removing content from the clipboard.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 16}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 16}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 16}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Clipboard Data Copy - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 40, "impact": 40, "mitre_attack": ["T1115"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=xclip Processes.process IN ("*-o *", "*-sel *", "*-selection *", "*clip *","*clipboard*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_clipboard_data_copy_filter`

[ESCU - Linux Common Process For Elevation Control - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for possible elevation control access using a common known process in linux platform to change the attribute and file ownership. This technique is commonly abused by adversaries, malware author and red teamers to gain persistence or privilege escalation on the target or compromised host. This common process is used to modify file attribute, file ownership or SUID. This tools can be used in legitimate purposes so filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.001", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for possible elevation control access using a common known process in linux platform to change the attribute and file ownership. This technique is commonly abused by adversaries, malware author and red teamers to gain persistence or privilege escalation on the target or compromised host. This common process is used to modify file attribute, file ownership or SUID. This tools can be used in legitimate purposes so filter is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-23
action.escu.modification_date = 2021-12-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Common Process For Elevation Control - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Common Process For Elevation Control - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1548.001", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("chmod", "chown", "fchmod", "fchmodat", "fchown", "fchownat", "fremovexattr", "fsetxattr", "lchown", "lremovexattr", "lsetxattr", "removexattr", "setuid", "setgid", "setreuid", "setregid", "chattr") OR Processes.process IN ("*chmod *", "*chown *", "*fchmod *", "*fchmodat *", "*fchown *", "*fchownat *", "*fremovexattr *", "*fsetxattr *", "*lchown *", "*lremovexattr *", "*lsetxattr *", "*removexattr *", "*setuid *", "*setgid *", "*setreuid *", "*setregid *", "*setcap *", "*chattr *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_common_process_for_elevation_control_filter`

[ESCU - Linux Composer Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Composer is a tool for dependency management in PHP. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you. If sudo right is given to tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Composer is a tool for dependency management in PHP. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you. If sudo right is given to tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Composer Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Composer Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*composer*" AND Processes.process="*run-script*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_composer_privilege_escalation_filter`

[ESCU - Linux Cpulimit Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = cpulimit is a simple program which attempts to limit the cpu usage of a process (expressed in percentage, not in cpu time). This is useful to control batch jobs, when you don't want them to eat too much cpu. If sudo right is given to the program for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = cpulimit is a simple program which attempts to limit the cpu usage of a process (expressed in percentage, not in cpu time). This is useful to control batch jobs, when you don't want them to eat too much cpu. If sudo right is given to the program for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Cpulimit Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 20}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Cpulimit Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*cpulimit*" AND Processes.process="*-l*" AND Processes.process="*-f*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_cpulimit_privilege_escalation_filter`

[ESCU - Linux Csvtool Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = csvtool is an easy to use command-line tool to work with .CSV files. If sudo right is given to the tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = csvtool is an easy to use command-line tool to work with .CSV files. If sudo right is given to the tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Csvtool Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Csvtool Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*csvtool*" AND Processes.process="*call*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_csvtool_privilege_escalation_filter`

[ESCU - Linux Curl Upload File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies curl being utilized with the -F or --form, --upload-file, -T, -d, --data, --data-raw, -I and --head switches to upload AWS credentials or config to a remote destination. This enables uploading of binary files and so forth. To force the 'content' part to be a file, prefix the file name with an @ sign. To just get the content part from a file, prefix the file name with the symbol <. The difference between @ and < is then that @ makes a file get attached in the post as a file upload, while the < makes a text field and just get the contents for that text field from a file. This technique was utlized by the TeamTNT group to exfiltrate AWS credentials.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies curl being utilized with the -F or --form, --upload-file, -T, -d, --data, --data-raw, -I and --head switches to upload AWS credentials or config to a remote destination. This enables uploading of binary files and so forth. To force the 'content' part to be a file, prefix the file name with an @ sign. To just get the content part from a file, prefix the file name with the symbol <. The difference between @ and < is then that @ makes a file get attached in the post as a file upload, while the < makes a text field and just get the contents for that text field from a file. This technique was utlized by the TeamTNT group to exfiltrate AWS credentials.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Filtering may be required. In addition to AWS credentials, add other important files and monitor. The inverse would be to look for _all_ -F behavior and tune from there.
action.escu.creation_date = 2022-07-29
action.escu.modification_date = 2022-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Curl Upload File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land", "Data Exfiltration", "Ingress Tool Transfer"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ by user $user$ attempting to upload important files to a remote destination.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Curl Upload File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land", "Data Exfiltration", "Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies curl being utilized with the -F or --form, --upload-file, -T, -d, --data, --data-raw, -I and --head switches to upload AWS credentials or config to a remote destination. This enables uploading of binary files and so forth. To force the 'content' part to be a file, prefix the file name with an @ sign. To just get the content part from a file, prefix the file name with the symbol <. The difference between @ and < is then that @ makes a file get attached in the post as a file upload, while the < makes a text field and just get the contents for that text field from a file. This technique was utlized by the TeamTNT group to exfiltrate AWS credentials.
action.notable.param.rule_title = Linux Curl Upload File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process IN ("*-F *", "*--form *","*--upload-file *","*-T *","*-d *","*--data *","*--data-raw *", "*-I *", "*--head *") AND Processes.process IN ("*.aws/credentials*". "*.aws/config*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_curl_upload_file_filter`

[ESCU - Linux Data Destruction Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a unix shell command that can wipe root folders of a linux host. This commandline is being abused by Awfulshred malware that wipes or corrupts files in a targeted Linux host. The shell command uses the rm command with force recursive deletion even in the root folder. This TTP can be a good indicator that a user or a process wants to wipe roots directory files in Linux host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a unix shell command that can wipe root folders of a linux host. This commandline is being abused by Awfulshred malware that wipes or corrupts files in a targeted Linux host. The shell command uses the rm command with force recursive deletion even in the root folder. This TTP can be a good indicator that a user or a process wants to wipe roots directory files in Linux host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Data Destruction Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ execute rm command with --no-preserve-root parmeter that can wipe root files in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Data Destruction Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a unix shell command that can wipe root folders of a linux host. This commandline is being abused by Awfulshred malware that wipes or corrupts files in a targeted Linux host. The shell command uses the rm command with force recursive deletion even in the root folder. This TTP can be a good indicator that a user or a process wants to wipe roots directory files in Linux host.
action.notable.param.rule_title = Linux Data Destruction Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND Processes.process IN ("* -rf*", "* -fr*") AND Processes.process = "* --no-preserve-root" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_data_destruction_command_filter`

[ESCU - Linux DD File Overwrite - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for dd command to overwrite file. This technique was abused by adversaries or threat actor to destroy files or data on specific system or in a large number of host within network to interrupt host avilability, services and many more. This is also used to destroy data where it make the file irrecoverable by forensic techniques through overwriting files, data or local and remote drives.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for dd command to overwrite file. This technique was abused by adversaries or threat actor to destroy files or data on specific system or in a large number of host within network to interrupt host avilability, services and many more. This is also used to destroy data where it make the file irrecoverable by forensic techniques through overwriting files, data or local and remote drives.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux DD File Overwrite - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux DD File Overwrite - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to look for dd command to overwrite file. This technique was abused by adversaries or threat actor to destroy files or data on specific system or in a large number of host within network to interrupt host avilability, services and many more. This is also used to destroy data where it make the file irrecoverable by forensic techniques through overwriting files, data or local and remote drives.
action.notable.param.rule_title = Linux DD File Overwrite
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dd" AND Processes.process = "*of=*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_dd_file_overwrite_filter`

[ESCU - Linux Decode Base64 to Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because  it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027", "T1059.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because  it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present based on legitimate software being utilized. Filter as needed.
action.escu.creation_date = 2022-07-27
action.escu.modification_date = 2022-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Decode Base64 to Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64 and passing it to a shell.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Decode Base64 to Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1027", "T1059.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because  it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions.
action.notable.param.rule_title = Linux Decode Base64 to Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*base64 -d*","*base64 --decode*") AND Processes.process="*|*" `linux_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_decode_base64_to_shell_filter`

[ESCU - Linux Deleting Critical Directory Using RM Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious deletion of a critical folder in Linux machine using rm command. This technique was seen in industroyer2 campaign to wipe or destroy energy facilities of a targeted sector. Deletion in these list of folder is not so common since it need some elevated privileges to access some of it. We recommend to look further events specially in file access or file deletion, process commandline that may related to this technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious deletion of a critical folder in Linux machine using rm command. This technique was seen in industroyer2 campaign to wipe or destroy energy facilities of a targeted sector. Deletion in these list of folder is not so common since it need some elevated privileges to access some of it. We recommend to look further events specially in file access or file deletion, process commandline that may related to this technique.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Deleting Critical Directory Using RM Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = A deletion in known critical list of folder using rm command $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Deleting Critical Directory Using RM Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious deletion of a critical folder in Linux machine using rm command. This technique was seen in industroyer2 campaign to wipe or destroy energy facilities of a targeted sector. Deletion in these list of folder is not so common since it need some elevated privileges to access some of it. We recommend to look further events specially in file access or file deletion, process commandline that may related to this technique.
action.notable.param.rule_title = Linux Deleting Critical Directory Using RM Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name =rm AND Processes.process= "* -rf *" AND Processes.process IN ("*/boot/*", "*/var/log/*", "*/etc/*", "*/dev/*") by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_deleting_critical_directory_using_rm_command_filter`

[ESCU - Linux Deletion Of Cron Jobs - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a deletion of cron job in a linux machine. This technique can be related to an attacker, threat actor or malware to disable scheduled cron jobs that might be related to security or to evade some detections. We also saw that this technique can be a good indicator for malware that is trying to wipe or delete several files on the compromised host like the acidrain malware. This anomaly detection can be a good pivot detection to look for process and user doing it why they doing. Take note that this event can be done by administrator so filtering on those possible false positive event is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a deletion of cron job in a linux machine. This technique can be related to an attacker, threat actor or malware to disable scheduled cron jobs that might be related to security or to evade some detections. We also saw that this technique can be a good indicator for malware that is trying to wipe or delete several files on the compromised host like the acidrain malware. This anomaly detection can be a good pivot detection to look for process and user doing it why they doing. Take note that this event can be done by administrator so filtering on those possible false positive event is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Deletion Of Cron Jobs - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AcidRain", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Linux cron jobs are deleted on host $dest$ by process GUID- $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Deletion Of Cron Jobs - Rule
action.correlationsearch.annotations = {"analytic_story": ["AcidRain", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path="/etc/cron.*" by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_cron_jobs_filter`

[ESCU - Linux Deletion Of Init Daemon Script - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a deletion of init daemon script in a linux machine. daemon script that place in /etc/init.d/ is a directory that can start and stop some daemon services in linux machines. attacker may delete or modify daemon script to impair some security features or act as defense evasion in a compromised linux machine. This TTP can be also a good indicator of a malware trying to wipe or delete several files in compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a deletion of init daemon script in a linux machine. daemon script that place in /etc/init.d/ is a directory that can start and stop some daemon services in linux machines. attacker may delete or modify daemon script to impair some security features or act as defense evasion in a compromised linux machine. This TTP can be also a good indicator of a malware trying to wipe or delete several files in compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Deletion Of Init Daemon Script - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AcidRain", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Init daemon script deleted on host $dest$ by process GUID- $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Deletion Of Init Daemon Script - Rule
action.correlationsearch.annotations = {"analytic_story": ["AcidRain", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a deletion of init daemon script in a linux machine. daemon script that place in /etc/init.d/ is a directory that can start and stop some daemon services in linux machines. attacker may delete or modify daemon script to impair some security features or act as defense evasion in a compromised linux machine. This TTP can be also a good indicator of a malware trying to wipe or delete several files in compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.notable.param.rule_title = Linux Deletion Of Init Daemon Script
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path IN ( "/etc/init.d/*") by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_init_daemon_script_filter`

[ESCU - Linux Deletion Of Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a deletion of services in a linux machine. attacker may delete or modify services to impair some security features or act as defense evasion in a compromised linux machine. This TTP can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a deletion of services in a linux machine. attacker may delete or modify services to impair some security features or act as defense evasion in a compromised linux machine. This TTP can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Deletion Of Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "AcidRain", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A services file $file_name$ deteted on host $dest$ by process GUID -  $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Deletion Of Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "AcidRain", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a deletion of services in a linux machine. attacker may delete or modify services to impair some security features or act as defense evasion in a compromised linux machine. This TTP can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.notable.param.rule_title = Linux Deletion Of Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path IN ( "/etc/systemd/*", "*/lib/systemd/*", "*/run/systemd/*") Filesystem.file_path = "*.service" by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_services_filter`

[ESCU - Linux Deletion of SSL Certificate - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a deletion of ssl certificate in a linux machine. attacker may delete or modify ssl certificate to impair some security features or act as defense evasion in compromised linux machine. This Anomaly can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a deletion of ssl certificate in a linux machine. attacker may delete or modify ssl certificate to impair some security features or act as defense evasion in compromised linux machine. This Anomaly can be also a good indicator of a malware trying to wipe or delete several files in a compromised host as part of its destructive payload like what acidrain malware does in linux or router machines. This detection can be a good pivot to check what process and user tries to delete this type of files which is not so common and need further investigation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Deletion of SSL Certificate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Acidrain"]
action.risk = 1
action.risk.param._risk_message = SSL certificate deleted on host $dest$ by process GUID- $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Deletion of SSL Certificate - Rule
action.correlationsearch.annotations = {"analytic_story": ["Acidrain"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path = "/etc/ssl/certs/*" Filesystem.file_path IN ("*.pem", "*.crt") by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_ssl_certificate_filter`

[ESCU - Linux Disable Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to detect events that attempts to disable a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to detect events that attempts to disable a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Disable Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable services on endpoint $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Disable Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is to detect events that attempts to disable a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
action.notable.param.rule_title = Linux Disable Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", "service", "svcadm")  Processes.process = "* disable*" by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_disable_services_filter`

[ESCU - Linux Doas Conf File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect the creation of doas.conf file in linux host platform. This configuration file can be use by doas utility tool to allow or permit standard users to perform tasks as root, the same way sudo does. This tool is developed as a minimalistic alternative to sudo application. This tool can be abused advesaries, attacker or malware to gain elevated privileges to the targeted or compromised host. On the other hand this can also be executed by administrator for a certain task that needs admin rights. In this case filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect the creation of doas.conf file in linux host platform. This configuration file can be use by doas utility tool to allow or permit standard users to perform tasks as root, the same way sudo does. This tool is developed as a minimalistic alternative to sudo application. This tool can be abused advesaries, attacker or malware to gain elevated privileges to the targeted or compromised host. On the other hand this can also be executed by administrator for a certain task that needs admin rights. In this case filter is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-05
action.escu.modification_date = 2022-01-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Doas Conf File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Doas Conf File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/doas.conf") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_doas_conf_file_creation_filter`

[ESCU - Linux Doas Tool Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect the doas tool execution in linux host platform. This utility tool allow standard users to perform tasks as root, the same way sudo does. This tool is developed as a minimalistic alternative to sudo application. This tool can be abused advesaries, attacker or malware to gain elevated privileges to the targeted or compromised host. On the other hand this can also be executed by administrator for a certain task that needs admin rights. In this case filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect the doas tool execution in linux host platform. This utility tool allow standard users to perform tasks as root, the same way sudo does. This tool is developed as a minimalistic alternative to sudo application. This tool can be abused advesaries, attacker or malware to gain elevated privileges to the targeted or compromised host. On the other hand this can also be executed by administrator for a certain task that needs admin rights. In this case filter is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-05
action.escu.modification_date = 2022-01-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Doas Tool Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A doas $process_name$ with commandline $process$ was executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Doas Tool Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "doas" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_doas_tool_execution_filter`

[ESCU - Linux Docker Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Docker is an open source containerization platform. It helps programmers to bundle applications into containers, which are standardized executable parts that include the application source code along with the OS libraries and dependencies needed to run that code in any setting. The user can add mount the root directory into a container and edit the /etc/password file to add a super user. This requires the user to be privileged enough to run docker, i.e. being in the docker group or being root.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Docker is an open source containerization platform. It helps programmers to bundle applications into containers, which are standardized executable parts that include the application source code along with the OS libraries and dependencies needed to run that code in any setting. The user can add mount the root directory into a container and edit the /etc/password file to add a super user. This requires the user to be privileged enough to run docker, i.e. being in the docker group or being root.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-07-31
action.escu.modification_date = 2022-07-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Docker Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 5}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Docker Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN("*docker*-v*/*:*","*docker*--volume*/*:*") OR Processes.process IN("*docker*exec*sh*","*docker*exec*bash*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_docker_privilege_escalation_filter`

[ESCU - Linux Edit Cron Table Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the suspicious editing of cron jobs in Linux via the crontab command-line parameter. This tactic could be used by adversaries or malware to schedule execution of their malicious code, potentially leading to system compromise or unauthorized persistent access. It pinpoints this activity by monitoring command-line executions involving 'crontab' and the edit parameter (-e).\
Recognizing such activity is vital for a SOC as cron job manipulations might signal unauthorized persistence attempts or scheduled malicious actions, potentially resulting in substantial harm. A true positive signifies an active threat, with implications ranging from unauthorized access to broader network compromise.\
To implement this analytic, logs capturing process name, parent process, and command-line executions from your endpoints must be ingested.\
Known false positives could stem from valid administrative tasks or automation processes using crontab. To reduce these, fine-tune the filter macros according to the benign activities within your environment. These adjustments ensure legitimate actions aren't mistaken for threats, allowing analysts to focus on genuine potential risks.\
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the suspicious editing of cron jobs in Linux via the crontab command-line parameter. This tactic could be used by adversaries or malware to schedule execution of their malicious code, potentially leading to system compromise or unauthorized persistent access. It pinpoints this activity by monitoring command-line executions involving 'crontab' and the edit parameter (-e).\
Recognizing such activity is vital for a SOC as cron job manipulations might signal unauthorized persistence attempts or scheduled malicious actions, potentially resulting in substantial harm. A true positive signifies an active threat, with implications ranging from unauthorized access to broader network compromise.\
To implement this analytic, logs capturing process name, parent process, and command-line executions from your endpoints must be ingested.\
Known false positives could stem from valid administrative tasks or automation processes using crontab. To reduce these, fine-tune the filter macros according to the benign activities within your environment. These adjustments ensure legitimate actions aren't mistaken for threats, allowing analysts to focus on genuine potential risks.\
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-17
action.escu.modification_date = 2021-12-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Edit Cron Table Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Edit Cron Table Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = crontab Processes.process = "*crontab *" Processes.process = "* -e*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_edit_cron_table_parameter_filter`

[ESCU - Linux Emacs Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = EMACS is a family of text editors that are characterized by their extensibility. The manual for the most widely used variant, GNU Emacs, describes it as "the extensible, customizable, self-documenting, real-time display editor". If sudo right is given to EMACS tool for the user, then the user can run special commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = EMACS is a family of text editors that are characterized by their extensibility. The manual for the most widely used variant, GNU Emacs, describes it as "the extensible, customizable, self-documenting, real-time display editor". If sudo right is given to EMACS tool for the user, then the user can run special commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Emacs Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 20}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Emacs Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*emacs*" AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_emacs_privilege_escalation_filter`

[ESCU - Linux File Created In Kernel Driver Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious file creation in kernel/driver directory in linux platform. This directory is known folder for all linux kernel module available within the system. so creation of file in this directory is a good indicator that there is a possible rootkit installation in the host machine. This technique was abuse by adversaries, malware author and red teamers to gain high privileges to their malicious code such us in kernel level. Even this event is not so common administrator or legitimate 3rd party tool may install driver or linux kernel module as part of its installation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.006", "T1547"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious file creation in kernel/driver directory in linux platform. This directory is known folder for all linux kernel module available within the system. so creation of file in this directory is a good indicator that there is a possible rootkit installation in the host machine. This technique was abuse by adversaries, malware author and red teamers to gain high privileges to their malicious code such us in kernel level. Even this event is not so common administrator or legitimate 3rd party tool may install driver or linux kernel module as part of its installation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can create file in this folders for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-22
action.escu.modification_date = 2021-12-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux File Created In Kernel Driver Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Rootkit"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux File Created In Kernel Driver Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Rootkit"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1547.006", "T1547"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/kernel/drivers/*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_created_in_kernel_driver_directory_filter`

[ESCU - Linux File Creation In Init Boot Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious file creation on init system directories for automatic execution of script or file upon boot up. This technique is commonly abuse by adversaries, malware author and red teamer to persist on the targeted or compromised host. This behavior can be executed or use by an administrator or network operator to add script files or binary files as part of a task or automation. filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1037.004", "T1037"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious file creation on init system directories for automatic execution of script or file upon boot up. This technique is commonly abuse by adversaries, malware author and red teamer to persist on the targeted or compromised host. This behavior can be executed or use by an administrator or network operator to add script files or binary files as part of a task or automation. filter is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase
action.escu.known_false_positives = Administrator or network operator can create file in this folders for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux File Creation In Init Boot Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux File Creation In Init Boot Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1037.004", "T1037"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/init.d/*", "*/etc/rc.d/*", "*/sbin/init.d/*", "*/etc/rc.local*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_creation_in_init_boot_directory_filter`

[ESCU - Linux File Creation In Profile Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious file creation in /etc/profile.d directory to automatically execute scripts by shell upon boot up of a linux machine. This technique is commonly abused by adversaries, malware and red teamers as a persistence mechanism to the targeted or compromised host. This Anomaly detection is a good indicator that someone wants to run a code after boot up which can be done also by the administrator or network operator for automation purposes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.004", "T1546"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious file creation in /etc/profile.d directory to automatically execute scripts by shell upon boot up of a linux machine. This technique is commonly abused by adversaries, malware and red teamers as a persistence mechanism to the targeted or compromised host. This Anomaly detection is a good indicator that someone wants to run a code after boot up which can be done also by the administrator or network operator for automation purposes.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can create file in profile.d folders for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux File Creation In Profile Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux File Creation In Profile Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1546.004", "T1546"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/profile.d/*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_creation_in_profile_directory_filter`

[ESCU - Linux Find Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Find is a command-line utility that locates files based on some user-specified criteria and either prints the pathname of each matched object or, if another action is requested, performs that action on each matched object. If sudo right is given to find utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Find is a command-line utility that locates files based on some user-specified criteria and either prints the pathname of each matched object or, if another action is requested, performs that action on each matched object. If sudo right is given to find utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Find Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 5}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Find Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*find*" AND Processes.process="*-exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_find_privilege_escalation_filter`

[ESCU - Linux GDB Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = GDB is the acronym for GNU Debugger. This tool helps to debug the programs written in C, C++, Ada, Fortran, etc. The console can be opened using the gdb command on terminal. If sudo right is given to GDB tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = GDB is the acronym for GNU Debugger. This tool helps to debug the programs written in C, C++, Ada, Fortran, etc. The console can be opened using the gdb command on terminal. If sudo right is given to GDB tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux GDB Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux GDB Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*gdb*" AND Processes.process="*-nx*" AND Processes.process="*-ex*!*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_gdb_privilege_escalation_filter`

[ESCU - Linux Gem Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = RubyGems is a package manager for the Ruby programming language that provides a standard format for distributing Ruby programs and libraries (in a self-contained format called a "gem"), a tool designed to easily manage the installation of gems, and a server for distributing them. If sudo right is given to GEM utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = RubyGems is a package manager for the Ruby programming language that provides a standard format for distributing Ruby programs and libraries (in a self-contained format called a "gem"), a tool designed to easily manage the installation of gems, and a server for distributing them. If sudo right is given to GEM utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Gem Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 10}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Gem Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*gem*open*-e*" AND Processes.process="*-c*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_gem_privilege_escalation_filter`

[ESCU - Linux GNU Awk Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = gawk command in Linux is used for pattern scanning and processing language. The awk command requires no compiling and allows the user to use variables, numeric functions, string functions, and logical operators. It is a utility that enables programmers to write tiny and effective programs in the form of statements that define text patterns that are to be searched for, in a text document and the action that is to be taken when a match is found within a line. If sudo right is given to gawk tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = gawk command in Linux is used for pattern scanning and processing language. The awk command requires no compiling and allows the user to use variables, numeric functions, string functions, and logical operators. It is a utility that enables programmers to write tiny and effective programs in the form of statements that define text patterns that are to be searched for, in a text document and the action that is to be taken when a match is found within a line. If sudo right is given to gawk tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux GNU Awk Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux GNU Awk Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*gawk*" AND Processes.process="*BEGIN*{system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_gnu_awk_privilege_escalation_filter`

[ESCU - Linux Hardware Addition SwapOff - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for process execution to disable the swapping of paging devices. This technique was seen in Awfulshred malware that disables the swapping of the specified devices and files. This anomaly detection can be a good indicator that a process or a user tries to disable this Linux feature in a targeted host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1200"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for process execution to disable the swapping of paging devices. This technique was seen in Awfulshred malware that disables the swapping of the specified devices and files. This anomaly detection can be a good indicator that a process or a user tries to disable this Linux feature in a targeted host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = administrator may disable swapping of devices in a linux host. Filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Hardware Addition SwapOff - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ swap off paging device in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Hardware Addition SwapOff - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1200"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "swapoff" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_hardware_addition_swapoff_filter`

[ESCU - Linux High Frequency Of File Deletion In Boot Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a high frequency of file deletion relative to process name and process id /boot/ folder. These events was seen in industroyer2 wiper malware where it tries to delete all files in a critical directory in linux directory. This detection already contains some filter that might cause false positive during our testing.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a high frequency of file deletion relative to process name and process id /boot/ folder. These events was seen in industroyer2 wiper malware where it tries to delete all files in a critical directory in linux directory. This detection already contains some filter that might cause false positive during our testing.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = linux package installer/uninstaller may cause this event. Please update you filter macro to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux High Frequency Of File Deletion In Boot Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = Multiple files detection in /boot/ folder on $dest$ by process GUID -  $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux High Frequency Of File Deletion In Boot Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a high frequency of file deletion relative to process name and process id /boot/ folder. These events was seen in industroyer2 wiper malware where it tries to delete all files in a critical directory in linux directory. This detection already contains some filter that might cause false positive during our testing.
action.notable.param.rule_title = Linux High Frequency Of File Deletion In Boot Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Filesystem.file_name) as deletedFileNames values(Filesystem.file_path) as deletedFilePath dc(Filesystem.file_path) as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path = "/boot/*" by _time span=1h  Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | where  numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_high_frequency_of_file_deletion_in_boot_folder_filter`

[ESCU - Linux High Frequency Of File Deletion In Etc Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a high frequency of file deletion relative to process name and process id /etc/ folder. These events was seen in acidrain wiper malware where it tries to delete all files in a non-standard directory in linux directory. This detection already contains some filter that might cause false positive during our testing. But we recommend to add more filter if needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a high frequency of file deletion relative to process name and process id /etc/ folder. These events was seen in acidrain wiper malware where it tries to delete all files in a non-standard directory in linux directory. This detection already contains some filter that might cause false positive during our testing. But we recommend to add more filter if needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = linux package installer/uninstaller may cause this event. Please update you filter macro to remove false positives.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux High Frequency Of File Deletion In Etc Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AcidRain", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Multiple files delted in /etc/ folder on $dest$ by process GUID -  $process_guid$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_guid", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux High Frequency Of File Deletion In Etc Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["AcidRain", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Filesystem.file_name) as deletedFileNames values(Filesystem.file_path) as deletedFilePath dc(Filesystem.file_path) as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path = "/etc/*" by _time span=1h  Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)`  | where  numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_high_frequency_of_file_deletion_in_etc_folder_filter`

[ESCU - Linux Impair Defenses Process Kill - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for PKILL process execution for possible termination of process. This technique is being used by several Threat actors, adversaries and red teamers to terminate processes in a targeted linux machine. This Hunting detection can be a good pivot to check a possible defense evasion technique or termination of security application in a linux host or wiper like Awfulshred that corrupt all files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for PKILL process execution for possible termination of process. This technique is being used by several Threat actors, adversaries and red teamers to terminate processes in a targeted linux machine. This Hunting detection can be a good pivot to check a possible defense evasion technique or termination of security application in a linux host or wiper like Awfulshred that corrupt all files.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network admin can terminate a process using this linux command. Filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Impair Defenses Process Kill - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Impair Defenses Process Kill - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ( "pgrep", "pkill") Processes.process = "*pkill *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_impair_defenses_process_kill_filter`

[ESCU - Linux Indicator Removal Clear Cache - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for processes that clear or free page cache in Linux system host. This technique was seen in Awfulshred malware wiper that tries to clear the cache using kernel system request drop_caches while wiping all files in the targeted host. This TTP detection can be a good indicator of user or process tries to clear page cache to delete tracks or might be a wiper like Awfulshred.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for processes that clear or free page cache in Linux system host. This technique was seen in Awfulshred malware wiper that tries to clear the cache using kernel system request drop_caches while wiping all files in the targeted host. This TTP detection can be a good indicator of user or process tries to clear page cache to delete tracks or might be a wiper like Awfulshred.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Indicator Removal Clear Cache - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ clear cache using kernel drop cache system request in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Indicator Removal Clear Cache - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for processes that clear or free page cache in Linux system host. This technique was seen in Awfulshred malware wiper that tries to clear the cache using kernel system request drop_caches while wiping all files in the targeted host. This TTP detection can be a good indicator of user or process tries to clear page cache to delete tracks or might be a wiper like Awfulshred.
action.notable.param.rule_title = Linux Indicator Removal Clear Cache
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", "sudo", "bash")  AND Processes.process  IN("* echo 3 &gt; *", "* echo 2 &gt; *","* echo 1 &gt; *") AND Processes.process = "*/proc/sys/vm/drop_caches" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_indicator_removal_clear_cache_filter`

[ESCU - Linux Indicator Removal Service File Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious linux processes that delete service unit configuration files. This technique was seen in several malware to delete service configuration files to corrupt a services or security product as part of its defense evasion. This TTP detection can be a good indicator of possible malware try to kill several services or a wiper like AwfulShred shell script that wipes the targeted linux host
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070.004", "T1070"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious linux processes that delete service unit configuration files. This technique was seen in several malware to delete service configuration files to corrupt a services or security product as part of its defense evasion. This TTP detection can be a good indicator of possible malware try to kill several services or a wiper like AwfulShred shell script that wipes the targeted linux host
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network admin can delete services unit configuration file as part of normal software installation. Filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Indicator Removal Service File Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ has a commandline $process$ to delete service configuration file in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Indicator Removal Service File Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1070.004", "T1070"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND Processes.process = "*rm *"  AND Processes.process = "*.service" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_indicator_removal_service_file_deletion_filter`

[ESCU - Linux Ingress Tool Transfer Hunting - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic hunts for curl and wget being utilized in the environment. This is meant to help with identifying normal usage and potentially malicious. Utilize this query to tune other curl and wget analytics.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic hunts for curl and wget being utilized in the environment. This is meant to help with identifying normal usage and potentially malicious. Utilize this query to tune other curl and wget analytics.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present. This query is meant to help tune other curl and wget analytics.
action.escu.creation_date = 2022-07-29
action.escu.modification_date = 2022-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Ingress Tool Transfer Hunting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land", "Ingress Tool Transfer"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Ingress Tool Transfer Hunting - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land", "Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 10, "impact": 10, "mitre_attack": ["T1105"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=curl OR Processes.process_name=wget) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ingress_tool_transfer_hunting_filter`

[ESCU - Linux Ingress Tool Transfer with Curl - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies curl with the command-line switches that are commonly used to download, output, a remote script or binary. MetaSploit Framework will combine the -sO switch with | chmod +x to enable a simple one liner to download and set the execute bit to run the file immediately. During triage, review the remote domain and file being downloaded for legitimacy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies curl with the command-line switches that are commonly used to download, output, a remote script or binary. MetaSploit Framework will combine the -sO switch with | chmod +x to enable a simple one liner to download and set the execute bit to run the file immediately. During triage, review the remote domain and file being downloaded for legitimacy.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present. Tune and then change type to TTP.
action.escu.creation_date = 2022-07-29
action.escu.modification_date = 2022-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Ingress Tool Transfer with Curl - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land", "Ingress Tool Transfer"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ by user $user$ to download a remote file. Review activity for further details.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Ingress Tool Transfer with Curl - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land", "Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 30, "impact": 40, "mitre_attack": ["T1105"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process, "(?i)(-O|-sO|-ksO|--output)") | `linux_ingress_tool_transfer_with_curl_filter`

[ESCU - Linux Insert Kernel Module Using Insmod Utility - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for inserting of linux kernel module using insmod utility function. This event can detect a installation of rootkit or malicious kernel module to gain elevated privileges to their malicious code and bypassed detections. This Anomaly detection is a good indicator that someone installing kernel module in a linux host either admin or adversaries. filter is needed in this scenario
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.006", "T1547"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for inserting of linux kernel module using insmod utility function. This event can detect a installation of rootkit or malicious kernel module to gain elevated privileges to their malicious code and bypassed detections. This Anomaly detection is a good indicator that someone installing kernel module in a linux host either admin or adversaries. filter is needed in this scenario
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-22
action.escu.modification_date = 2021-12-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Insert Kernel Module Using Insmod Utility - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Rootkit"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may install kernel module on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Insert Kernel Module Using Insmod Utility - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Rootkit"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1547.006", "T1547"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod", "sudo") AND Processes.process = *insmod* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_insert_kernel_module_using_insmod_utility_filter`

[ESCU - Linux Install Kernel Module Using Modprobe Utility - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for possible installing a linux kernel module using modprobe utility function. This event can detect a installation of rootkit or malicious kernel module to gain elevated privileges to their malicious code and bypassed detections. This Anomaly detection is a good indicator that someone installing kernel module in a linux host either admin or adversaries. filter is needed in this scenario
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.006", "T1547"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for possible installing a linux kernel module using modprobe utility function. This event can detect a installation of rootkit or malicious kernel module to gain elevated privileges to their malicious code and bypassed detections. This Anomaly detection is a good indicator that someone installing kernel module in a linux host either admin or adversaries. filter is needed in this scenario
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-22
action.escu.modification_date = 2021-12-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Install Kernel Module Using Modprobe Utility - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Rootkit"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may install kernel module on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Install Kernel Module Using Modprobe Utility - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Rootkit"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1547.006", "T1547"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod", "sudo") AND Processes.process = *modprobe* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_install_kernel_module_using_modprobe_utility_filter`

[ESCU - Linux Iptables Firewall Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious commandline that modify the iptables firewall setting of a linux machine. This technique was seen in cyclopsblink malware where it modifies the firewall setting of the compromised machine to allow traffic to its tcp port that will be used to communicate with its C2 server.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious commandline that modify the iptables firewall setting of a linux machine. This technique was seen in cyclopsblink malware where it modifies the firewall setting of the compromised machine to allow traffic to its tcp port that will be used to communicate with its C2 server.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = administrator may do this commandline for auditing and testing purposes. In this scenario filter is needed.
action.escu.creation_date = 2023-04-12
action.escu.modification_date = 2023-04-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Iptables Firewall Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Sandworm Tools", "CyclopsBLink"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may modify iptables firewall on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Iptables Firewall Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sandworm Tools", "CyclopsBLink"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*iptables *" AND Processes.process = "* --dport *" AND Processes.process = "* ACCEPT*" AND Processes.process = "*&amp;&gt;/dev/null*" AND Processes.process = "* tcp *" AND NOT(Processes.parent_process_path IN("/bin/*", "/lib/*", "/usr/bin/*", "/sbin/*")) by Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest _time span=10s Processes.user Processes.parent_process_name  Processes.parent_process_path Processes.process_path | rex field=Processes.process "--dport (?<port>3269|636|989|994|995|8443)" | stats values(Processes.process) as processes_exec values(port) as ports values(Processes.process_guid) as guids values(Processes.process_id) as pids dc(port) as port_count count by Processes.process_name Processes.parent_process_name Processes.parent_process_id Processes.dest Processes.user Processes.parent_process_path Processes.process_path | where port_count >=3 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_iptables_firewall_modification_filter`

[ESCU - Linux Java Spawning Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the process name of Java, Apache, or Tomcat spawning a Linux shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "sh", "ksh", "zsh", "bash", "dash", "rbash", "fish", "csh', "tcsh', "ion", "eshell". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the process name of Java, Apache, or Tomcat spawning a Linux shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "sh", "ksh", "zsh", "bash", "dash", "rbash", "fish", "csh', "tcsh', "ion", "eshell". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on asset type.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Java Spawning Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Spring4Shell CVE-2022-22965", "Hermetic Wiper", "Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Linux shell, potentially indicative of exploitation.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Java Spawning Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Spring4Shell CVE-2022-22965", "Hermetic Wiper", "Log4Shell CVE-2021-44228"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-44228"], "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the process name of Java, Apache, or Tomcat spawning a Linux shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "sh", "ksh", "zsh", "bash", "dash", "rbash", "fish", "csh', "tcsh', "ion", "eshell". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
action.notable.param.rule_title = Linux Java Spawning Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java OR Processes.parent_process_name=apache OR Processes.parent_process_name=tomcat `linux_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_java_spawning_shell_filter`

[ESCU - Linux Kernel Module Enumeration - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the process kmod being utilized to list kernel modules in use. Typically, this is not seen as malicious, however it may be a precurser to the use of insmod to install a module.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1082", "T1014"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the process kmod being utilized to list kernel modules in use. Typically, this is not seen as malicious, however it may be a precurser to the use of insmod to install a module.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-07-27
action.escu.modification_date = 2022-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Kernel Module Enumeration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Rootkit"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ enumeration kernel modules.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 15}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Kernel Module Enumeration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Rootkit"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1082", "T1014"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=kmod Processes.process IN ("*lsmod*", "*list*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_kernel_module_enumeration_filter`

[ESCU - Linux Kworker Process In Writable Process Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious process kworker commandline in a linux machine. kworker process name or thread are common names of kernel threads in linux process. This hunting detections can lead to investigate process contains process path in writable directory in linux like /home/, /var/log and /tmp/. This technique was seen in cyclopsblink malware to blend its core and other of its child process as normal kworker on the compromised machine. This detection might be a good pivot to look for other IOC related to cyclopsblink malware or attacks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036.004", "T1036"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious process kworker commandline in a linux machine. kworker process name or thread are common names of kernel threads in linux process. This hunting detections can lead to investigate process contains process path in writable directory in linux like /home/, /var/log and /tmp/. This technique was seen in cyclopsblink malware to blend its core and other of its child process as normal kworker on the compromised machine. This detection might be a good pivot to look for other IOC related to cyclopsblink malware or attacks.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-12
action.escu.modification_date = 2023-04-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Kworker Process In Writable Process Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Sandworm Tools", "CyclopsBLink"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Kworker Process In Writable Process Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sandworm Tools", "CyclopsBLink"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1036.004", "T1036"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.parent_process = "*[kworker/*" Processes.parent_process_path IN ("/home/*", "/tmp/*", "/var/log/*") Processes.process="*iptables*" by  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_path Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_kworker_process_in_writable_process_path_filter`

[ESCU - Linux Make Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The Linux make command is used to build and maintain groups of programs and files from the source code. In Linux, it is one of the most frequently used commands by the developers. It assists developers to install and compile many utilities from the terminal. If sudo right is given to make utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The Linux make command is used to build and maintain groups of programs and files from the source code. In Linux, it is one of the most frequently used commands by the developers. It assists developers to install and compile many utilities from the terminal. If sudo right is given to make utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Make Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 20}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Make Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*make*-s*" AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_make_privilege_escalation_filter`

[ESCU - Linux MySQL Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = MySQL is an open-source relational database management system. Its name is a combination of "My", the name of co-founder Michael Widenius's daughter My, and "SQL", the abbreviation for Structured Query Language. If sudo right is given to mysql utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = MySQL is an open-source relational database management system. Its name is a combination of "My", the name of co-founder Michael Widenius's daughter My, and "SQL", the abbreviation for Structured Query Language. If sudo right is given to mysql utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux MySQL Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux MySQL Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*mysql*-e*" AND Processes.process="*\!**" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_mysql_privilege_escalation_filter`

[ESCU - Linux Ngrok Reverse Proxy Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Ngrok being utilized on the Linux operating system. Unfortunately, there is no original file name for Ngrok, so it may be worth an additional hunt to identify any command-line arguments. The sign of someone using Ngrok is not malicious, however, more recently it has become an adversary tool.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1572", "T1090", "T1102"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Ngrok being utilized on the Linux operating system. Unfortunately, there is no original file name for Ngrok, so it may be worth an additional hunt to identify any command-line arguments. The sign of someone using Ngrok is not malicious, however, more recently it has become an adversary tool.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present if Ngrok is an authorized utility. Filter as needed.
action.escu.creation_date = 2023-01-12
action.escu.modification_date = 2023-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Ngrok Reverse Proxy Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Reverse Network Proxy"]
action.risk = 1
action.risk.param._risk_message = A reverse proxy was identified spawning from $parent_process_name$ -  $process_name$ on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 50}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 50}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 50}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Ngrok Reverse Proxy Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Reverse Network Proxy"], "cis20": ["CIS 10"], "confidence": 100, "impact": 50, "mitre_attack": ["T1572", "T1090", "T1102"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", "*tcp*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ngrok_reverse_proxy_usage_filter`

[ESCU - Linux Node Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Node.js is a back-end JavaScript runtime environment that is open-source, cross-platform, runs on the V8 engine, and executes JavaScript code outside of a web browser. It was created to help create scalable network applications. If the binary is allowed to run as superuser by sudo, it does not drop the elevated privileges and may be used to access the file system, escalate or maintain privileged access.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Node.js is a back-end JavaScript runtime environment that is open-source, cross-platform, runs on the V8 engine, and executes JavaScript code outside of a web browser. It was created to help create scalable network applications. If the binary is allowed to run as superuser by sudo, it does not drop the elevated privileges and may be used to access the file system, escalate or maintain privileged access.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-07-31
action.escu.modification_date = 2022-07-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Node Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Node Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*node*" AND Processes.process="*-e*" AND Processes.process="*child_process.spawn*" AND Processes.process="*stdio*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_node_privilege_escalation_filter`

[ESCU - Linux NOPASSWD Entry In Sudoers File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for suspicious command lines that may add entry to /etc/sudoers with NOPASSWD attribute in linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to gain elevated privilege to the targeted or compromised host. /etc/sudoers file controls who can run what commands users can execute on the machines and can also control whether user need a password to execute particular commands. This file is composed of aliases (basically variables) and user specifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for suspicious command lines that may add entry to /etc/sudoers with NOPASSWD attribute in linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to gain elevated privilege to the targeted or compromised host. /etc/sudoers file controls who can run what commands users can execute on the machines and can also control whether user need a password to execute particular commands. This file is composed of aliases (basically variables) and user specifications.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux NOPASSWD Entry In Sudoers File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = a commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux NOPASSWD Entry In Sudoers File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*NOPASSWD:*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_nopasswd_entry_in_sudoers_file_filter`

[ESCU - Linux Obfuscated Files or Information Base64 Decode - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of base64 decode on Linux being utilized to deobfuscate a file. Identify the source of the file and determine if legitimate. Review parallel processes for further behavior before and after.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of base64 decode on Linux being utilized to deobfuscate a file. Identify the source of the file and determine if legitimate. Review parallel processes for further behavior before and after.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and will require some tuning based on processes. Filter as needed.
action.escu.creation_date = 2022-07-27
action.escu.modification_date = 2022-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Obfuscated Files or Information Base64 Decode - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 15}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Obfuscated Files or Information Base64 Decode - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1027"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*base64 -d*","*base64 --decode*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_obfuscated_files_or_information_base64_decode_filter`

[ESCU - Linux Octave Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = GNU Octave is a high-level programming language primarily intended for scientific computing and numerical computation. Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB. If sudo right is given to the application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = GNU Octave is a high-level programming language primarily intended for scientific computing and numerical computation. Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB. If sudo right is given to the application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Octave Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 20}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Octave Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*octave-cli*" AND Processes.process="*--eval*" AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_octave_privilege_escalation_filter`

[ESCU - Linux OpenVPN Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = OpenVPN is a virtual private network system that implements techniques to create secure point-to-point or site-to-site connections in routed or bridged configurations and remote access facilities. It implements both client and server applications. If sudo right is given to the OpenVPN application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = OpenVPN is a virtual private network system that implements techniques to create secure point-to-point or site-to-site connections in routed or bridged configurations and remote access facilities. It implements both client and server applications. If sudo right is given to the OpenVPN application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux OpenVPN Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux OpenVPN Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*openvpn*" AND Processes.process="*--dev*" AND Processes.process="*--script-security*" AND Processes.process="*--up*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_openvpn_privilege_escalation_filter`

[ESCU - Linux Persistence and Privilege Escalation Risk Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following correlation is specific to Linux persistence and privilege escalation tactics and is tied to two analytic stories and any Linux analytic tied to persistence and privilege escalation. These techniques often overlap with Persistence techniques, as OS features that let an adversary persist can execute in an elevated context.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following correlation is specific to Linux persistence and privilege escalation tactics and is tied to two analytic stories and any Linux analytic tied to persistence and privilege escalation. These techniques often overlap with Persistence techniques, as OS features that let an adversary persist can execute in an elevated context.
action.escu.how_to_implement = Ensure Linux anomaly and TTP analytics are enabled. TTP may be set to Notables for point detections, anomaly should not be notables but risk generators. The correlation relies on more than x amount of distict detection names generated before generating a notable. Modify the value as needed. Default value is set to 4. This value may need to be increased based on activity in your environment.
action.escu.known_false_positives = False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
action.escu.creation_date = 2022-08-30
action.escu.modification_date = 2022-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Persistence and Privilege Escalation Risk Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Linux Persistence and Privilege Escalation Risk Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1548"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following correlation is specific to Linux persistence and privilege escalation tactics and is tied to two analytic stories and any Linux analytic tied to persistence and privilege escalation. These techniques often overlap with Persistence techniques, as OS features that let an adversary persist can execute in an elevated context.
action.notable.param.rule_title = RBA: Linux Persistence and Privilege Escalation Risk Behavior
action.notable.param.security_domain = audit
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where (All_Risk.analyticstories IN ("Linux Privilege Escalation", "Linux Persistence Techniques") OR source = "*Linux*") All_Risk.annotations.mitre_attack.mitre_tactic IN ("persistence", "privilege-escalation") All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `linux_persistence_and_privilege_escalation_risk_behavior_filter`

[ESCU - Linux PHP Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = PHP is a general-purpose scripting language geared toward web development. It was originally created by Danish-Canadian programmer Rasmus Lerdorf in 1994. The PHP reference implementation is now produced by The PHP Group. If sudo right is given to php application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = PHP is a general-purpose scripting language geared toward web development. It was originally created by Danish-Canadian programmer Rasmus Lerdorf in 1994. The PHP reference implementation is now produced by The PHP Group. If sudo right is given to php application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux PHP Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux PHP Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*php*-r*" AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_php_privilege_escalation_filter`

[ESCU - Linux pkexec Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `pkexec` spawning with no command-line arguments. A vulnerability in Polkit's pkexec component identified as CVE-2021-4034 (PwnKit) which is present in the default configuration of all major Linux distributions and can be exploited to gain full root privileges on the system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `pkexec` spawning with no command-line arguments. A vulnerability in Polkit's pkexec component identified as CVE-2021-4034 (PwnKit) which is present in the default configuration of all major Linux distributions and can be exploited to gain full root privileges on the system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-01-28
action.escu.modification_date = 2022-01-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux pkexec Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ related to a local privilege escalation in polkit pkexec.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux pkexec Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-4034"], "impact": 80, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `pkexec` spawning with no command-line arguments. A vulnerability in Polkit's pkexec component identified as CVE-2021-4034 (PwnKit) which is present in the default configuration of all major Linux distributions and can be exploited to gain full root privileges on the system.
action.notable.param.rule_title = Linux pkexec Privilege Escalation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=pkexec by _time Processes.dest Processes.user Processes.process_id Processes.parent_process_name Processes.process_name Processes.process Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(^.{1}$)" | `linux_pkexec_privilege_escalation_filter`

[ESCU - Linux Possible Access Or Modification Of sshd Config File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for suspicious process command-line that might be accessing or modifying sshd_config. This file is the ssh configuration file that might be modify by threat actors or adversaries to redirect port connection, allow user using authorized key generated during attack. This anomaly detection might catch noise from administrator auditing or modifying ssh configuration file. In this scenario filter is needed
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098.004", "T1098"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for suspicious process command-line that might be accessing or modifying sshd_config. This file is the ssh configuration file that might be modify by threat actors or adversaries to redirect port connection, allow user using authorized key generated during attack. This anomaly detection might catch noise from administrator auditing or modifying ssh configuration file. In this scenario filter is needed
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-11
action.escu.modification_date = 2022-01-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Access Or Modification Of sshd Config File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = a commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Access Or Modification Of sshd Config File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1098.004", "T1098"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/ssh/sshd_config") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_or_modification_of_sshd_config_file_filter`

[ESCU - Linux Possible Access To Credential Files - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a possible attempt to dump or access the content of /etc/passwd and /etc/shadow to enable offline credential cracking. "etc/passwd" store user information within linux OS while "etc/shadow" contain the user passwords hash. Adversaries and threat actors may attempt to access this to gain persistence and/or privilege escalation. This anomaly detection can be a good indicator of possible credential dumping technique but it might catch some normal administrator automation scripts or during credential auditing. In this scenario filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.008", "T1003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a possible attempt to dump or access the content of /etc/passwd and /etc/shadow to enable offline credential cracking. "etc/passwd" store user information within linux OS while "etc/shadow" contain the user passwords hash. Adversaries and threat actors may attempt to access this to gain persistence and/or privilege escalation. This anomaly detection can be a good indicator of possible credential dumping technique but it might catch some normal administrator automation scripts or during credential auditing. In this scenario filter is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-10
action.escu.modification_date = 2022-01-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Access To Credential Files - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Access To Credential Files - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1003.008", "T1003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/shadow*", "*/etc/passwd*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_to_credential_files_filter`

[ESCU - Linux Possible Access To Sudoers File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a possible access or modification of /etc/sudoers file. "/etc/sudoers" file controls who can run what command as what users on what machine and can also control whether a specific user need a password for particular commands.  adversaries and threat actors abuse this file to gain persistence and/or privilege escalation during attack on targeted host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a possible access or modification of /etc/sudoers file. "/etc/sudoers" file controls who can run what command as what users on what machine and can also control whether a specific user need a password for particular commands.  adversaries and threat actors abuse this file to gain persistence and/or privilege escalation during attack on targeted host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-10
action.escu.modification_date = 2022-01-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Access To Sudoers File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Access To Sudoers File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/sudoers*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_to_sudoers_file_filter`

[ESCU - Linux Possible Append Command To At Allow Config File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to identify suspicious command lines that may append user entries to either /etc/at.allow or /etc/at.deny. These files can be exploited by malicious actors for persistence on a compromised Linux host by altering permissions for scheduled tasks using the at command.\
In this context, an attacker can create a user or add an existing user to these configuration files to execute their malicious code through scheduled tasks. The detection of such anomalous behavior can serve as an effective indicator warranting further investigation to validate if the activity is indeed malicious or a false positive.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.002", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to identify suspicious command lines that may append user entries to either /etc/at.allow or /etc/at.deny. These files can be exploited by malicious actors for persistence on a compromised Linux host by altering permissions for scheduled tasks using the at command.\
In this context, an attacker can create a user or add an existing user to these configuration files to execute their malicious code through scheduled tasks. The detection of such anomalous behavior can serve as an effective indicator warranting further investigation to validate if the activity is indeed malicious or a false positive.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Append Command To At Allow Config File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may modify at allow config file in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Append Command To At Allow Config File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1053.002", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*/etc/at.allow", "*/etc/at.deny") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_command_to_at_allow_config_file_filter`

[ESCU - Linux Possible Append Command To Profile Config File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious command-lines that can be possibly used to modify user profile files to automatically execute scripts/executables by shell upon reboot of the machine. This technique is commonly abused by adversaries, malware and red teamers as persistence mechanism to the targeted or compromised host. This Anomaly detection is a good indicator that someone wants to run code after reboot which can be done also by the administrator or network operator for automation purposes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.004", "T1546"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious command-lines that can be possibly used to modify user profile files to automatically execute scripts/executables by shell upon reboot of the machine. This technique is commonly abused by adversaries, malware and red teamers as persistence mechanism to the targeted or compromised host. This Anomaly detection is a good indicator that someone wants to run code after reboot which can be done also by the administrator or network operator for automation purposes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Append Command To Profile Config File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = a commandline $process$ that may modify profile files in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Append Command To Profile Config File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1546.004", "T1546"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*~/.bashrc", "*~/.bash_profile", "*/etc/profile", "~/.bash_login", "*~/.profile", "~/.bash_logout") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_command_to_profile_config_file_filter`

[ESCU - Linux Possible Append Cronjob Entry on Existing Cronjob File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect potential tampering with cronjob files on a Linux system. It specifically searches for command lines that may be used to append code to existing cronjob files, a technique often employed by adversaries, malware, and red teamers for persistence or privilege escalation. Altering existing or sometimes normal cronjob script files allows malicious code to be executed automatically.\
The analytic operates by monitoring logs for specific process names, parent processes, and command-line executions from your endpoints. It specifically checks for any 'echo' command which modifies files in directories commonly associated with cron jobs such as '/etc/cron*', '/var/spool/cron/', and '/etc/anacrontab'. If such activity is detected, an alert is triggered.\
This behavior is worth identifying for a SOC because malicious cron jobs can lead to system compromises and unauthorized data access, impacting business operations and data integrity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to detect potential tampering with cronjob files on a Linux system. It specifically searches for command lines that may be used to append code to existing cronjob files, a technique often employed by adversaries, malware, and red teamers for persistence or privilege escalation. Altering existing or sometimes normal cronjob script files allows malicious code to be executed automatically.\
The analytic operates by monitoring logs for specific process names, parent processes, and command-line executions from your endpoints. It specifically checks for any 'echo' command which modifies files in directories commonly associated with cron jobs such as '/etc/cron*', '/var/spool/cron/', and '/etc/anacrontab'. If such activity is detected, an alert is triggered.\
This behavior is worth identifying for a SOC because malicious cron jobs can lead to system compromises and unauthorized data access, impacting business operations and data integrity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. Therefore, it's recommended to adjust filter macros to eliminate such false positives.
action.escu.creation_date = 2021-12-17
action.escu.modification_date = 2021-12-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Append Cronjob Entry on Existing Cronjob File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Append Cronjob Entry on Existing Cronjob File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_cronjob_entry_on_existing_cronjob_file_filter`

[ESCU - Linux Possible Cronjob Modification With Editor - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential unauthorized modifications to Linux cronjobs using text editors like "nano", "vi" or "vim". It identifies this behavior by tracking command-line executions that interact with paths related to cronjob configuration, a common Linux scheduling utility. Cronjob files may be manipulated by attackers for privilege escalation or persistent access, making such changes critical to monitor.\ The identified behavior is significant for a Security Operations Center (SOC) as it could indicate an ongoing attempt at establishing persistent access or privilege escalation, leading to data breaches, system compromise, or other malicious activities.\
In case of a true positive, the impact could be severe. An attacker with escalated privileges or persistent access could carry out damaging actions, such as data theft, sabotage, or further network penetration.\
To implement this analytic, ensure ingestion of logs tracking process name, parent process, and command-line executions from your endpoints. Utilize the Add-on for Linux Sysmon from Splunkbase if you're using Sysmon.\
Known false positives include legitimate administrative tasks, as these commands may also be used for benign purposes. Careful tuning and filtering based on known benign activity in your environment can minimize these instances.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects potential unauthorized modifications to Linux cronjobs using text editors like "nano", "vi" or "vim". It identifies this behavior by tracking command-line executions that interact with paths related to cronjob configuration, a common Linux scheduling utility. Cronjob files may be manipulated by attackers for privilege escalation or persistent access, making such changes critical to monitor.\ The identified behavior is significant for a Security Operations Center (SOC) as it could indicate an ongoing attempt at establishing persistent access or privilege escalation, leading to data breaches, system compromise, or other malicious activities.\
In case of a true positive, the impact could be severe. An attacker with escalated privileges or persistent access could carry out damaging actions, such as data theft, sabotage, or further network penetration.\
To implement this analytic, ensure ingestion of logs tracking process name, parent process, and command-line executions from your endpoints. Utilize the Add-on for Linux Sysmon from Splunkbase if you're using Sysmon.\
Known false positives include legitimate administrative tasks, as these commands may also be used for benign purposes. Careful tuning and filtering based on known benign activity in your environment can minimize these instances.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-17
action.escu.modification_date = 2021-12-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Cronjob Modification With Editor - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Cronjob Modification With Editor - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 30, "impact": 20, "mitre_attack": ["T1053.003", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN("nano","vim.basic") OR Processes.process IN ("*nano *", "*vi *", "*vim *")) AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_cronjob_modification_with_editor_filter`

[ESCU - Linux Possible Ssh Key File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for possible ssh key file creation on ~/.ssh/ folder. This technique is commonly abused by threat actors and adversaries to gain persistence and privilege escalation to the targeted host. by creating ssh private and public key and passing the public key to the attacker server. threat actor can access remotely the machine using openssh daemon service.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098.004", "T1098"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for possible ssh key file creation on ~/.ssh/ folder. This technique is commonly abused by threat actors and adversaries to gain persistence and privilege escalation to the targeted host. by creating ssh private and public key and passing the public key to the attacker server. threat actor can access remotely the machine using openssh daemon service.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = Administrator or network operator can create file in ~/.ssh folders for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-11
action.escu.modification_date = 2022-01-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Possible Ssh Key File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Possible Ssh Key File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1098.004", "T1098"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/.ssh*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_possible_ssh_key_file_creation_filter`

[ESCU - Linux Preload Hijack Library Calls - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious command that may hijack a library function in linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to gain privileges and persist on the machine. This detection pertains to loading a dll to hijack or hook a library function of specific program using LD_PRELOAD command.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.006", "T1574"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious command that may hijack a library function in linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to gain privileges and persist on the machine. This detection pertains to loading a dll to hijack or hook a library function of specific program using LD_PRELOAD command.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-22
action.escu.modification_date = 2021-12-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Preload Hijack Library Calls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may hijack library function on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Preload Hijack Library Calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1574.006", "T1574"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious command that may hijack a library function in linux platform. This technique is commonly abuse by adversaries, malware author and red teamers to gain privileges and persist on the machine. This detection pertains to loading a dll to hijack or hook a library function of specific program using LD_PRELOAD command.
action.notable.param.rule_title = Linux Preload Hijack Library Calls
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*LD_PRELOAD*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_preload_hijack_library_calls_filter`

[ESCU - Linux Proxy Socks Curl - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies curl being utilized with a proxy based on command-line arguments - -x, socks, --preproxy and --proxy. This behavior is built into the MetaSploit Framework as a auxiliary module. What does socks buy an adversary? SOCKS4a extends the SOCKS4 protocol to allow a client to specify a destination domain name rather than an IP address. The SOCKS5 protocol is defined in RFC 1928. It is an incompatible extension of the SOCKS4 protocol; it offers more choices for authentication and adds support for IPv6 and UDP, the latter of which can be used for DNS lookups. The protocols, and a proxy itself, allow an adversary to evade controls in place monitoring traffic, making it harder for the defender to identify and track activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1090", "T1095"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies curl being utilized with a proxy based on command-line arguments - -x, socks, --preproxy and --proxy. This behavior is built into the MetaSploit Framework as a auxiliary module. What does socks buy an adversary? SOCKS4a extends the SOCKS4 protocol to allow a client to specify a destination domain name rather than an IP address. The SOCKS5 protocol is defined in RFC 1928. It is an incompatible extension of the SOCKS4 protocol; it offers more choices for authentication and adds support for IPv6 and UDP, the latter of which can be used for DNS lookups. The protocols, and a proxy itself, allow an adversary to evade controls in place monitoring traffic, making it harder for the defender to identify and track activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present based on proxy usage internally. Filter as needed.
action.escu.creation_date = 2022-07-29
action.escu.modification_date = 2022-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Proxy Socks Curl - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land", "Ingress Tool Transfer"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ by user $user$ utilizing a proxy. Review activity for further details.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Proxy Socks Curl - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land", "Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1090", "T1095"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies curl being utilized with a proxy based on command-line arguments - -x, socks, --preproxy and --proxy. This behavior is built into the MetaSploit Framework as a auxiliary module. What does socks buy an adversary? SOCKS4a extends the SOCKS4 protocol to allow a client to specify a destination domain name rather than an IP address. The SOCKS5 protocol is defined in RFC 1928. It is an incompatible extension of the SOCKS4 protocol; it offers more choices for authentication and adds support for IPv6 and UDP, the latter of which can be used for DNS lookups. The protocols, and a proxy itself, allow an adversary to evade controls in place monitoring traffic, making it harder for the defender to identify and track activity.
action.notable.param.rule_title = Linux Proxy Socks Curl
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process IN ("*-x *", "*socks4a://*", "*socks5h://*", "*socks4://*","*socks5://*", "*--preproxy *", "--proxy*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_proxy_socks_curl_filter`

[ESCU - Linux Puppet Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = In computing, Puppet is a software configuration management tool which includes its own declarative language to describe system configuration. It is a model-driven solution that requires limited programming knowledge to use. If sudo right is given to the tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = In computing, Puppet is a software configuration management tool which includes its own declarative language to describe system configuration. It is a model-driven solution that requires limited programming knowledge to use. If sudo right is given to the tool for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Puppet Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 5}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Puppet Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*puppet*" AND Processes.process="*apply*" AND Processes.process="*-e*" AND Processes.process="*exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_puppet_privilege_escalation_filter`

[ESCU - Linux RPM Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = RPM Package Manager is a free and open-source package management system. The name RPM refers to the .rpm file format and the package manager program itself. RPM was intended primarily for Linux distributions; the file format is the baseline package format of the Linux Standard Base. If sudo right is given to rpm utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = RPM Package Manager is a free and open-source package management system. The name RPM refers to the .rpm file format and the package manager program itself. RPM was intended primarily for Linux distributions; the file format is the baseline package format of the Linux Standard Base. If sudo right is given to rpm utility for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux RPM Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux RPM Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*rpm*--eval*" AND Processes.process="*lua:os.execute*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_rpm_privilege_escalation_filter`

[ESCU - Linux Ruby Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Ruby is one of the most used and easy to use programming languages. Ruby is an open-source, object-oriented interpreter that can be installed on a Linux system. If sudo right is given to ruby application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Ruby is one of the most used and easy to use programming languages. Ruby is an open-source, object-oriented interpreter that can be installed on a Linux system. If sudo right is given to ruby application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are present based on automated tooling or system administrative usage. Filter as needed.
action.escu.creation_date = 2022-08-09
action.escu.modification_date = 2022-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Ruby Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Ruby Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*ruby*-e*" AND Processes.process="*exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ruby_privilege_escalation_filter`

[ESCU - Linux Service File Created In Systemd Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect suspicious file creation within the systemd timer directory on Linux platforms. Systemd is a system and service manager for Linux, similar to the combination of wininit.exe and services.exe on Windows. This process initializes a Linux system and starts defined services in unit files. Malicious actors, such as adversaries, malware, or red teamers, can exploit this feature by embedding a systemd service file for persistence on the targeted or compromised host.\
The analytic works by monitoring logs with file name, file path, and process GUID data from your endpoints. If a .service file is created in certain systemd directories, the analytic triggers an alert. This behavior is significant for a Security Operations Center (SOC) as it may indicate a persistent threat within the network, with a potential impact of system compromise or data exfiltration.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.006", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to detect suspicious file creation within the systemd timer directory on Linux platforms. Systemd is a system and service manager for Linux, similar to the combination of wininit.exe and services.exe on Windows. This process initializes a Linux system and starts defined services in unit files. Malicious actors, such as adversaries, malware, or red teamers, can exploit this feature by embedding a systemd service file for persistence on the targeted or compromised host.\
The analytic works by monitoring logs with file name, file path, and process GUID data from your endpoints. If a .service file is created in certain systemd directories, the analytic triggers an alert. This behavior is significant for a Security Operations Center (SOC) as it may indicate a persistent threat within the network, with a potential impact of system compromise or data exfiltration.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = False positives may arise when administrators or network operators create files in systemd directories for legitimate automation tasks. Therefore, it's important to adjust filter macros to account for valid activities. To implement this search successfully, it's crucial to ingest appropriate logs, preferably using the Linux Sysmon Add-on from Splunkbase for those using Sysmon.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Service File Created In Systemd Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A service file named as $file_path$ is created in systemd folder on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Service File Created In Systemd Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1053.006", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name = *.service Filesystem.file_path IN ("*/etc/systemd/system*", "*/lib/systemd/system*", "*/usr/lib/systemd/system*", "*/run/systemd/system*", "*~/.config/systemd/*", "*~/.local/share/systemd/*","*/etc/systemd/user*", "*/lib/systemd/user*", "*/usr/lib/systemd/user*", "*/run/systemd/user*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_service_file_created_in_systemd_directory_filter`

[ESCU - Linux Service Restarted - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the restarting or re-enabling of services in the Linux platform. It focuses on the use of the systemctl or service tools for executing these actions. Adversaries may leverage this technique to repeatedly execute malicious payloads as a form of persistence. Linux hosts typically start services during boot to perform background system functions. However, administrators may also create legitimate services for specific tools or applications as part of task automation. In such cases, it is recommended to verify the service path of the registered script or executable and identify the creator of the service for further validation.\
It's important to be aware that this analytic may generate false positives as administrators or network operators may use the same command-line for legitimate automation purposes. Filter macros should be updated accordingly to minimize false positives.\
Identifying restarted or re-enabled services is valuable for a SOC as it can indicate potential malicious activities attempting to maintain persistence or execute unauthorized actions on Linux systems. By detecting and investigating these events, security analysts can respond promptly to mitigate risks and prevent further compromise. The impact of a true positive can range from unauthorized access to data destruction or other damaging outcomes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.006", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the restarting or re-enabling of services in the Linux platform. It focuses on the use of the systemctl or service tools for executing these actions. Adversaries may leverage this technique to repeatedly execute malicious payloads as a form of persistence. Linux hosts typically start services during boot to perform background system functions. However, administrators may also create legitimate services for specific tools or applications as part of task automation. In such cases, it is recommended to verify the service path of the registered script or executable and identify the creator of the service for further validation.\
It's important to be aware that this analytic may generate false positives as administrators or network operators may use the same command-line for legitimate automation purposes. Filter macros should be updated accordingly to minimize false positives.\
Identifying restarted or re-enabled services is valuable for a SOC as it can indicate potential malicious activities attempting to maintain persistence or execute unauthorized actions on Linux systems. By detecting and investigating these events, security analysts can respond promptly to mitigate risks and prevent further compromise. The impact of a true positive can range from unauthorized access to data destruction or other damaging outcomes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Service Restarted - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Linux Privilege Escalation", "Linux Living Off The Land", "Data Destruction", "Linux Persistence Techniques", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may create or start a service on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Service Restarted - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Linux Privilege Escalation", "Linux Living Off The Land", "Data Destruction", "Linux Persistence Techniques", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.006", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process IN ("*restart*", "*reload*", "*reenable*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_service_restarted_filter`

[ESCU - Linux Service Started Or Enabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation or enabling of services in Linux platforms, specifically using the systemctl or service tool application. This behavior is worth identifying as adversaries may create or modify services to execute malicious payloads as part of persistence. Legitimate services created by administrators for automation purposes may also trigger this analytic, so it is important to update the filter macros to remove false positives. If a true positive is found, it suggests an possible attacker is attempting to persist within the environment or deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. To implement this analytic, ensure you are ingesting logs with the process name, parent process, and command-line executions from your endpoints.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.006", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation or enabling of services in Linux platforms, specifically using the systemctl or service tool application. This behavior is worth identifying as adversaries may create or modify services to execute malicious payloads as part of persistence. Legitimate services created by administrators for automation purposes may also trigger this analytic, so it is important to update the filter macros to remove false positives. If a true positive is found, it suggests an possible attacker is attempting to persist within the environment or deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. To implement this analytic, ensure you are ingesting logs with the process name, parent process, and command-line executions from your endpoints.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Service Started Or Enabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = a commandline $process$ that may create or start a service on $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Service Started Or Enabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1053.006", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process IN ("* start *", "* enable *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_service_started_or_enabled_filter`

[ESCU - Linux Setuid Using Chmod Utility - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious chmod utility execution to enable SUID bit. This allows a user to temporarily gain root access, usually in order to run a program. For example, only the root account is allowed to change the password information contained in the password database; If the SUID bit appears as an s, the file's owner also has execute permission to the file; if it appears as an S, the file's owner does not have execute permission. The second specialty permission is the SGID, or set group id bit. It is similar to the SUID bit, except it can temporarily change group membership, usually to execute a program. The SGID bit is set if an s or an S appears in the group section of permissions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.001", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious chmod utility execution to enable SUID bit. This allows a user to temporarily gain root access, usually in order to run a program. For example, only the root account is allowed to change the password information contained in the password database; If the SUID bit appears as an s, the file's owner also has execute permission to the file; if it appears as an S, the file's owner does not have execute permission. The second specialty permission is the SGID, or set group id bit. It is similar to the SUID bit, except it can temporarily change group membership, usually to execute a program. The SGID bit is set if an s or an S appears in the group section of permissions.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Setuid Using Chmod Utility - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = a commandline $process$ that may set suid or sgid on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Setuid Using Chmod Utility - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1548.001", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes WHERE (Processes.process_name = chmod OR Processes.process = "*chmod *") AND Processes.process IN("* g+s *", "* u+s *", "* 4777 *", "* 4577 *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_setuid_using_chmod_utility_filter`

[ESCU - Linux Setuid Using Setcap Utility - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious setcap utility execution to enable SUID bit. This allows a user to temporarily gain root access, usually in order to run a program. For example, only the root account is allowed to change the password information contained in the password database; If the SUID bit appears as an s, the file's owner also has execute permission to the file; if it appears as an S, the file's owner does not have execute permission. The second specialty permission is the SGID, or set group id bit. It is similar to the SUID bit, except it can temporarily change group membership, usually to execute a program. The SGID bit is set if an s or an S appears in the group section of permissions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.001", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious setcap utility execution to enable SUID bit. This allows a user to temporarily gain root access, usually in order to run a program. For example, only the root account is allowed to change the password information contained in the password database; If the SUID bit appears as an s, the file's owner also has execute permission to the file; if it appears as an S, the file's owner does not have execute permission. The second specialty permission is the SGID, or set group id bit. It is similar to the SUID bit, except it can temporarily change group membership, usually to execute a program. The SGID bit is set if an s or an S appears in the group section of permissions.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Setuid Using Setcap Utility - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ that may set suid or sgid on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Setuid Using Setcap Utility - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1548.001", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = setcap OR Processes.process = "*setcap *") AND Processes.process IN ("* cap_setuid=ep *", "* cap_setuid+ep *", "* cap_net_bind_service+p *", "* cap_net_raw+ep *", "* cap_dac_read_search+ep *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_setuid_using_setcap_utility_filter`

[ESCU - Linux Shred Overwrite Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a shred process to overwrite a files in a linux machine. Shred Linux application is designed to overwrite file to hide its contents or make the deleted file un-recoverable. Weve seen this technique in industroyer2 malware that tries to wipe energy facilities of targeted sector as part of its destructive attack. It might be some normal user may use this command for valid purposes but it is recommended to check what files, disk or folder it tries to shred that might be good pivot for incident response in this type of destructive malware.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a shred process to overwrite a files in a linux machine. Shred Linux application is designed to overwrite file to hide its contents or make the deleted file un-recoverable. Weve seen this technique in industroyer2 malware that tries to wipe energy facilities of targeted sector as part of its destructive attack. It might be some normal user may use this command for valid purposes but it is recommended to check what files, disk or folder it tries to shred that might be good pivot for incident response in this type of destructive malware.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Shred Overwrite Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Industroyer2", "AwfulShred", "Linux Privilege Escalation", "Data Destruction", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A possible shred overwrite command $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Shred Overwrite Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Industroyer2", "AwfulShred", "Linux Privilege Escalation", "Data Destruction", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a shred process to overwrite a files in a linux machine. Shred Linux application is designed to overwrite file to hide its contents or make the deleted file un-recoverable. Weve seen this technique in industroyer2 malware that tries to wipe energy facilities of targeted sector as part of its destructive attack. It might be some normal user may use this command for valid purposes but it is recommended to check what files, disk or folder it tries to shred that might be good pivot for incident response in this type of destructive malware.
action.notable.param.rule_title = Linux Shred Overwrite Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name =shred AND Processes.process IN ("*-n*", "*-u*", "*-z*", "*-s*") by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_shred_overwrite_command_filter`

[ESCU - Linux Sqlite3 Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = sqlite3 is a terminal-based front-end to the SQLite library that can evaluate queries interactively and display the results in multiple formats. sqlite3 can also be used within shell scripts and other applications to provide batch processing features. If sudo right is given to this application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = sqlite3 is a terminal-based front-end to the SQLite library that can evaluate queries interactively and display the results in multiple formats. sqlite3 can also be used within shell scripts and other applications to provide batch processing features. If sudo right is given to this application for the user, then the user can run system commands as root and possibly get a root shell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2022-08-11
action.escu.modification_date = 2022-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Sqlite3 Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Sqlite3 Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sqlite3*" AND Processes.process="*.shell*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sqlite3_privilege_escalation_filter`

[ESCU - Linux SSH Authorized Keys Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies based on process execution the modification of SSH Authorized Keys. Adversaries perform this behavior to persist on endpoints. During triage, review parallel processes and capture any additional file modifications for review.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098.004"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies based on process execution the modification of SSH Authorized Keys. Adversaries perform this behavior to persist on endpoints. During triage, review parallel processes and capture any additional file modifications for review.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Filtering will be required as system administrators will add and remove. One way to filter query is to add "echo".
action.escu.creation_date = 2022-07-27
action.escu.modification_date = 2022-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux SSH Authorized Keys Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ modifying SSH Authorized Keys.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 15}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux SSH Authorized Keys Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1098.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process_name IN ("bash","cat") Processes.process IN ("*/authorized_keys*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ssh_authorized_keys_modification_filter`

[ESCU - Linux SSH Remote Services Script Execute - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies SSH being utilized to move laterally and execute a script or file on the remote host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies SSH being utilized to move laterally and execute a script or file on the remote host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This is not a common command to be executed. Filter as needed.
action.escu.creation_date = 2023-03-03
action.escu.modification_date = 2023-03-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux SSH Remote Services Script Execute - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ by user $user$ attempting to move laterally and download a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux SSH Remote Services Script Execute - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1021.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies SSH being utilized to move laterally and execute a script or file on the remote host.
action.notable.param.rule_title = Linux SSH Remote Services Script Execute
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process_name=ssh Processes.process IN ("*oStrictHostKeyChecking*", "*oConnectTimeout*", "*oBatchMode*") AND Processes.process IN ("*http:*","*https:*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ssh_remote_services_script_execute_filter`

[ESCU - Linux Stdout Redirection To Dev Null File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for suspicious commandline that redirect the stdout or possible stderror to dev/null file. This technique was seen in cyclopsblink malware where it redirect the possible output or error while modify the iptables firewall setting of the compromised machine to hide its action from the user. This Anomaly detection is a good pivot to look further why process or user use this un common approach.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for suspicious commandline that redirect the stdout or possible stderror to dev/null file. This technique was seen in cyclopsblink malware where it redirect the possible output or error while modify the iptables firewall setting of the compromised machine to hide its action from the user. This Anomaly detection is a good pivot to look further why process or user use this un common approach.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Stdout Redirection To Dev Null File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["CyclopsBLink", "Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = a commandline $process$ that redirect stdout to dev/null in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Stdout Redirection To Dev Null File - Rule
action.correlationsearch.annotations = {"analytic_story": ["CyclopsBLink", "Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*&amp;&gt;/dev/null*" by  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_stdout_redirection_to_dev_null_file_filter`

[ESCU - Linux Stop Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to detect events that attempt to stop or clear a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to detect events that attempt to stop or clear a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Stop Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified attempting to stop services on endpoint $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Stop Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is to detect events that attempt to stop or clear a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique like industroyer2 malware to terminate security services or other related services to continue there objective as a destructive payload.
action.notable.param.rule_title = Linux Stop Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", "service", "svcadm")  Processes.process ="*stop*" by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_stop_services_filter`

[ESCU - Linux Sudo OR Su Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect the execution of sudo or su command in linux operating system. The "sudo" command allows a system administrator to delegate authority to give certain users (or groups of users) the ability to run some (or all) commands as root or another user while providing an audit trail of the commands and their arguments. This command is commonly abused by adversaries, malware author and red teamers to elevate privileges to the targeted host. This command can be executed by administrator for legitimate purposes or to execute process that need admin privileges, In this scenario filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect the execution of sudo or su command in linux operating system. The "sudo" command allows a system administrator to delegate authority to give certain users (or groups of users) the ability to run some (or all) commands as root or another user while providing an audit trail of the commands and their arguments. This command is commonly abused by adversaries, malware author and red teamers to elevate privileges to the targeted host. This command can be executed by administrator for legitimate purposes or to execute process that need admin privileges, In this scenario filter is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-01-04
action.escu.modification_date = 2022-01-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Sudo OR Su Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Sudo OR Su Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("sudo", "su") OR Processes.parent_process_name IN ("sudo", "su") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sudo_or_su_execution_filter`

[ESCU - Linux Sudoers Tmp File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to looks for file creation of sudoers.tmp file cause by editing /etc/sudoers using visudo or editor in linux platform. This technique may abuse by adversaries, malware author and red teamers to gain elevated privilege to targeted or compromised host. /etc/sudoers file controls who can run what commands as what users on what machines and can also control special things such as whether you need a password for particular commands. The file is composed of aliases (basically variables) and user specifications (which control who can run what).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to looks for file creation of sudoers.tmp file cause by editing /etc/sudoers using visudo or editor in linux platform. This technique may abuse by adversaries, malware author and red teamers to gain elevated privilege to targeted or compromised host. /etc/sudoers file controls who can run what commands as what users on what machines and can also control special things such as whether you need a password for particular commands. The file is composed of aliases (basically variables) and user specifications (which control who can run what).
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
action.escu.known_false_positives = administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-23
action.escu.modification_date = 2021-12-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Sudoers Tmp File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A file $file_name$ is created in $file_path$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Sudoers Tmp File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*sudoers.tmp*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_sudoers_tmp_file_creation_filter`

[ESCU - Linux System Network Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for possible enumeration of local network configuration. This technique is commonly used as part of recon of adversaries or threat actor to know some network information for its next or further attack. This anomaly detections may capture normal event made by administrator during auditing or testing network connection of specific host or network to network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1016"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for possible enumeration of local network configuration. This technique is commonly used as part of recon of adversaries or threat actor to know some network information for its next or further attack. This anomaly detections may capture normal event made by administrator during auditing or testing network connection of specific host or network to network.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux System Network Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Network Discovery", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux System Network Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Network Discovery", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1016"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name_list values(Processes.process) as process_list values(Processes.process_id) as process_id_list values(Processes.parent_process_id) as parent_process_id_list values(Processes.process_guid) as process_guid_list dc(Processes.process_name) as process_name_count from datamodel=Endpoint.Processes where Processes.process_name IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", "route") by _time span=30m Processes.dest Processes.user | where process_name_count >=4 | `drop_dm_object_name(Processes)`| `linux_system_network_discovery_filter`

[ESCU - Linux System Reboot Via System Request Key - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for possible execution of SysReq hack to reboot the Linux system host. This technique was seen in Awfulshred malware wiper to reboot the compromised host by using the linux magic sysreq key. This kernel configuration can trigger reboot by piping out 'b' to /proc/sysrq-trigger after enabling all the functions of sysrq. This TTP detection can be a good indicator of possible suspicious processes running on the Linux host since this command is not a common way to reboot a system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1529"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for possible execution of SysReq hack to reboot the Linux system host. This technique was seen in Awfulshred malware wiper to reboot the compromised host by using the linux magic sysreq key. This kernel configuration can trigger reboot by piping out 'b' to /proc/sysrq-trigger after enabling all the functions of sysrq. This TTP detection can be a good indicator of possible suspicious processes running on the Linux host since this command is not a common way to reboot a system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux System Reboot Via System Request Key - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ execute sysrq command $process$ to reboot $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux System Reboot Via System Request Key - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1529"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to look for possible execution of SysReq hack to reboot the Linux system host. This technique was seen in Awfulshred malware wiper to reboot the compromised host by using the linux magic sysreq key. This kernel configuration can trigger reboot by piping out 'b' to /proc/sysrq-trigger after enabling all the functions of sysrq. This TTP detection can be a good indicator of possible suspicious processes running on the Linux host since this command is not a common way to reboot a system.
action.notable.param.rule_title = Linux System Reboot Via System Request Key
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", "sudo", "bash")  Processes.process =  "* echo b &gt; *" Processes.process = "*/proc/sysrq-trigger" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_system_reboot_via_system_request_key_filter`

[ESCU - Linux Unix Shell Enable All SysRq Functions - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for possible execution of SysReq hack to enable all functions of kernel system requests of the Linux system host. This technique was seen in AwfulShred malware wiper to reboot the compromised host by using the linux magic sysreq key. This kernel configuration can be triggered by piping out bitmask '1' to /proc/sys/kernel/sysrq. This TTP detection can be a good indicator of possible suspicious processes running on the Linux host since this command is not so common shell commandline.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.004", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for possible execution of SysReq hack to enable all functions of kernel system requests of the Linux system host. This technique was seen in AwfulShred malware wiper to reboot the compromised host by using the linux magic sysreq key. This kernel configuration can be triggered by piping out bitmask '1' to /proc/sys/kernel/sysrq. This TTP detection can be a good indicator of possible suspicious processes running on the Linux host since this command is not so common shell commandline.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Unix Shell Enable All SysRq Functions - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AwfulShred", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ execute sysrq command $process$ to enable all function of system request in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Unix Shell Enable All SysRq Functions - Rule
action.correlationsearch.annotations = {"analytic_story": ["AwfulShred", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1059.004", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", "sudo", "bash")  Processes.process =  "* echo 1 &gt; *" Processes.process = "*/proc/sys/kernel/sysrq" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_unix_shell_enable_all_sysrq_functions_filter`

[ESCU - Linux Visudo Utility Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to looks for suspicious commandline that add entry to /etc/sudoers by using visudo utility tool in linux platform. This technique may abuse by adversaries, malware author and red teamers to gain elevated privilege to targeted or compromised host. /etc/sudoers file controls who can run what commands as what users on what machines and can also control special things such as whether you need a password for particular commands. The file is composed of aliases (basically variables) and user specifications (which control who can run what).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to looks for suspicious commandline that add entry to /etc/sudoers by using visudo utility tool in linux platform. This technique may abuse by adversaries, malware author and red teamers to gain elevated privilege to targeted or compromised host. /etc/sudoers file controls who can run what commands as what users on what machines and can also control special things such as whether you need a password for particular commands. The file is composed of aliases (basically variables) and user specifications (which control who can run what).
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Linux Visudo Utility Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Privilege Escalation", "Linux Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A commandline $process$ executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 16}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Linux Visudo Utility Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Privilege Escalation", "Linux Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 40, "impact": 40, "mitre_attack": ["T1548.003", "T1548"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = visudo by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_visudo_utility_execution_filter`

[ESCU - Living Off The Land - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following correlation identifies a distinct amount of analytics associated with the Living Off The Land analytic story that identify potentially suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105", "T1190", "T1059", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following correlation identifies a distinct amount of analytics associated with the Living Off The Land analytic story that identify potentially suspicious behavior.
action.escu.how_to_implement = To implement this correlation search a user needs to enable all detections in the Living Off The Land Analytic Story and confirm it is generating risk events. A simple search `index=risk analyticstories="Living Off The Land"` should contain events.
action.escu.known_false_positives = There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation. Modify the static value distinct_detection_name to a higher value. It is also required to tune analytics that are also tagged to ensure volume is never too much.
action.escu.creation_date = 2022-09-09
action.escu.modification_date = 2022-09-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Living Off The Land - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Living Off The Land - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1105", "T1190", "T1059", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following correlation identifies a distinct amount of analytics associated with the Living Off The Land analytic story that identify potentially suspicious behavior.
action.notable.param.rule_title = RBA: Living Off The Land
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Living Off The Land" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 5 | `living_off_the_land_filter`

[ESCU - Loading Of Dynwrapx Module - Rule]
action.escu = 0
action.escu.enabled = 1
description = DynamicWrapperX is an ActiveX component that can be used in a script to call Windows API functions, but it requires the dynwrapx.dll to be installed and registered. With that, registering or loading dynwrapx.dll to a host is highly suspicious. In most instances when it is used maliciously, the best way to triage is to review parallel processes and pivot on the process_guid. Review the registry for any suspicious modifications meant to load dynwrapx.dll. Identify any suspicious module loads of dynwrapx.dll. This detection will return and identify the processes that invoke vbs/wscript/cscript.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055", "T1055.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = DynamicWrapperX is an ActiveX component that can be used in a script to call Windows API functions, but it requires the dynwrapx.dll to be installed and registered. With that, registering or loading dynwrapx.dll to a host is highly suspicious. In most instances when it is used maliciously, the best way to triage is to review parallel processes and pivot on the process_guid. Review the registry for any suspicious modifications meant to load dynwrapx.dll. Identify any suspicious module loads of dynwrapx.dll. This detection will return and identify the processes that invoke vbs/wscript/cscript.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on processes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives should be limited, however it is possible to filter by Processes.process_name and specific processes (ex. wscript.exe). Filter as needed. This may need modification based on EDR telemetry and how it brings in registry data. For example, removal of (Default).
action.escu.creation_date = 2021-11-18
action.escu.modification_date = 2021-11-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Loading Of Dynwrapx Module - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Remcos", "AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = dynwrapx.dll loaded by process $process_name$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Loading Of Dynwrapx Module - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1055", "T1055.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = DynamicWrapperX is an ActiveX component that can be used in a script to call Windows API functions, but it requires the dynwrapx.dll to be installed and registered. With that, registering or loading dynwrapx.dll to a host is highly suspicious. In most instances when it is used maliciously, the best way to triage is to review parallel processes and pivot on the process_guid. Review the registry for any suspicious modifications meant to load dynwrapx.dll. Identify any suspicious module loads of dynwrapx.dll. This detection will return and identify the processes that invoke vbs/wscript/cscript.
action.notable.param.rule_title = Loading Of Dynwrapx Module
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 (ImageLoaded = "*\\dynwrapx.dll" OR OriginalFileName = "dynwrapx.dll" OR  Product = "DynamicWrapperX") | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName Product process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `loading_of_dynwrapx_module_filter`

[ESCU - Local Account Discovery with Net - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to query for local users. The two arguments `user` and 'users', return a list of all local users. Red Teams and adversaries alike use net.exe to enumerate users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to query for local users. The two arguments `user` and 'users', return a list of all local users. Red Teams and adversaries alike use net.exe to enumerate users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Local Account Discovery with Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Sandworm Tools"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Local Account Discovery with Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` (Processes.process=*user OR Processes.process=*users) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `local_account_discovery_with_net_filter`

[ESCU - Local Account Discovery With Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for local users. The argument `useraccount` is used to leverage WMI to return a list of all local users. Red Teams and adversaries alike use net.exe to enumerate users for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to query for local users. The argument `useraccount` is used to leverage WMI to return a list of all local users. Red Teams and adversaries alike use net.exe to enumerate users for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Local Account Discovery With Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Local Account Discovery With Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1087", "T1087.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*useraccount*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `local_account_discovery_with_wmic_filter`

[ESCU - Log4Shell CVE-2021-44228 Exploitation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This correlation find exploitation of Log4Shell CVE-2021-44228 against systems using detections from Splunk Security Content Analytic Story. It does this by calculating the distinct count of MITRE ATT&CK tactics from Log4Shell detections fired. If the count is larger than 2 or more distinct MITRE ATT&CK tactics we assume high problability of exploitation. The Analytic story breaks down into 3 major phases of a Log4Shell exploitation, specifically> Initial Payload delivery eg. `${jndi:ldap://PAYLOAD_INJECTED}` Call back to malicious LDAP server eg. Exploit.class Post Exploitation Activity/Lateral Movement using Powershell or similar T1562.001 Each of these phases fall into different MITRE ATT&CK Tactics (Initial Access, Execution, Command And Control), by looking into 2 or more phases showing up in detections triggerd is how this correlation search finds exploitation. If we get a notable from this correlation search the best way to triage it is by investigating the affected systems against Log4Shell exploitation using Splunk SOAR playbooks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105", "T1190", "T1059", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = This correlation find exploitation of Log4Shell CVE-2021-44228 against systems using detections from Splunk Security Content Analytic Story. It does this by calculating the distinct count of MITRE ATT&CK tactics from Log4Shell detections fired. If the count is larger than 2 or more distinct MITRE ATT&CK tactics we assume high problability of exploitation. The Analytic story breaks down into 3 major phases of a Log4Shell exploitation, specifically> Initial Payload delivery eg. `${jndi:ldap://PAYLOAD_INJECTED}` Call back to malicious LDAP server eg. Exploit.class Post Exploitation Activity/Lateral Movement using Powershell or similar T1562.001 Each of these phases fall into different MITRE ATT&CK Tactics (Initial Access, Execution, Command And Control), by looking into 2 or more phases showing up in detections triggerd is how this correlation search finds exploitation. If we get a notable from this correlation search the best way to triage it is by investigating the affected systems against Log4Shell exploitation using Splunk SOAR playbooks.
action.escu.how_to_implement = To implement this correlation search a user needs to enable all detections in the Log4Shell Analytic Story and confirm it is generation risk events. A simple search `index=risk analyticstories="Log4Shell CVE-2021-44228"` should contain events.
action.escu.known_false_positives = There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation.
action.escu.creation_date = 2022-09-09
action.escu.modification_date = 2022-09-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Log4Shell CVE-2021-44228 Exploitation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Log4Shell CVE-2021-44228", "CISA AA22-320A"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Log4Shell CVE-2021-44228 Exploitation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1105", "T1190", "T1059", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This correlation find exploitation of Log4Shell CVE-2021-44228 against systems using detections from Splunk Security Content Analytic Story. It does this by calculating the distinct count of MITRE ATT&CK tactics from Log4Shell detections fired. If the count is larger than 2 or more distinct MITRE ATT&CK tactics we assume high problability of exploitation. The Analytic story breaks down into 3 major phases of a Log4Shell exploitation, specifically> Initial Payload delivery eg. `${jndi:ldap://PAYLOAD_INJECTED}` Call back to malicious LDAP server eg. Exploit.class Post Exploitation Activity/Lateral Movement using Powershell or similar T1562.001 Each of these phases fall into different MITRE ATT&CK Tactics (Initial Access, Execution, Command And Control), by looking into 2 or more phases showing up in detections triggerd is how this correlation search finds exploitation. If we get a notable from this correlation search the best way to triage it is by investigating the affected systems against Log4Shell exploitation using Splunk SOAR playbooks.
action.notable.param.rule_title = RBA: Log4Shell CVE-2021-44228 Exploitation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Log4Shell CVE-2021-44228" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 2 | `log4shell_cve_2021_44228_exploitation_filter`

[ESCU - Logon Script Event Trigger Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious modification of registry entry to persist and gain privilege escalation upon booting up of compromised host. This technique was seen in several APT and malware where it modify UserInitMprLogonScript registry entry to its malicious payload to be executed upon boot up of the machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1037", "T1037.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious modification of registry entry to persist and gain privilege escalation upon booting up of compromised host. This technique was seen in several APT and malware where it modify UserInitMprLogonScript registry entry to its malicious payload to be executed upon boot up of the machine.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Logon Script Event Trigger Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Windows Privilege Escalation", "Hermetic Wiper", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $Registry.registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Logon Script Event Trigger Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Windows Privilege Escalation", "Hermetic Wiper", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1037", "T1037.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious modification of registry entry to persist and gain privilege escalation upon booting up of compromised host. This technique was seen in several APT and malware where it modify UserInitMprLogonScript registry entry to its malicious payload to be executed upon boot up of the machine.
action.notable.param.rule_title = Logon Script Event Trigger Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Environment\\UserInitMprLogonScript") by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `logon_script_event_trigger_execution_filter`

[ESCU - LOLBAS With Network Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies LOLBAS with network traffic. When adversaries abuse LOLBAS they are often used to download malicious code or executables. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like downloading malicious code. Looking for these process can help defenders identify lateral movement, command-and-control, or exfiltration activies.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1105", "T1567", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = The following analytic identifies LOLBAS with network traffic. When adversaries abuse LOLBAS they are often used to download malicious code or executables. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like downloading malicious code. Looking for these process can help defenders identify lateral movement, command-and-control, or exfiltration activies.
action.escu.how_to_implement = To successfully implement this detection you must ingest events into the Network traffic data model that contain the source, destination, and communicating process in the app feild. Relevant processes must also be ingested in the Endpoint data model with matching process_id feild. Sysmon EID1 and EID3 are good examples of this type this data type.
action.escu.known_false_positives = Legitmate usage of internal automation or scripting, espically powershell.exe internal to internal or logon scripts. It may be necessary to omit internal IP ranges if extremely noisy. ie NOT dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","170.98.0.0/16","0:0:0:0:0:0:0:1") 
action.escu.creation_date = 2021-12-09
action.escu.modification_date = 2021-12-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - LOLBAS With Network Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = The LOLBAS $process_name$ on device $src$ was seen communicating with $dest$.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - LOLBAS With Network Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1105", "T1567", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies LOLBAS with network traffic. When adversaries abuse LOLBAS they are often used to download malicious code or executables. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like downloading malicious code. Looking for these process can help defenders identify lateral movement, command-and-control, or exfiltration activies.
action.notable.param.rule_title = LOLBAS With Network Traffic
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic where (All_Traffic.app IN ("*Regsvcs.exe", "*\\Ftp.exe", "*OfflineScannerShell.exe", "*Rasautou.exe", "*Schtasks.exe", "*Xwizard.exe", "*Pnputil.exe", "*Atbroker.exe", "*Pcwrun.exe", "*Ttdinject.exe", "*Mshta.exe", "*Bitsadmin.exe", "*Certoc.exe", "*Ieexec.exe", "*Microsoft.Workflow.Compiler.exe", "*Runscripthelper.exe", "*Forfiles.exe", "*Msbuild.exe", "*Register-cimprovider.exe", "*Tttracer.exe", "*Ie4uinit.exe", "*Bash.exe", "*Hh.exe", "*SettingSyncHost.exe", "*Cmstp.exe", "*Stordiag.exe", "*Scriptrunner.exe", "*Odbcconf.exe", "*Extexport.exe", "*Msdt.exe", "*WorkFolders.exe", "*Diskshadow.exe", "*Mavinject.exe", "*Regasm.exe", "*Gpscript.exe", "*Regsvr32.exe", "*Msiexec.exe", "*Wuauclt.exe", "*Presentationhost.exe", "*Wmic.exe", "*Runonce.exe", "*Syncappvpublishingserver.exe", "*Verclsid.exe", "*Infdefaultinstall.exe", "*Installutil.exe", "*Netsh.exe", "*Wab.exe", "*Dnscmd.exe", "*\\At.exe", "*Pcalua.exe", "*Msconfig.exe", "*makecab.exe", "*cscript.exe", "*notepad.exe", "*\\cmd.exe", "*certutil.exe", "*\\powershell.exe", "*powershell_ise.exe")) by All_Traffic.app,All_Traffic.src,All_Traffic.src_ip,All_Traffic.user,All_Traffic.dest,All_Traffic.dest_ip | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rex field=app ".*\\\(?<process_name>.*)$" | rename app as process | `lolbas_with_network_traffic_filter`

[ESCU - MacOS - Re-opened Applications - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list.
action.escu.creation_date = 2020-02-07
action.escu.modification_date = 2020-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - MacOS - Re-opened Applications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["ColdRoot MacOS RAT"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MacOS - Re-opened Applications - Rule
action.correlationsearch.annotations = {"analytic_story": ["ColdRoot MacOS RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.
action.notable.param.rule_title = MacOS - Re-opened Applications
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter`

[ESCU - MacOS LOLbin - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect multiple executions of Living off the Land (LOLbin) binaries in a short period of time.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.004", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect multiple executions of Living off the Land (LOLbin) binaries in a short period of time.
action.escu.how_to_implement = This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery.
action.escu.known_false_positives = None identified.
action.escu.creation_date = 2022-03-04
action.escu.modification_date = 2022-03-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - MacOS LOLbin - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = Multiplle LOLbin are executed on host $host$ by user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "process", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MacOS LOLbin - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.004", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Detect multiple executions of Living off the Land (LOLbin) binaries in a short period of time.
action.notable.param.rule_title = MacOS LOLbin
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `osquery` name=es_process_events columns.cmdline IN ("find*", "crontab*", "screencapture*", "openssl*", "curl*", "wget*", "killall*", "funzip*") | rename columns.* as * | stats  min(_time) as firstTime max(_time) as lastTime values(cmdline) as cmdline, values(pid) as pid, values(parent) as parent, values(path) as path, values(signing_id) as signing_id,  dc(path) as dc_path by username host | rename username as User, cmdline as process, path as process_path | where dc_path > 3 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `macos_lolbin_filter`

[ESCU - MacOS plutil - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect usage of plutil to modify plist files. Adversaries can modiy plist files to executed binaries or add command line arguments. Plist files in auto-run locations are executed upon user logon or system startup.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1647"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect usage of plutil to modify plist files. Adversaries can modiy plist files to executed binaries or add command line arguments. Plist files in auto-run locations are executed upon user logon or system startup.
action.escu.how_to_implement = This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery.
action.escu.known_false_positives = Administrators using plutil to change plist files.
action.escu.creation_date = 2022-05-26
action.escu.modification_date = 2022-05-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - MacOS plutil - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = plutil are executed on $host$ from $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "process", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MacOS plutil - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1647"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Detect usage of plutil to modify plist files. Adversaries can modiy plist files to executed binaries or add command line arguments. Plist files in auto-run locations are executed upon user logon or system startup.
action.notable.param.rule_title = MacOS plutil
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `osquery` name=es_process_events columns.path=/usr/bin/plutil | rename columns.* as * | stats count  min(_time) as firstTime max(_time) as lastTime by username host cmdline pid path parent signing_id | rename username as User, cmdline as process, path as process_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `macos_plutil_filter`

[ESCU - Mailsniper Invoke functions - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect known mailsniper.ps1 functions executed in a machine. This technique was seen in some attacker to harvest some sensitive e-mail in a compromised exchange server.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1114", "T1114.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect known mailsniper.ps1 functions executed in a machine. This technique was seen in some attacker to harvest some sensitive e-mail in a compromised exchange server.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Mailsniper Invoke functions - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = mailsniper.ps1 functions $ScriptBlockText$ executed on a $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Mailsniper Invoke functions - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1114", "T1114.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect known mailsniper.ps1 functions executed in a machine. This technique was seen in some attacker to harvest some sensitive e-mail in a compromised exchange server.
action.notable.param.rule_title = Mailsniper Invoke functions
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mailsniper_invoke_functions_filter`

[ESCU - Malicious InProcServer32 Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process modifying the registry with a known malicious CLSID under InProcServer32. Most COM classes are registered with the operating system and are identified by a GUID that represents the Class Identifier (CLSID) within the registry (usually under HKLM\\Software\\Classes\\CLSID or HKCU\\Software\\Classes\\CLSID).  Behind the implementation of a COM class is the server (some binary) that is referenced within registry keys under the CLSID.  The LocalServer32 key represents a path to an executable (exe) implementation, and the InprocServer32 key represents a path to a dynamic link library (DLL) implementation (Bohops). During triage, review parallel processes for suspicious activity. Pivot on the process GUID to see the full timeline of events. Analyze the value and look for file modifications. Being this is looking for inprocserver32, a DLL found in the value will most likely be loaded by a parallel process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.010", "T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process modifying the registry with a known malicious CLSID under InProcServer32. Most COM classes are registered with the operating system and are identified by a GUID that represents the Class Identifier (CLSID) within the registry (usually under HKLM\\Software\\Classes\\CLSID or HKCU\\Software\\Classes\\CLSID).  Behind the implementation of a COM class is the server (some binary) that is referenced within registry keys under the CLSID.  The LocalServer32 key represents a path to an executable (exe) implementation, and the InprocServer32 key represents a path to a dynamic link library (DLL) implementation (Bohops). During triage, review parallel processes for suspicious activity. Pivot on the process GUID to see the full timeline of events. Analyze the value and look for file modifications. Being this is looking for inprocserver32, a DLL found in the value will most likely be loaded by a parallel process.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, filter as needed. In our test case, Remcos used regsvr32.exe to modify the registry. It may be required, dependent upon the EDR tool producing registry events, to remove (Default) from the command-line.
action.escu.creation_date = 2021-10-05
action.escu.modification_date = 2021-10-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Malicious InProcServer32 Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Regsvr32 Activity", "Remcos"]
action.risk = 1
action.risk.param._risk_message = The $process_name$ was identified on endpoint $dest$ modifying the registry with a known malicious clsid under InProcServer32.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Malicious InProcServer32 Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvr32 Activity", "Remcos"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218.010", "T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a process modifying the registry with a known malicious CLSID under InProcServer32. Most COM classes are registered with the operating system and are identified by a GUID that represents the Class Identifier (CLSID) within the registry (usually under HKLM\\Software\\Classes\\CLSID or HKCU\\Software\\Classes\\CLSID).  Behind the implementation of a COM class is the server (some binary) that is referenced within registry keys under the CLSID.  The LocalServer32 key represents a path to an executable (exe) implementation, and the InprocServer32 key represents a path to a dynamic link library (DLL) implementation (Bohops). During triage, review parallel processes for suspicious activity. Pivot on the process GUID to see the full timeline of events. Analyze the value and look for file modifications. Being this is looking for inprocserver32, a DLL found in the value will most likely be loaded by a parallel process.
action.notable.param.rule_title = Malicious InProcServer32 Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\CLSID\\{89565275-A714-4a43-912E-978B935EDCCC}\\InProcServer32\\(Default)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest Registry.process_guid Registry.user | `drop_dm_object_name(Registry)` | fields _time dest registry_path registry_key_name registry_value_name process_name process_path process process_guid user] | stats count min(_time) as firstTime max(_time) as lastTime by dest, process_name registry_path registry_key_name registry_value_name user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_inprocserver32_modification_filter`

[ESCU - Malicious Powershell Executed As A Service - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection is to identify the abuse the Windows SC.exe to execute malicious commands or payloads via PowerShell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This detection is to identify the abuse the Windows SC.exe to execute malicious commands or payloads via PowerShell.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = Creating a hidden powershell service is rare and could key off of those instances.
action.escu.creation_date = 2021-04-07
action.escu.modification_date = 2021-04-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Malicious Powershell Executed As A Service - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Malicious Powershell"]
action.risk = 1
action.risk.param._risk_message = Identifies the abuse the Windows SC.exe to execute malicious powerShell as a service $Service_File_Name$ by $user$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Malicious Powershell Executed As A Service - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious Powershell"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection is to identify the abuse the Windows SC.exe to execute malicious commands or payloads via PowerShell.
action.notable.param.rule_title = Malicious Powershell Executed As A Service
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_system` EventCode=7045 | eval l_Service_File_Name=lower(Service_File_Name) | regex l_Service_File_Name="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" | regex l_Service_File_Name="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type Service_Account user dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_filter`

[ESCU - Malicious PowerShell Process - Encoded Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of the EncodedCommand PowerShell parameter. This is typically used by Administrators to run complex scripts, but commonly used by adversaries to hide their code. \
The analytic identifies all variations of EncodedCommand, as PowerShell allows the ability to shorten the parameter. For example enc, enco, encod and so forth. In addition, through our research it was identified that PowerShell will interpret different command switch types beyond the hyphen. We have added endash, emdash, horizontal bar, and forward slash. \
During triage, review parallel events to determine legitimacy. Tune as needed based on admin scripts in use. \
Alternatively, may use regex per matching here https://regexr.com/662ov.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of the EncodedCommand PowerShell parameter. This is typically used by Administrators to run complex scripts, but commonly used by adversaries to hide their code. \
The analytic identifies all variations of EncodedCommand, as PowerShell allows the ability to shorten the parameter. For example enc, enco, encod and so forth. In addition, through our research it was identified that PowerShell will interpret different command switch types beyond the hyphen. We have added endash, emdash, horizontal bar, and forward slash. \
During triage, review parallel events to determine legitimacy. Tune as needed based on admin scripts in use. \
Alternatively, may use regex per matching here https://regexr.com/662ov.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = System administrators may use this option, but it's not common.
action.escu.creation_date = 2022-01-18
action.escu.modification_date = 2022-01-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hermetic Wiper", "Malicious PowerShell", "NOBELIUM Group", "WhisperGate", "DarkCrystal RAT", "Qakbot", "CISA AA22-320A", "Sandworm Tools", "Data Destruction", "Volt Typhoon"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Malicious PowerShell Process - Encoded Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Malicious PowerShell", "NOBELIUM Group", "WhisperGate", "DarkCrystal RAT", "Qakbot", "CISA AA22-320A", "Sandworm Tools", "Data Destruction", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1027"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/|||]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]") | `malicious_powershell_process___encoded_command_filter`

[ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DHS Report TA18-074A", "HAFNIUM Group", "DarkCrystal RAT", "AsyncRAT", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = PowerShell local execution policy bypass attempt on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "HAFNIUM Group", "DarkCrystal RAT", "AsyncRAT", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.
action.notable.param.rule_title = Malicious PowerShell Process - Execution Policy Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter`

[ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = These characters might be legitimately on the command-line, but it is not common.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Powershell.exe running with potential obfuscated arguments on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.
action.notable.param.rule_title = Malicious PowerShell Process With Obfuscation Techniques
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 

[ESCU - Mimikatz PassTheTicket CommandLine Parameters - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic looks for the use of Mimikatz command line parameters leveraged to execute pass the ticket attacks. Red teams and adversaries alike may use the pass the ticket technique using stolen Kerberos tickets to move laterally within an environment, bypassing normal system access controls. Defenders should be aware that adversaries may customize the source code of Mimikatz and modify the command line parameters. This would effectively bypass this analytic.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550", "T1550.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic looks for the use of Mimikatz command line parameters leveraged to execute pass the ticket attacks. Red teams and adversaries alike may use the pass the ticket technique using stolen Kerberos tickets to move laterally within an environment, bypassing normal system access controls. Defenders should be aware that adversaries may customize the source code of Mimikatz and modify the command line parameters. This would effectively bypass this analytic.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although highly unlikely, legitimate applications may use the same command line parameters as Mimikatz.
action.escu.creation_date = 2022-02-01
action.escu.modification_date = 2022-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Mimikatz PassTheTicket CommandLine Parameters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "CISA AA22-320A", "Sandworm Tools"]
action.risk = 1
action.risk.param._risk_message = Mimikatz command line parameters for pass the ticket attacks were used on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Mimikatz PassTheTicket CommandLine Parameters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "CISA AA22-320A", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1550", "T1550.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic looks for the use of Mimikatz command line parameters leveraged to execute pass the ticket attacks. Red teams and adversaries alike may use the pass the ticket technique using stolen Kerberos tickets to move laterally within an environment, bypassing normal system access controls. Defenders should be aware that adversaries may customize the source code of Mimikatz and modify the command line parameters. This would effectively bypass this analytic.
action.notable.param.rule_title = Mimikatz PassTheTicket CommandLine Parameters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*sekurlsa::tickets /export*" OR Processes.process = "*kerberos::ptt*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mimikatz_passtheticket_commandline_parameters_filter`

[ESCU - Mmc LOLBAS Execution Process Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `mmc.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the DCOM protocol and the MMC20 COM object, the executed command is spawned as a child processs of `mmc.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of mmc.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.003", "T1218.014"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `mmc.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the DCOM protocol and the MMC20 COM object, the executed command is spawned as a child processs of `mmc.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of mmc.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate applications may trigger this behavior, filter as needed.
action.escu.creation_date = 2021-11-23
action.escu.modification_date = 2021-11-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Mmc LOLBAS Execution Process Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = Mmc.exe spawned a LOLBAS process on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Mmc LOLBAS Execution Process Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1021", "T1021.003", "T1218.014"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `mmc.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the DCOM protocol and the MMC20 COM object, the executed command is spawned as a child processs of `mmc.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of mmc.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.notable.param.rule_title = Mmc LOLBAS Execution Process Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=mmc.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mmc_lolbas_execution_process_spawn_filter`

[ESCU - Modification Of Wallpaper - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious modification of registry to deface or change the wallpaper of a compromised machines as part of its payload. This technique was commonly seen in ransomware like REVIL where it create a bitmap file contain a note that the machine was compromised and make it as a wallpaper.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1491"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies suspicious modification of registry to deface or change the wallpaper of a compromised machines as part of its payload. This technique was commonly seen in ransomware like REVIL where it create a bitmap file contain a note that the machine was compromised and make it as a wallpaper.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = 3rd party tool may used to changed the wallpaper of the machine
action.escu.creation_date = 2021-06-02
action.escu.modification_date = 2021-06-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Modification Of Wallpaper - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ransomware", "Revil Ransomware", "BlackMatter Ransomware", "Windows Registry Abuse", "Brute Ratel C4", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = Wallpaper modification on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Modification Of Wallpaper - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware", "BlackMatter Ransomware", "Windows Registry Abuse", "Brute Ratel C4", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1491"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies suspicious modification of registry to deface or change the wallpaper of a compromised machines as part of its payload. This technique was commonly seen in ransomware like REVIL where it create a bitmap file contain a note that the machine was compromised and make it as a wallpaper.
action.notable.param.rule_title = Modification Of Wallpaper
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode =13  (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Image != "*\\explorer.exe") OR (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Details = "*\\temp\\*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Image TargetObject Details Computer process_guid process_id user_id | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modification_of_wallpaper_filter`

[ESCU - Modify ACL permission To Files Or Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious modification of ACL permission to a files or folder to make it available to everyone. This technique may be used by the adversary to evade ACLs or protected files access. This changes is commonly configured by the file or directory owner with appropriate permission. This behavior is a good indicator if this command seen on a machine utilized by an account with no permission to do so.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies suspicious modification of ACL permission to a files or folder to make it available to everyone. This technique may be used by the adversary to evade ACLs or protected files access. This changes is commonly configured by the file or directory owner with appropriate permission. This behavior is a good indicator if this command seen on a machine utilized by an account with no permission to do so.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = administrators may use this command. Filter as needed.
action.escu.creation_date = 2022-03-17
action.escu.modification_date = 2022-03-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Modify ACL permission To Files Or Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = Suspicious ACL permission modification on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 32}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Modify ACL permission To Files Or Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "cis20": ["CIS 10"], "confidence": 80, "impact": 40, "mitre_attack": ["T1222"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe") AND Processes.process = "*/G*" AND (Processes.process = "* everyone:*"  OR Processes.process = "* SYSTEM:*" OR Processes.process = "* S-1-1-0:*") by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modify_acl_permission_to_files_or_folder_filter`

[ESCU - Monitor Registry Keys for Print Monitors - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.010", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = You will encounter noise from legitimate print-monitor registry entries.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Monitor Registry Keys for Print Monitors - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = New print monitor added on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Monitor Registry Keys for Print Monitors - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1547.010", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.
action.notable.param.rule_title = Monitor Registry Keys for Print Monitors
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `monitor_registry_keys_for_print_monitors_filter`

[ESCU - MS Exchange Mailbox Replication service writing Active Server Pages - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. The analytic is limited to process name MSExchangeMailboxReplication.exe, which typically does not write .aspx files to disk. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.003", "T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. The analytic is limited to process name MSExchangeMailboxReplication.exe, which typically does not write .aspx files to disk. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
action.escu.known_false_positives = The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - MS Exchange Mailbox Replication service writing Active Server Pages - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["ProxyShell", "Ransomware", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = A file - $file_name$ was written to disk that is related to IIS exploitation related to ProxyShell. Review further file modifications on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "file_name", "risk_object_type": "other", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MS Exchange Mailbox Replication service writing Active Server Pages - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyShell", "Ransomware", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1505", "T1505.003", "T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. The analytic is limited to process name MSExchangeMailboxReplication.exe, which typically does not write .aspx files to disk. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.
action.notable.param.rule_title = MS Exchange Mailbox Replication service writing Active Server Pages
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=MSExchangeMailboxReplication.exe  by _time span=1h Processes.process_id Processes.process_name Processes.process_guid Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process process_guid] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `ms_exchange_mailbox_replication_service_writing_active_server_pages_filter`

[ESCU - MS Scripting Process Loading Ldap Module - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious MS scripting process such as wscript.exe or cscript.exe that loading ldap module to process ldap query. This behavior was seen in FIN7 implant where it uses javascript to execute ldap query to parse host information that will send to its C2 server. this anomaly detections is a good initial step to hunt further a suspicious ldap query or ldap related events to the host that may give you good information regarding ldap or AD information processing or might be a attacker.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious MS scripting process such as wscript.exe or cscript.exe that loading ldap module to process ldap query. This behavior was seen in FIN7 implant where it uses javascript to execute ldap query to parse host information that will send to its C2 server. this anomaly detections is a good initial step to hunt further a suspicious ldap query or ldap related events to the host that may give you good information regarding ldap or AD information processing or might be a attacker.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
action.escu.known_false_positives = automation scripting language may used by network operator to do ldap query.
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - MS Scripting Process Loading Ldap Module - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["FIN7"]
action.risk = 1
action.risk.param._risk_message = $process_name$ loading ldap modules $ImageLoaded$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MS Scripting Process Loading Ldap Module - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded IN ("*\\Wldap32.dll", "*\\adsldp.dll", "*\\adsldpc.dll") | stats min(_time) as firstTime max(_time) as lastTime count by Image EventCode process_name ProcessId ProcessGuid Computer ImageLoaded | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_ldap_module_filter`

[ESCU - MS Scripting Process Loading WMI Module - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious MS scripting process such as wscript.exe or cscript.exe that loading wmi module to process wmi query. This behavior was seen in FIN7 implant where it uses javascript to execute wmi query to parse host information that will send to its C2 server. this anomaly detections is a good initial step to hunt further a suspicious wmi query or wmi related events to the host that may give you good information regarding process that are commonly using wmi query or modules or might be an attacker using this technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious MS scripting process such as wscript.exe or cscript.exe that loading wmi module to process wmi query. This behavior was seen in FIN7 implant where it uses javascript to execute wmi query to parse host information that will send to its C2 server. this anomaly detections is a good initial step to hunt further a suspicious wmi query or wmi related events to the host that may give you good information regarding process that are commonly using wmi query or modules or might be an attacker using this technique.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
action.escu.known_false_positives = automation scripting language may used by network operator to do ldap query.
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - MS Scripting Process Loading WMI Module - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["FIN7"]
action.risk = 1
action.risk.param._risk_message = $process_name$ loading wmi modules $ImageLoaded$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MS Scripting Process Loading WMI Module - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1059", "T1059.007"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded IN ("*\\fastprox.dll", "*\\wbemdisp.dll", "*\\wbemprox.dll", "*\\wbemsvc.dll" , "*\\wmiutils.dll", "*\\wbemcomn.dll") | stats min(_time) as firstTime max(_time) as lastTime count by Image EventCode process_name ProcessId ProcessGuid Computer ImageLoaded | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_wmi_module_filter`

[ESCU - MSBuild Suspicious Spawned By Script Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious child process of MSBuild spawned by Windows Script Host - cscript or wscript. This behavior or event are commonly seen and used by malware or adversaries to execute malicious msbuild process using malicious script in the compromised host. During triage, review parallel processes and identify any file modifications. MSBuild may load a script from the same path without having command-line arguments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1127.001", "T1127"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious child process of MSBuild spawned by Windows Script Host - cscript or wscript. This behavior or event are commonly seen and used by malware or adversaries to execute malicious msbuild process using malicious script in the compromised host. During triage, review parallel processes and identify any file modifications. MSBuild may load a script from the same path without having command-line arguments.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as developers do not spawn MSBuild via a WSH.
action.escu.creation_date = 2021-10-04
action.escu.modification_date = 2021-10-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - MSBuild Suspicious Spawned By Script Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild"]
action.risk = 1
action.risk.param._risk_message = Msbuild.exe process spawned by $parent_process_name$ on $dest$ executed by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MSBuild Suspicious Spawned By Script Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1127.001", "T1127"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious child process of MSBuild spawned by Windows Script Host - cscript or wscript. This behavior or event are commonly seen and used by malware or adversaries to execute malicious msbuild process using malicious script in the compromised host. During triage, review parallel processes and identify any file modifications. MSBuild may load a script from the same path without having command-line arguments.
action.notable.param.rule_title = MSBuild Suspicious Spawned By Script Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("wscript.exe", "cscript.exe") AND `process_msbuild` by Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msbuild_suspicious_spawned_by_script_process_filter`

[ESCU - Mshta spawning Rundll32 OR Regsvr32 Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious mshta.exe process that spawn rundll32 or regsvr32 child process. This technique was seen in several malware nowadays like trickbot to load its initial .dll stage loader to execute and download the the actual trickbot payload.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious mshta.exe process that spawn rundll32 or regsvr32 child process. This technique was seen in several malware nowadays like trickbot to load its initial .dll stage loader to execute and download the the actual trickbot payload.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = limitted. this anomaly behavior is not commonly seen in clean host.
action.escu.creation_date = 2021-07-19
action.escu.modification_date = 2021-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Mshta spawning Rundll32 OR Regsvr32 Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trickbot", "IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = a mshta parent process $parent_process_name$ spawn child process $process_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Mshta spawning Rundll32 OR Regsvr32 Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious mshta.exe process that spawn rundll32 or regsvr32 child process. This technique was seen in several malware nowadays like trickbot to load its initial .dll stage loader to execute and download the the actual trickbot payload.
action.notable.param.rule_title = Mshta spawning Rundll32 OR Regsvr32 Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "mshta.exe"  `process_rundll32` OR `process_regsvr32` by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `mshta_spawning_rundll32_or_regsvr32_process_filter`

[ESCU - MSHTML Module Load in Office Product - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the module load of mshtml.dll into an Office product. This behavior has been related to CVE-2021-40444, whereas the malicious document will load ActiveX, which activates the MSHTML component. The vulnerability resides in the MSHTML component. During triage, identify parallel processes and capture any file modifications for analysis.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following detection identifies the module load of mshtml.dll into an Office product. This behavior has been related to CVE-2021-40444, whereas the malicious document will load ActiveX, which activates the MSHTML component. The vulnerability resides in the MSHTML component. During triage, identify parallel processes and capture any file modifications for analysis.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process names and image loads from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Limited false positives will be present, however, tune as necessary.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - MSHTML Module Load in Office Product - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spearphishing Attachments", "Microsoft MSHTML Remote Code Execution CVE-2021-40444", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ loading mshtml.dll.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MSHTML Module Load in Office Product - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Microsoft MSHTML Remote Code Execution CVE-2021-40444", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-40444"], "impact": 80, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the module load of mshtml.dll into an Office product. This behavior has been related to CVE-2021-40444, whereas the malicious document will load ActiveX, which activates the MSHTML component. The vulnerability resides in the MSHTML component. During triage, identify parallel processes and capture any file modifications for analysis.
action.notable.param.rule_title = MSHTML Module Load in Office Product
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=7  parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe","Graph.exe","winproj.exe") ImageLoaded IN ("*\\mshtml.dll", "*\\Microsoft.mshtml.dll","*\\IE.Interop.MSHTML.dll","*\\MshtmlDac.dll","*\\MshtmlDed.dll","*\\MshtmlDer.dll") | stats count min(_time) as firstTime max(_time) as lastTime by dest, process_name, ImageLoaded, OriginalFileName, ProcessGuid  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mshtml_module_load_in_office_product_filter`

[ESCU - MSI Module Loaded by Non-System Binary - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies `msi.dll` being loaded by a binary not located in `system32`, `syswow64`, `winsxs` or `windows` paths. This behavior is most recently related to InstallerFileTakeOver, or CVE-2021-41379, and DLL side-loading. CVE-2021-41379 requires a binary to be dropped and `msi.dll` to be loaded by it. To Successful exploitation of this issue happens in four parts \
1. Generation of an MSI that will trigger bad behavior. \
1. Preparing a directory for MSI installation. \
1. Inducing an error state. \
1. Racing to introduce a junction and a symlink to trick msiexec.exe to modify the attacker specified file. \
In addition, `msi.dll` has been abused in DLL side-loading attacks by being loaded by non-system binaries.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic identifies `msi.dll` being loaded by a binary not located in `system32`, `syswow64`, `winsxs` or `windows` paths. This behavior is most recently related to InstallerFileTakeOver, or CVE-2021-41379, and DLL side-loading. CVE-2021-41379 requires a binary to be dropped and `msi.dll` to be loaded by it. To Successful exploitation of this issue happens in four parts \
1. Generation of an MSI that will trigger bad behavior. \
1. Preparing a directory for MSI installation. \
1. Inducing an error state. \
1. Racing to introduce a junction and a symlink to trick msiexec.exe to modify the attacker specified file. \
In addition, `msi.dll` has been abused in DLL side-loading attacks by being loaded by non-system binaries.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = It is possible some Administrative utilities will load msi.dll outside of normal system paths, filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - MSI Module Loaded by Non-System Binary - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - MSI Module Loaded by Non-System Binary - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-41379"], "impact": 80, "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 ImageLoaded="*\\msi.dll" NOT (Image IN ("*\\System32\\*","*\\syswow64\\*","*\\windows\\*", "*\\winsxs\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msi_module_loaded_by_non_system_binary_filter`

[ESCU - Msmpeng Application DLL Side Loading - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder. This technique was seen couple days ago with revil ransomware in Kaseya Supply chain. The approach is to drop an old version of msmpeng.exe to load the actual payload name as mspvc.dll which will load the revil ransomware to the compromise machine
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder. This technique was seen couple days ago with revil ransomware in Kaseya Supply chain. The approach is to drop an old version of msmpeng.exe to load the actual payload name as mspvc.dll which will load the revil ransomware to the compromise machine
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = quite minimal false positive expected.
action.escu.creation_date = 2023-03-15
action.escu.modification_date = 2023-03-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Msmpeng Application DLL Side Loading - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Msmpeng Application DLL Side Loading - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder. This technique was seen couple days ago with revil ransomware in Kaseya Supply chain. The approach is to drop an old version of msmpeng.exe to load the actual payload name as mspvc.dll which will load the revil ransomware to the compromise machine
action.notable.param.rule_title = Msmpeng Application DLL Side Loading
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll")  AND NOT (Filesystem.file_path IN ("*\\Program Files\\windows defender\\*","*\\WinSxS\\*defender-service*","*\\WinSxS\\Temp\\*defender-service*")) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter`

[ESCU - Net Localgroup Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic will identify the use of localgroup discovery using `net localgroup`. During triage, review parallel processes and identify any further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic will identify the use of localgroup discovery using `net localgroup`. During triage, review parallel processes and identify any further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present. Tune as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Net Localgroup Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "IcedID", "Windows Discovery Techniques", "Windows Post-Exploitation", "Azorult", "Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Net Localgroup Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prestige Ransomware", "Volt Typhoon", "Graceful Wipe Out Attack", "IcedID", "Windows Discovery Techniques", "Windows Post-Exploitation", "Azorult", "Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=net.exe OR Processes.process_name=net1.exe (Processes.process="*localgroup*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `net_localgroup_discovery_filter`

[ESCU - NET Profiler UAC bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect modification of registry to bypass UAC windows feature. This technique is to add a payload dll path on .NET COR file path that will be loaded by mmc.exe as soon it was executed. This detection rely on monitoring the registry key and values in the detection area. It may happened that windows update some dll related to mmc.exe and add dll path in this registry. In this case filtering is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect modification of registry to bypass UAC windows feature. This technique is to add a payload dll path on .NET COR file path that will be loaded by mmc.exe as soon it was executed. This detection rely on monitoring the registry key and values in the detection area. It may happened that windows update some dll related to mmc.exe and add dll path in this registry. In this case filtering is needed.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = limited false positive. It may trigger by some windows update that will modify this registry.
action.escu.creation_date = 2022-02-18
action.escu.modification_date = 2022-02-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - NET Profiler UAC bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Suspicious modification of registry $registry_path$ with possible payload path $registry_path$ and key $registry_key_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - NET Profiler UAC bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect modification of registry to bypass UAC windows feature. This technique is to add a payload dll path on .NET COR file path that will be loaded by mmc.exe as soon it was executed. This detection rely on monitoring the registry key and values in the detection area. It may happened that windows update some dll related to mmc.exe and add dll path in this registry. In this case filtering is needed.
action.notable.param.rule_title = NET Profiler UAC bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_data = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter`

[ESCU - Network Connection Discovery With Arp - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `arp.exe` utilized to get a listing of network connections on a compromised system. Red Teams and adversaries alike may use arp.exe for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `arp.exe` utilized to get a listing of network connections on a compromised system. Red Teams and adversaries alike may use arp.exe for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-10
action.escu.modification_date = 2021-09-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Network Connection Discovery With Arp - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Qakbot", "Windows Post-Exploitation", "Prestige Ransomware", "Volt Typhoon", "IcedID"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Network Connection Discovery With Arp - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Qakbot", "Windows Post-Exploitation", "Prestige Ransomware", "Volt Typhoon", "IcedID"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="arp.exe") (Processes.process=*-a*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_arp_filter`

[ESCU - Network Connection Discovery With Net - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` with command-line arguments utilized to get a listing of network connections on a compromised system. Red Teams and adversaries alike may use net.exe for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` with command-line arguments utilized to get a listing of network connections on a compromised system. Red Teams and adversaries alike may use net.exe for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-10
action.escu.modification_date = 2021-09-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Network Connection Discovery With Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Azorult", "Windows Post-Exploitation", "Prestige Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Network Connection Discovery With Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Azorult", "Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process=*use*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_net_filter`

[ESCU - Network Connection Discovery With Netstat - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `netstat.exe` with command-line arguments utilized to get a listing of network connections on a compromised system. Red Teams and adversaries alike may use netstat.exe for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `netstat.exe` with command-line arguments utilized to get a listing of network connections on a compromised system. Red Teams and adversaries alike may use netstat.exe for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-10
action.escu.modification_date = 2021-09-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Network Connection Discovery With Netstat - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Qakbot", "CISA AA22-277A", "Windows Post-Exploitation", "Prestige Ransomware", "Volt Typhoon"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Network Connection Discovery With Netstat - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Qakbot", "CISA AA22-277A", "Windows Post-Exploitation", "Prestige Ransomware", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="netstat.exe") (Processes.process=*-a*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_netstat_filter`

[ESCU - Network Discovery Using Route Windows App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic look for a spawned process of route.exe windows application. Adversaries and red teams alike abuse this application the recon or do a network discovery on a target host. but one possible false positive might be an automated tool used by a system administator or a powershell script in amazon ec2 config services.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1016", "T1016.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic look for a spawned process of route.exe windows application. Adversaries and red teams alike abuse this application the recon or do a network discovery on a target host. but one possible false positive might be an automated tool used by a system administator or a powershell script in amazon ec2 config services.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = A network operator or systems administrator may utilize an automated host discovery application that may generate false positives or an amazon ec2 script that uses this application. Filter as needed.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Network Discovery Using Route Windows App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Qakbot", "CISA AA22-277A", "Windows Post-Exploitation", "Prestige Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Network Discovery Using Route Windows App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Qakbot", "CISA AA22-277A", "Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1016", "T1016.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_route` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_discovery_using_route_windows_app_filter`

[ESCU - Network Share Discovery Via Dir Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies object access on Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The IcedID malware family also implements this behavior to try to infect other machines in the infected network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1135"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies object access on Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The IcedID malware family also implements this behavior to try to infect other machines in the infected network.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5140 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.
action.escu.known_false_positives = System Administrators may use looks like net.exe or "dir commandline" for troubleshooting or administrations tasks. However, this will typically come only from certain users and certain systems that can be added to an allow list.
action.escu.creation_date = 2023-05-23
action.escu.modification_date = 2023-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Network Share Discovery Via Dir Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["IcedID"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Network Share Discovery Via Dir Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1135"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=5140 ShareName IN("\\\\*\\ADMIN$","\\\\*\\C$","*\\\\*\\IPC$") AccessMask= 0x1 | stats min(_time) as firstTime max(_time) as lastTime count by ShareName IpAddress ObjectType SubjectUserName SubjectDomainName IpPort AccessMask Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_share_discovery_via_dir_command_filter`

[ESCU - Nishang PowershellTCPOneLine - Rule]
action.escu = 0
action.escu.enabled = 1
description = This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote Command And Control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote Command And Control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives may be present. Filter as needed based on initial analysis.
action.escu.creation_date = 2021-03-03
action.escu.modification_date = 2021-03-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Nishang PowershellTCPOneLine - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["HAFNIUM Group"]
action.risk = 1
action.risk.param._risk_message = Possible Nishang Invoke-PowerShellTCPOneLine behavior on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Nishang PowershellTCPOneLine - Rule
action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote Command And Control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process.
action.notable.param.rule_title = Nishang PowershellTCPOneLine
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process=*Net.Sockets.TCPClient* AND Processes.process=*System.Text.ASCIIEncoding*) by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `nishang_powershelltcponeline_filter`

[ESCU - NLTest Domain Trust Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.
action.escu.creation_date = 2022-04-18
action.escu.modification_date = 2022-04-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - NLTest Domain Trust Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware", "Domain Trust Discovery", "IcedID", "Active Directory Discovery", "Qakbot"]
action.risk = 1
action.risk.param._risk_message = Domain trust discovery execution on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - NLTest Domain Trust Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Domain Trust Discovery", "IcedID", "Active Directory Discovery", "Qakbot"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1482"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next.
action.notable.param.rule_title = NLTest Domain Trust Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_nltest` (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter`

[ESCU - Non Chrome Process Accessing Chrome Default Dir - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect an anomaly event of a non-chrome process accessing the files in chrome user default folder. This folder contains all the sqlite database of the chrome browser related to users login, history, cookies and etc. Most of the RAT, trojan spy as well as FIN7 jssloader try to parse the those sqlite database to collect information on the compromised host. This SACL Event (4663) need to be enabled to tthe firefox profile directory to be eable to use this. Since you monitoring this access to the folder,  we observed noise that needs to be filter out and hence added sqlite db browser and explorer .exe to make this detection more stable.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1555", "T1555.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect an anomaly event of a non-chrome process accessing the files in chrome user default folder. This folder contains all the sqlite database of the chrome browser related to users login, history, cookies and etc. Most of the RAT, trojan spy as well as FIN7 jssloader try to parse the those sqlite database to collect information on the compromised host. This SACL Event (4663) need to be enabled to tthe firefox profile directory to be eable to use this. Since you monitoring this access to the folder,  we observed noise that needs to be filter out and hence added sqlite db browser and explorer .exe to make this detection more stable.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = other browser not listed related to firefox may catch by this rule.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Non Chrome Process Accessing Chrome Default Dir - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Remcos", "3CX Supply Chain Attack", "AgentTesla", "RedLine Stealer", "FIN7", "Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = a non firefox browser process $process_name$ accessing $Object_Name$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Non Chrome Process Accessing Chrome Default Dir - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos", "3CX Supply Chain Attack", "AgentTesla", "RedLine Stealer", "FIN7", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1555", "T1555.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 NOT (process_name IN ("*\\chrome.exe", "*\\explorer.exe", "*sql*")) Object_Name="*\\Google\\Chrome\\User Data\\Default*" | stats count min(_time) as firstTime max(_time) as lastTime by Object_Name Object_Type process_name Access_Mask Accesses process_id EventCode dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_chrome_process_accessing_chrome_default_dir_filter`

[ESCU - Non Firefox Process Access Firefox Profile Dir - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect an anomaly event of a non-firefox process accessing the files in the profile folder. This folder contains all the sqlite database of the firefox browser related to users login, history, cookies and etc. Most of the RAT, trojan spy as well as FIN7 jssloader try to parse the those sqlite database to collect information on the compromised host. This SACL Event (4663) needs to be enabled to the firefox profile directory to use this. Since this is monitoring the access to the folder, we have obsevered noise and hence added `sqlite db browser` and `explorer.exe` to make this detection more stable.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1555", "T1555.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect an anomaly event of a non-firefox process accessing the files in the profile folder. This folder contains all the sqlite database of the firefox browser related to users login, history, cookies and etc. Most of the RAT, trojan spy as well as FIN7 jssloader try to parse the those sqlite database to collect information on the compromised host. This SACL Event (4663) needs to be enabled to the firefox profile directory to use this. Since this is monitoring the access to the folder, we have obsevered noise and hence added `sqlite db browser` and `explorer.exe` to make this detection more stable.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = other browser not listed related to firefox may catch by this rule.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Non Firefox Process Access Firefox Profile Dir - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Azorult", "Remcos", "3CX Supply Chain Attack", "AgentTesla", "RedLine Stealer", "FIN7", "Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = a non firefox browser process $process_name$ accessing $Object_Name$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Non Firefox Process Access Firefox Profile Dir - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult", "Remcos", "3CX Supply Chain Attack", "AgentTesla", "RedLine Stealer", "FIN7", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1555", "T1555.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 NOT (process_name IN ("*\\firefox.exe", "*\\explorer.exe", "*sql*")) Object_Name="*\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles*" | stats count min(_time) as firstTime max(_time) as lastTime by Object_Name Object_Type process_name Access_Mask Accesses process_id EventCode dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_firefox_process_access_firefox_profile_dir_filter`

[ESCU - Notepad with no Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies behavior related to default SliverC2 framework where it will inject into Notepad.exe and spawn Notepad.exe with no command line arguments. In testing, this is a common procedure for SliverC2 usage, however may be modified or changed. From Microsoft, "The Sideload, SpawnDll, and Execute-Assembly commands spawn and inject into notepad.exe by default. The following query finds process creation events where the same process creates and injects into notepad.exe within 10 seconds."
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies behavior related to default SliverC2 framework where it will inject into Notepad.exe and spawn Notepad.exe with no command line arguments. In testing, this is a common procedure for SliverC2 usage, however may be modified or changed. From Microsoft, "The Sideload, SpawnDll, and Execute-Assembly commands spawn and inject into notepad.exe by default. The following query finds process creation events where the same process creates and injects into notepad.exe within 10 seconds."
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering may need to occur based on organization endpoint behavior.
action.escu.creation_date = 2023-02-22
action.escu.modification_date = 2023-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Notepad with no Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BishopFox Sliver Adversary Emulation Framework"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ with no command line arguments.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Notepad with no Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["BishopFox Sliver Adversary Emulation Framework"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies behavior related to default SliverC2 framework where it will inject into Notepad.exe and spawn Notepad.exe with no command line arguments. In testing, this is a common procedure for SliverC2 usage, however may be modified or changed. From Microsoft, "The Sideload, SpawnDll, and Execute-Assembly commands spawn and inject into notepad.exe by default. The following query finds process creation events where the same process creates and injects into notepad.exe within 10 seconds."
action.notable.param.rule_title = Notepad with no Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=notepad.exe AND Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(notepad\.exe.{0,4}$)" | `notepad_with_no_command_line_arguments_filter`

[ESCU - Ntdsutil Export NTDS - Rule]
action.escu = 0
action.escu.enabled = 1
description = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \
ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \
This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \
ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \
This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives.
action.escu.creation_date = 2021-01-28
action.escu.modification_date = 2021-01-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ntdsutil Export NTDS - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "HAFNIUM Group", "Living Off The Land", "Prestige Ransomware", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Active Directory NTDS export on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ntdsutil Export NTDS - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group", "Living Off The Land", "Prestige Ransomware", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 50, "impact": 100, "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \
ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \
This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination.
action.notable.param.rule_title = Ntdsutil Export NTDS
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter`

[ESCU - Office Application Drop Executable - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious MS office application that drops or creates executables or scripts in a Windows Operating System. This behavior is commonly seen in spear phishing office attachment where it drop malicious files or script to compromised the host. It might be some normal macro may drop script or tools as part of automation but still this behavior is reallly suspicious and not commonly seen in normal office application
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious MS office application that drops or creates executables or scripts in a Windows Operating System. This behavior is commonly seen in spear phishing office attachment where it drop malicious files or script to compromised the host. It might be some normal macro may drop script or tools as part of automation but still this behavior is reallly suspicious and not commonly seen in normal office application
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
action.escu.known_false_positives = office macro for automation may do this behavior
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Application Drop Executable - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["FIN7", "AgentTesla", "CVE-2023-21716 Word RTF Heap Corruption", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ drops a file $file_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Application Drop Executable - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7", "AgentTesla", "CVE-2023-21716 Word RTF Heap Corruption", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious MS office application that drops or creates executables or scripts in a Windows Operating System. This behavior is commonly seen in spear phishing office attachment where it drop malicious files or script to compromised the host. It might be some normal macro may drop script or tools as part of automation but still this behavior is reallly suspicious and not commonly seen in normal office application
action.notable.param.rule_title = Office Application Drop Executable
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe","*.dll","*.pif","*.scr","*.js","*.vbs","*.vbe","*.ps1") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process process_guid] | dedup file_create_time | table dest, process_name, process, file_create_time, file_name, file_path, process_guid | `office_application_drop_executable_filter`

[ESCU - Office Application Spawn Regsvr32 process - Rule]
action.escu = 0
action.escu.enabled = 1
description = this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like IcedID that used MS office as its weapon or attack vector to initially infect the machines.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like IcedID that used MS office as its weapon or attack vector to initially infect the machines.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Application Spawn Regsvr32 process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Qakbot"]
action.risk = 1
action.risk.param._risk_message = Office application spawning regsvr32.exe on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Application Spawn Regsvr32 process - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Qakbot"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like IcedID that used MS office as its weapon or attack vector to initially infect the machines.
action.notable.param.rule_title = Office Application Spawn Regsvr32 process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "outlook.exe" OR Processes.parent_process_name = "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name="msaccess.exe") `process_regsvr32` by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_regsvr32_process_filter`

[ESCU - Office Application Spawn rundll32 process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection was designed to identify suspicious spawned processes of known MS office applications due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This detection was designed to identify suspicious spawned processes of known MS office applications due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Application Spawn rundll32 process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Trickbot", "IcedID", "AgentTesla"]
action.risk = 1
action.risk.param._risk_message = Office application spawning rundll32.exe on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Application Spawn rundll32 process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Trickbot", "IcedID", "AgentTesla"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection was designed to identify suspicious spawned processes of known MS office applications due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines.
action.notable.param.rule_title = Office Application Spawn rundll32 process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name= "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name = "msaccess.exe") AND `process_rundll32` by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_rundll32_process_filter`

[ESCU - Office Document Creating Schedule Task - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Document Creating Schedule Task - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = Office document creating a schedule task on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Document Creating Schedule Task - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'
action.notable.param.rule_title = Office Document Creating Schedule Task
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 parent_process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe") ImageLoaded = "*\\taskschd.dll" | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image parent_process_name ProcessId ProcessGuid | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_creating_schedule_task_filter`

[ESCU - Office Document Executing Macro Code - Rule]
action.escu = 0
action.escu.enabled = 1
description = this detection was designed to identifies suspicious office documents that using macro code. Macro code is known to be one of the prevalent weaponization or attack vector of threat actor. This malicious macro code is embed to a office document as an attachment that may execute malicious payload, download malware payload or other malware component. It is really good practice to disable macro by default to avoid automatically execute macro code while opening or closing a office document files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = this detection was designed to identifies suspicious office documents that using macro code. Macro code is known to be one of the prevalent weaponization or attack vector of threat actor. This malicious macro code is embed to a office document as an attachment that may execute malicious payload, download malware payload or other malware component. It is really good practice to disable macro by default to avoid automatically execute macro code while opening or closing a office document files.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
action.escu.known_false_positives = Normal Office Document macro use for automation
action.escu.creation_date = 2023-01-24
action.escu.modification_date = 2023-01-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Document Executing Macro Code - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spearphishing Attachments", "Trickbot", "IcedID", "DarkCrystal RAT", "AgentTesla", "Qakbot", "Azorult", "Remcos"]
action.risk = 1
action.risk.param._risk_message = Office document executing a macro on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Document Executing Macro Code - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Trickbot", "IcedID", "DarkCrystal RAT", "AgentTesla", "Qakbot", "Azorult", "Remcos"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this detection was designed to identifies suspicious office documents that using macro code. Macro code is known to be one of the prevalent weaponization or attack vector of threat actor. This malicious macro code is embed to a office document as an attachment that may execute malicious payload, download malware payload or other malware component. It is really good practice to disable macro by default to avoid automatically execute macro code while opening or closing a office document files.
action.notable.param.rule_title = Office Document Executing Macro Code
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 parent_process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid | rename Computer as dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_executing_macro_code_filter`

[ESCU - Office Document Spawned Child Process To Download - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect potential malicious office document executing lolbin child process to download payload or other malware. Since most of the attacker abused the capability of office document to execute living on land application to blend it to the normal noise in the infected machine to cover its track.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect potential malicious office document executing lolbin child process to download payload or other malware. Since most of the attacker abused the capability of office document to execute living on land application to blend it to the normal noise in the infected machine to cover its track.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Default browser not in the filter list.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Document Spawned Child Process To Download - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = Office document spawning suspicious child process on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Document Spawned Child Process To Download - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect potential malicious office document executing lolbin child process to download payload or other malware. Since most of the attacker abused the capability of office document to execute living on land application to blend it to the normal noise in the infected machine to cover its track.
action.notable.param.rule_title = Office Document Spawned Child Process To Download
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe", "Graph.exe","winproj.exe") Processes.process IN ("*http:*","*https:*") NOT (Processes.original_file_name IN("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe"))  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_spawned_child_process_to_download_filter`

[ESCU - Office Product Spawn CMD Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to detect a suspicious office product process that spawn cmd child process. This is commonly seen in a ms office product having macro to execute shell command to download or execute malicious lolbin relative to its malicious code. This is seen in trickbot spear phishing doc where it execute shell cmd to run mshta payload.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = this search is to detect a suspicious office product process that spawn cmd child process. This is commonly seen in a ms office product having macro to execute shell command to download or execute malicious lolbin relative to its malicious code. This is seen in trickbot spear phishing doc where it execute shell cmd to run mshta payload.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = IT or network admin may create an document automation that will run shell script.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawn CMD Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trickbot", "DarkCrystal RAT", "Azorult", "Remcos", "Qakbot", "AgentTesla", "CVE-2023-21716 Word RTF Heap Corruption", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = an office product parent process $parent_process_name$ spawn child process $process_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawn CMD Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "DarkCrystal RAT", "Azorult", "Remcos", "Qakbot", "AgentTesla", "CVE-2023-21716 Word RTF Heap Corruption", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to detect a suspicious office product process that spawn cmd child process. This is commonly seen in a ms office product having macro to execute shell command to download or execute malicious lolbin relative to its malicious code. This is seen in trickbot spear phishing doc where it execute shell cmd to run mshta payload.
action.notable.param.rule_title = Office Product Spawn CMD Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name= "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name= "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name = "msaccess.exe" OR  Processes.parent_process_name="Graph.exe" OR Processes.parent_process_name="winproj.exe") `process_cmd` by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_product_spawn_cmd_process_filter`

[ESCU - Office Product Spawning BITSAdmin - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `bitsadmin.exe`. In malicious instances, the command-line of `bitsadmin.exe` will contain a URL to a remote destination or similar command-line arguments as transfer, Download, priority, Foreground. In addition, Threat Research has released a detections identifying suspicious use of `bitsadmin.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `bitsadmin.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `bitsadmin.exe`. In malicious instances, the command-line of `bitsadmin.exe` will contain a URL to a remote destination or similar command-line arguments as transfer, Download, priority, Foreground. In addition, Threat Research has released a detections identifying suspicious use of `bitsadmin.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `bitsadmin.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = No false positives known. Filter as needed.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawning BITSAdmin - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawning BITSAdmin - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `bitsadmin.exe`. In malicious instances, the command-line of `bitsadmin.exe` will contain a URL to a remote destination or similar command-line arguments as transfer, Download, priority, Foreground. In addition, Threat Research has released a detections identifying suspicious use of `bitsadmin.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `bitsadmin.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.notable.param.rule_title = Office Product Spawning BITSAdmin
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe", "Graph.exe","winproj.exe") `process_bitsadmin` by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_bitsadmin_filter`

[ESCU - Office Product Spawning CertUtil - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `certutil.exe`. In malicious instances, the command-line of `certutil.exe` will contain a URL to a remote destination. In addition, Threat Research has released a detections identifying suspicious use of `certutil.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `certutil.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `certutil.exe`. In malicious instances, the command-line of `certutil.exe` will contain a URL to a remote destination. In addition, Threat Research has released a detections identifying suspicious use of `certutil.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `certutil.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = No false positives known. Filter as needed.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawning CertUtil - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "AgentTesla", "Trickbot", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawning CertUtil - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "AgentTesla", "Trickbot", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `certutil.exe`. In malicious instances, the command-line of `certutil.exe` will contain a URL to a remote destination. In addition, Threat Research has released a detections identifying suspicious use of `certutil.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `certutil.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.notable.param.rule_title = Office Product Spawning CertUtil
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe", "Graph.exe","winproj.exe") `process_certutil` by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_certutil_filter`

[ESCU - Office Product Spawning MSHTA - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = No false positives known. Filter as needed.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawning MSHTA - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "IcedID", "Azorult", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawning MSHTA - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "IcedID", "Azorult", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.notable.param.rule_title = Office Product Spawning MSHTA
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe", "onenote.exe","onenotem.exe", "msaccess.exe","Graph.exe","winproj.exe") `process_mshta` by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_mshta_filter`

[ESCU - Office Product Spawning Rundll32 with no DLL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. In malicious instances, the command-line of `rundll32.exe` will look like `rundll32 ..\oepddl.igk2,DllRegisterServer`. In addition, Threat Research has released a detection identifying the use of `DllRegisterServer` on the command-line of `rundll32.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the `DLL` that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. In malicious instances, the command-line of `rundll32.exe` will look like `rundll32 ..\oepddl.igk2,DllRegisterServer`. In addition, Threat Research has released a detection identifying the use of `DllRegisterServer` on the command-line of `rundll32.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the `DLL` that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, but if any are present, filter as needed.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawning Rundll32 with no DLL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ and no dll commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawning Rundll32 with no DLL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. In malicious instances, the command-line of `rundll32.exe` will look like `rundll32 ..\oepddl.igk2,DllRegisterServer`. In addition, Threat Research has released a detection identifying the use of `DllRegisterServer` on the command-line of `rundll32.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the `DLL` that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.notable.param.rule_title = Office Product Spawning Rundll32 with no DLL
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe", "Graph.exe","winproj.exe") `process_rundll32` (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_rundll32_with_no_dll_filter`

[ESCU - Office Product Spawning Windows Script Host - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic will identify a Windows Office Product spawning WScript.exe or CScript.exe. Tuning may be required based on legitimate application usage that may spawn scripts from an Office product.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic will identify a Windows Office Product spawning WScript.exe or CScript.exe. Tuning may be required based on legitimate application usage that may spawn scripts from an Office product.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present based on macro based approved documents in the organization. Filtering may be needed.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawning Windows Script Host - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Remcos", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ on host $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 63}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawning Windows Script Host - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Remcos", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic will identify a Windows Office Product spawning WScript.exe or CScript.exe. Tuning may be required based on legitimate application usage that may spawn scripts from an Office product.
action.notable.param.rule_title = Office Product Spawning Windows Script Host
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe","Graph.exe","winproj.exe") Processes.process_name IN ("wscript.exe", "cscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_windows_script_host_filter`

[ESCU - Office Product Spawning Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the latest behavior utilized by Ursnif malware family. This detection identifies any Windows Office Product spawning `wmic.exe`. In malicious instances, the command-line of `wmic.exe` will contain `wmic process call create`. In addition, Threat Research has released a detection identifying the use of `wmic process call create` on the command-line of `wmic.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `wmic.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies the latest behavior utilized by Ursnif malware family. This detection identifies any Windows Office Product spawning `wmic.exe`. In malicious instances, the command-line of `wmic.exe` will contain `wmic process call create`. In addition, Threat Research has released a detection identifying the use of `wmic process call create` on the command-line of `wmic.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `wmic.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = No false positives known. Filter as needed.
action.escu.creation_date = 2023-07-11
action.escu.modification_date = 2023-07-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Spawning Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "FIN7", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Spawning Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "FIN7", "CVE-2023-36884 Office and Windows HTML RCE Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the latest behavior utilized by Ursnif malware family. This detection identifies any Windows Office Product spawning `wmic.exe`. In malicious instances, the command-line of `wmic.exe` will contain `wmic process call create`. In addition, Threat Research has released a detection identifying the use of `wmic process call create` on the command-line of `wmic.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `wmic.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.notable.param.rule_title = Office Product Spawning Wmic
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe", "Graph.exe","winproj.exe") `process_wmic` by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_wmic_filter`

[ESCU - Office Product Writing cab or inf - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies behavior related to CVE-2021-40444. Whereas the malicious document will load ActiveX and download the remote payload (.inf, .cab). During triage, review parallel processes and further activity on endpoint to identify additional patterns. Retrieve the file modifications and analyze further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies behavior related to CVE-2021-40444. Whereas the malicious document will load ActiveX and download the remote payload (.inf, .cab). During triage, review parallel processes and further activity on endpoint to identify additional patterns. Retrieve the file modifications and analyze further.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
action.escu.known_false_positives = The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Product Writing cab or inf - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Microsoft MSHTML Remote Code Execution CVE-2021-40444"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on $dest$ writing an inf or cab file to this. This is not typical of $process_name$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Product Writing cab or inf - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Microsoft MSHTML Remote Code Execution CVE-2021-40444"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-40444"], "impact": 80, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies behavior related to CVE-2021-40444. Whereas the malicious document will load ActiveX and download the remote payload (.inf, .cab). During triage, review parallel processes and further activity on endpoint to identify additional patterns. Retrieve the file modifications and analyze further.
action.notable.param.rule_title = Office Product Writing cab or inf
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.inf","*.cab") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] | dedup file_create_time | table dest, process_name, process, file_create_time, file_name, file_path, proc_guid | `office_product_writing_cab_or_inf_filter`

[ESCU - Office Spawning Control - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies control.exe spawning from an office product. This detection identifies any Windows Office Product spawning `control.exe`. In malicious instances, the command-line of `control.exe` will contain a file path to a .cpl or .inf, related to CVE-2021-40444. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. review parallel and child processes to identify further suspicious behavior
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies control.exe spawning from an office product. This detection identifies any Windows Office Product spawning `control.exe`. In malicious instances, the command-line of `control.exe` will contain a file path to a .cpl or .inf, related to CVE-2021-40444. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. review parallel and child processes to identify further suspicious behavior
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present.
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Office Spawning Control - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Microsoft MSHTML Remote Code Execution CVE-2021-40444"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ clicking a suspicious attachment.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Office Spawning Control - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Microsoft MSHTML Remote Code Execution CVE-2021-40444"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-40444"], "impact": 80, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies control.exe spawning from an office product. This detection identifies any Windows Office Product spawning `control.exe`. In malicious instances, the command-line of `control.exe` will contain a file path to a .cpl or .inf, related to CVE-2021-40444. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. review parallel and child processes to identify further suspicious behavior
action.notable.param.rule_title = Office Spawning Control
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") Processes.process_name=control.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `office_spawning_control_filter`

[ESCU - Outbound Network Connection from Java Using Default Ports - Rule]
action.escu = 0
action.escu.enabled = 1
description = A required step while exploiting the CVE-2021-44228-Log4j vulnerability is that the victim server will perform outbound connections to attacker-controlled infrastructure. This is required as part of the JNDI lookup as well as for retrieving the second stage .class payload. The following analytic identifies the Java process reaching out to default ports used by the LDAP and RMI protocols. This behavior could represent successfull exploitation. Note that adversaries can easily decide to use arbitrary ports for these protocols and potentially bypass this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = A required step while exploiting the CVE-2021-44228-Log4j vulnerability is that the victim server will perform outbound connections to attacker-controlled infrastructure. This is required as part of the JNDI lookup as well as for retrieving the second stage .class payload. The following analytic identifies the Java process reaching out to default ports used by the LDAP and RMI protocols. This behavior could represent successfull exploitation. Note that adversaries can easily decide to use arbitrary ports for these protocols and potentially bypass this detection.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate Java applications may use perform outbound connections to these ports. Filter as needed
action.escu.creation_date = 2022-06-28
action.escu.modification_date = 2022-06-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Outbound Network Connection from Java Using Default Ports - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = Java performed outbound connections to default ports of LDAP or RMI on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Outbound Network Connection from Java Using Default Ports - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228"], "cis20": ["CIS 10"], "confidence": 60, "cve": ["CVE-2021-44228"], "impact": 90, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = A required step while exploiting the CVE-2021-44228-Log4j vulnerability is that the victim server will perform outbound connections to attacker-controlled infrastructure. This is required as part of the JNDI lookup as well as for retrieving the second stage .class payload. The following analytic identifies the Java process reaching out to default ports used by the LDAP and RMI protocols. This behavior could represent successfull exploitation. Note that adversaries can easily decide to use arbitrary ports for these protocols and potentially bypass this detection.
action.notable.param.rule_title = Outbound Network Connection from Java Using Default Ports
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where (Processes.process_name="java.exe" OR Processes.process_name=javaw.exe OR Processes.process_name=javaw.exe) by _time Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where (All_Traffic.dest_port= 389 OR All_Traffic.dest_port= 636 OR All_Traffic.dest_port = 1389 OR All_Traffic.dest_port = 1099 ) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process connection_to_CNC dest_port| `outbound_network_connection_from_java_using_default_ports_filter`

[ESCU - Overwriting Accessibility Binaries - Rule]
action.escu = 0
action.escu.enabled = 1
description = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546", "T1546.008"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.
action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
action.escu.known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Overwriting Accessibility Binaries - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation", "Flax Typhoon"]
action.risk = 1
action.risk.param._risk_message = A suspicious file modification or replace in $file_path$  in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "file_path", "threat_object_type": "file"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Overwriting Accessibility Binaries - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation", "Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1546", "T1546.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.
action.notable.param.rule_title = Overwriting Accessibility Binaries
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`

[ESCU - PaperCut NG Suspicious Behavior Debug Log - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic is designed to monitor and detect potential exploitation attempts targeting a PaperCut NG server by analyzing its debug log data. By focusing on public IP addresses accessing the PaperCut NG instance, this analytic aims to identify unauthorized or suspicious access attempts. Furthermore, it searches for specific URIs that have been discovered in the proof of concept code, which are associated with known exploits or vulnerabilities. The analytic is focused on the user admin. Regex is used mainly because the log is not parsed by Splunk and there is no TA for this debug log.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic is designed to monitor and detect potential exploitation attempts targeting a PaperCut NG server by analyzing its debug log data. By focusing on public IP addresses accessing the PaperCut NG instance, this analytic aims to identify unauthorized or suspicious access attempts. Furthermore, it searches for specific URIs that have been discovered in the proof of concept code, which are associated with known exploits or vulnerabilities. The analytic is focused on the user admin. Regex is used mainly because the log is not parsed by Splunk and there is no TA for this debug log.
action.escu.how_to_implement = Debug logs must be enabled and shipped to Splunk in order to properly identify behavior with this analytic.
action.escu.known_false_positives = False positives may be present, as this is based on the admin user accessing the Papercut NG instance from a public IP address. Filter as needed.
action.escu.creation_date = 2023-05-15
action.escu.modification_date = 2023-05-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - PaperCut NG Suspicious Behavior Debug Log - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PaperCut MF NG Vulnerability"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PaperCut NG Suspicious Behavior Debug Log - Rule
action.correlationsearch.annotations = {"analytic_story": ["PaperCut MF NG Vulnerability"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `papercutng` (loginType=Admin OR userName=admin) | eval uri_match=if(match(_raw, "(?i)(\/app\?service=page\/SetupCompleted|\/app|\/app\?service=page\/PrinterList|\/app\?service=direct\/1\/PrinterList\/selectPrinter&sp=l1001|\/app\?service=direct\/1\/PrinterDetails\/printerOptionsTab\.tab)"), "URI matches", null()) | eval ip_match=if(match(_raw, "(?i)((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))") AND NOT match(_raw, "(?i)(10\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(172\.(1[6-9]|2[0-9]|3[0-1])\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(192\.168\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))"), "IP matches", null()) | where  (isnotnull(uri_match) OR isnotnull(ip_match)) | stats sparkline, count, values(uri_match) AS uri_match, values(ip_match) AS ip_match latest(_raw) BY host, index, sourcetype | `papercut_ng_suspicious_behavior_debug_log_filter`

[ESCU - Password Policy Discovery with Net - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` or `net1.exe` with command line arguments used to obtain the domain password policy. Red Teams and adversaries may leverage `net.exe` for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` or `net1.exe` with command line arguments used to obtain the domain password policy. Red Teams and adversaries may leverage `net.exe` for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-26
action.escu.modification_date = 2021-08-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Password Policy Discovery with Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Password Policy Discovery with Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1201"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") AND Processes.process = "*accounts*" AND Processes.process = "*/domain*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `password_policy_discovery_with_net_filter`

[ESCU - Permission Modification using Takeown App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a modification of file or directory permission using takeown.exe windows app. This technique was seen in some ransomware that take the ownership of a folder or files to encrypt or delete it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a modification of file or directory permission using takeown.exe windows app. This technique was seen in some ransomware that take the ownership of a folder or files to encrypt or delete it.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = takeown.exe is a normal windows application that may used by network operator.
action.escu.creation_date = 2021-06-10
action.escu.modification_date = 2021-06-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Permission Modification using Takeown App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Sandworm Tools"]
action.risk = 1
action.risk.param._risk_message = A suspicious of execution of $process_name$ with process id $process_id$ and commandline $process$ to modify permission of directory or files in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Permission Modification using Takeown App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1222"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a modification of file or directory permission using takeown.exe windows app. This technique was seen in some ransomware that take the ownership of a folder or files to encrypt or delete it.
action.notable.param.rule_title = Permission Modification using Takeown App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe" Processes.process = "*/f*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `permission_modification_using_takeown_app_filter`

[ESCU - PetitPotam Network Share Access Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Windows Event Code 5145, "A network share object was checked to see whether client can be granted desired access". During our research into PetitPotam, CVE-2021-36942, we identified the ocurrence of this event on the target host with specific values. \
To enable 5145 events via Group Policy - Computer Configuration->Polices->Windows Settings->Security Settings->Advanced Audit Policy Configuration. Expand this node, go to Object Access (Audit Polices->Object Access), then select the Setting Audit Detailed File Share Audit \
It is possible this is not enabled by default and may need to be reviewed and enabled. \
During triage, review parallel security events to identify further suspicious activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1187"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Windows Event Code 5145, "A network share object was checked to see whether client can be granted desired access". During our research into PetitPotam, CVE-2021-36942, we identified the ocurrence of this event on the target host with specific values. \
To enable 5145 events via Group Policy - Computer Configuration->Polices->Windows Settings->Security Settings->Advanced Audit Policy Configuration. Expand this node, go to Object Access (Audit Polices->Object Access), then select the Setting Audit Detailed File Share Audit \
It is possible this is not enabled by default and may need to be reviewed and enabled. \
During triage, review parallel security events to identify further suspicious activity.
action.escu.how_to_implement = Windows Event Code 5145 is required to utilize this analytic and it may not be enabled in most environments.
action.escu.known_false_positives = False positives have been limited when the Anonymous Logon is used for Account Name.
action.escu.creation_date = 2021-08-31
action.escu.modification_date = 2021-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - PetitPotam Network Share Access Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["PetitPotam NTLM Relay on Active Directory Certificate Services"]
action.risk = 1
action.risk.param._risk_message = A remote host is enumerating a $dest$ to identify permissions. This is a precursor event to CVE-2021-36942, PetitPotam.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PetitPotam Network Share Access Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["PetitPotam NTLM Relay on Active Directory Certificate Services"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-36942"], "impact": 80, "mitre_attack": ["T1187"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Windows Event Code 5145, "A network share object was checked to see whether client can be granted desired access". During our research into PetitPotam, CVE-2021-36942, we identified the ocurrence of this event on the target host with specific values. \
To enable 5145 events via Group Policy - Computer Configuration->Polices->Windows Settings->Security Settings->Advanced Audit Policy Configuration. Expand this node, go to Object Access (Audit Polices->Object Access), then select the Setting Audit Detailed File Share Audit \
It is possible this is not enabled by default and may need to be reviewed and enabled. \
During triage, review parallel security events to identify further suspicious activity.
action.notable.param.rule_title = PetitPotam Network Share Access Request
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` Account_Name="ANONYMOUS LOGON" EventCode=5145 Relative_Target_Name=lsarpc | stats count min(_time) as firstTime max(_time) as lastTime by dest, Security_ID, Share_Name, Source_Address, Accesses, Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `petitpotam_network_share_access_request_filter`

[ESCU - PetitPotam Suspicious Kerberos TGT Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifes Event Code 4768, A `Kerberos authentication ticket (TGT) was requested`, successfull occurs. This behavior has been identified to assist with detecting PetitPotam, CVE-2021-36942. Once an attacer obtains a computer certificate by abusing Active Directory Certificate Services in combination with PetitPotam, the next step would be to leverage the certificate for malicious purposes. One way of doing this is to request a Kerberos Ticket Granting Ticket using a tool like Rubeus. This request will generate a 4768 event with some unusual fields depending on the environment. This analytic will require tuning, we recommend filtering Account_Name to Domain Controllers for your environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifes Event Code 4768, A `Kerberos authentication ticket (TGT) was requested`, successfull occurs. This behavior has been identified to assist with detecting PetitPotam, CVE-2021-36942. Once an attacer obtains a computer certificate by abusing Active Directory Certificate Services in combination with PetitPotam, the next step would be to leverage the certificate for malicious purposes. One way of doing this is to request a Kerberos Ticket Granting Ticket using a tool like Rubeus. This request will generate a 4768 event with some unusual fields depending on the environment. This analytic will require tuning, we recommend filtering Account_Name to Domain Controllers for your environment.
action.escu.how_to_implement = The following analytic requires Event Code 4768. Ensure that it is logging no Domain Controllers and appearing in Splunk.
action.escu.known_false_positives = False positives are possible if the environment is using certificates for authentication.
action.escu.creation_date = 2021-08-31
action.escu.modification_date = 2021-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - PetitPotam Suspicious Kerberos TGT Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["PetitPotam NTLM Relay on Active Directory Certificate Services", "Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = A Kerberos TGT was requested in a non-standard manner against $dest$, potentially related to CVE-2021-36942, PetitPotam.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PetitPotam Suspicious Kerberos TGT Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["PetitPotam NTLM Relay on Active Directory Certificate Services", "Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-36942"], "impact": 80, "mitre_attack": ["T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifes Event Code 4768, A `Kerberos authentication ticket (TGT) was requested`, successfull occurs. This behavior has been identified to assist with detecting PetitPotam, CVE-2021-36942. Once an attacer obtains a computer certificate by abusing Active Directory Certificate Services in combination with PetitPotam, the next step would be to leverage the certificate for malicious purposes. One way of doing this is to request a Kerberos Ticket Granting Ticket using a tool like Rubeus. This request will generate a 4768 event with some unusual fields depending on the environment. This analytic will require tuning, we recommend filtering Account_Name to Domain Controllers for your environment.
action.notable.param.rule_title = PetitPotam Suspicious Kerberos TGT Request
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4768 Client_Address!="::1" Certificate_Thumbprint!="" Account_Name=*$ | stats count min(_time) as firstTime max(_time) as lastTime by dest, Account_Name, Client_Address, action, Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `petitpotam_suspicious_kerberos_tgt_request_filter`

[ESCU - Ping Sleep Batch Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify the possible execution of ping sleep batch commands. This technique was seen in several malware samples and is used to trigger sleep times without explicitly calling sleep functions or commandlets. The goal is to delay the execution of malicious code and bypass detection or sandbox analysis. This  detection can be a good indicator of a process delaying its execution for malicious purposes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1497", "T1497.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify the possible execution of ping sleep batch commands. This technique was seen in several malware samples and is used to trigger sleep times without explicitly calling sleep functions or commandlets. The goal is to delay the execution of malicious code and bypass detection or sandbox analysis. This  detection can be a good indicator of a process delaying its execution for malicious purposes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator or network operator may execute this command. Please update the filter macros to remove false positives.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ping Sleep Batch Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "WhisperGate", "BlackByte Ransomware", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = suspicious $process$ commandline run in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ping Sleep Batch Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "WhisperGate", "BlackByte Ransomware", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1497", "T1497.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_ping` (Processes.parent_process = "*ping*" Processes.parent_process = *-n* Processes.parent_process="* Nul*"Processes.parent_process="*&gt;*") OR (Processes.process = "*ping*" Processes.process = *-n* Processes.process="* Nul*"Processes.process="*&gt;*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `ping_sleep_batch_command_filter`

[ESCU - Possible Browser Pass View Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect if a suspicious process contains a commandline parameter related to a web browser credential dumper. This technique is used by Remcos RAT malware which uses the Nirsoft webbrowserpassview.exe application to dump web browser credentials. Remcos uses the "/stext" command line to dump the credentials in text format. This Hunting query is a good indicator of hosts suffering from possible Remcos RAT infection. Since the hunting query is based on the parameter command and the possible path where it will save the text credential information, it may catch normal tools that are using the same command and behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1555.003", "T1555"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will detect if a suspicious process contains a commandline parameter related to a web browser credential dumper. This technique is used by Remcos RAT malware which uses the Nirsoft webbrowserpassview.exe application to dump web browser credentials. Remcos uses the "/stext" command line to dump the credentials in text format. This Hunting query is a good indicator of hosts suffering from possible Remcos RAT infection. Since the hunting query is based on the parameter command and the possible path where it will save the text credential information, it may catch normal tools that are using the same command and behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positive is quite limited. Filter is needed
action.escu.creation_date = 2021-11-22
action.escu.modification_date = 2021-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Possible Browser Pass View Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Possible Browser Pass View Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 40, "impact": 40, "mitre_attack": ["T1555.003", "T1555"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process  IN ("*/stext *", "*/shtml *", "*/LoadPasswordsIE*", "*/LoadPasswordsFirefox*", "*/LoadPasswordsChrome*", "*/LoadPasswordsOpera*", "*/LoadPasswordsSafari*" , "*/UseOperaPasswordFile*", "*/OperaPasswordFile*","*/stab*", "*/scomma*", "*/stabular*", "*/shtml*", "*/sverhtml*", "*/sxml*", "*/skeepass*" ) AND Processes.process IN ("*\\temp\\*", "*\\users\\public\\*", "*\\programdata\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `possible_browser_pass_view_parameter_filter`

[ESCU - Possible Lateral Movement PowerShell Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to identify possible lateral movement attacks that involve the spawning of a PowerShell process as a child or grandchild process of commonly abused processes. These processes include services.exe, wmiprsve.exe, svchost.exe, wsmprovhost.exe, and mmc.exe.\
Such behavior is indicative of legitimate Windows features such as the Service Control Manager, Windows Management Instrumentation, Task Scheduler, Windows Remote Management, and the DCOM protocol being abused to start a process on a remote endpoint. This behavior is often seen during lateral movement techniques where adversaries or red teams abuse these services for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.003", "T1021.006", "T1047", "T1053.005", "T1543.003", "T1059.001", "T1218.014"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to identify possible lateral movement attacks that involve the spawning of a PowerShell process as a child or grandchild process of commonly abused processes. These processes include services.exe, wmiprsve.exe, svchost.exe, wsmprovhost.exe, and mmc.exe.\
Such behavior is indicative of legitimate Windows features such as the Service Control Manager, Windows Management Instrumentation, Task Scheduler, Windows Remote Management, and the DCOM protocol being abused to start a process on a remote endpoint. This behavior is often seen during lateral movement techniques where adversaries or red teams abuse these services for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate applications may spawn PowerShell as a child process of the the identified processes. Filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Possible Lateral Movement PowerShell Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Malicious PowerShell", "Hermetic Wiper", "Data Destruction", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A PowerShell process was spawned as a child process of typically abused processes on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Possible Lateral Movement PowerShell Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Malicious PowerShell", "Hermetic Wiper", "Data Destruction", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1021", "T1021.003", "T1021.006", "T1047", "T1053.005", "T1543.003", "T1059.001", "T1218.014"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to identify possible lateral movement attacks that involve the spawning of a PowerShell process as a child or grandchild process of commonly abused processes. These processes include services.exe, wmiprsve.exe, svchost.exe, wsmprovhost.exe, and mmc.exe.\
Such behavior is indicative of legitimate Windows features such as the Service Control Manager, Windows Management Instrumentation, Task Scheduler, Windows Remote Management, and the DCOM protocol being abused to start a process on a remote endpoint. This behavior is often seen during lateral movement techniques where adversaries or red teams abuse these services for lateral movement and remote code execution.
action.notable.param.rule_title = Possible Lateral Movement PowerShell Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe OR Processes.parent_process_name=services.exe OR Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wsmprovhost.exe OR Processes.parent_process_name=mmc.exe) (Processes.process_name=powershell.exe OR (Processes.process_name=cmd.exe AND Processes.process=*powershell.exe*) OR Processes.process_name=pwsh.exe OR (Processes.process_name=cmd.exe AND Processes.process=*pwsh.exe*)) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `possible_lateral_movement_powershell_spawn_filter`

[ESCU - Potential password in username - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search identifies users who have entered their passwords in username fields. This is done by looking for failed authentication attempts using usernames with a length longer than 7 characters and a high Shannon entropy, and looks for the next successful authentication attempt from the same source system to the same destination system as the failed attempt.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.003", "T1552.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search identifies users who have entered their passwords in username fields. This is done by looking for failed authentication attempts using usernames with a length longer than 7 characters and a high Shannon entropy, and looks for the next successful authentication attempt from the same source system to the same destination system as the failed attempt.
action.escu.how_to_implement = To successfully implement this search, you need to have relevant authentication logs mapped to the Authentication data model. You also need to have the Splunk TA URL Toolbox (https://splunkbase.splunk.com/app/2734/) installed. The detection must run with a time interval shorter than endtime+1000.
action.escu.known_false_positives = Valid usernames with high entropy or source/destination system pairs with multiple authenticating users will make it difficult to identify the real user authenticating.
action.escu.creation_date = 2022-05-11
action.escu.modification_date = 2022-05-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Potential password in username - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping", "Insider Threat"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Potential password in username - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Insider Threat"], "cis20": ["CIS 10"], "confidence": 70, "impact": 30, "mitre_attack": ["T1078.003", "T1552.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication WHERE nodename=Authentication.Failed_Authentication BY "Authentication.user" | `drop_dm_object_name(Authentication)` | lookup ut_shannon_lookup word AS user | where ut_shannon>3 AND len(user)>=8 AND mvcount(src) == 1 | sort count, - ut_shannon | eval incorrect_cred=user | eval endtime=endtime+1000 | map maxsearches=70 search="| tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication WHERE nodename=Authentication.Successful_Authentication Authentication.src=\"$src$\" Authentication.dest=\"$dest$\" sourcetype IN (\"$sourcetype$\") earliest=\"$starttime$\" latest=\"$endtime$\" BY \"Authentication.user\" | `drop_dm_object_name(\"Authentication\")` | `potential_password_in_username_false_positive_reduction` | eval incorrect_cred=\"$incorrect_cred$\" | eval ut_shannon=\"$ut_shannon$\" | sort count" | where user!=incorrect_cred | outlier action=RM count | `potential_password_in_username_filter`

[ESCU - Potentially malicious code on commandline - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses a pretrained machine learning text classifier to detect potentially malicious commandlines.  The model identifies unusual combinations of keywords found in samples of commandlines where adversaries executed powershell code, primarily for C2 communication.  For example, adversaries will leverage IO capabilities such as "streamreader" and "webclient", threading capabilties such as "mutex" locks, programmatic constructs like "function" and "catch", and cryptographic operations like "computehash".  Although observing one of these keywords in a commandline script is possible, combinations of keywords observed in attack data are not typically found in normal usage of the commandline.  The model will output a score where all values above zero are suspicious, anything greater than one particularly so.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic uses a pretrained machine learning text classifier to detect potentially malicious commandlines.  The model identifies unusual combinations of keywords found in samples of commandlines where adversaries executed powershell code, primarily for C2 communication.  For example, adversaries will leverage IO capabilities such as "streamreader" and "webclient", threading capabilties such as "mutex" locks, programmatic constructs like "function" and "catch", and cryptographic operations like "computehash".  Although observing one of these keywords in a commandline script is possible, combinations of keywords observed in attack data are not typically found in normal usage of the commandline.  The model will output a score where all values above zero are suspicious, anything greater than one particularly so.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This model is an anomaly detector that identifies usage of APIs and scripting constructs that are correllated with malicious activity.  These APIs and scripting constructs are part of the programming langauge and advanced scripts may generate false positives.
action.escu.creation_date = 2022-01-14
action.escu.modification_date = 2022-01-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Potentially malicious code on commandline - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Command-Line Executions"]
action.risk = 1
action.risk.param._risk_message = Unusual command-line execution with hallmarks of malicious activity run by $user$ found on $dest$ with commandline $process$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Potentially malicious code on commandline - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions"], "cis20": ["CIS 10"], "confidence": 20, "impact": 60, "mitre_attack": ["T1059.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel="Endpoint.Processes" by Processes.parent_process_name Processes.process_name Processes.process Processes.user Processes.dest  | `drop_dm_object_name(Processes)`  | where len(process) > 200 | `potentially_malicious_code_on_cmdline_tokenize_score` | apply unusual_commandline_detection | eval score='predicted(unusual_cmdline_logits)', process=orig_process | fields - unusual_cmdline* predicted(unusual_cmdline_logits) orig_process | where score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `potentially_malicious_code_on_commandline_filter`

[ESCU - PowerShell 4104 Hunting - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following Hunting analytic assists with identifying suspicious PowerShell execution using Script Block Logging, or EventCode 4104. This analytic is not meant to be ran hourly, but occasionally to identify malicious or suspicious PowerShell. This analytic is a combination of work completed by Alex Teixeira and Splunk Threat Research Team.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following Hunting analytic assists with identifying suspicious PowerShell execution using Script Block Logging, or EventCode 4104. This analytic is not meant to be ran hourly, but occasionally to identify malicious or suspicious PowerShell. This analytic is a combination of work completed by Alex Teixeira and Splunk Threat Research Team.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Limited false positives. May filter as needed.
action.escu.creation_date = 2023-06-14
action.escu.modification_date = 2023-06-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell 4104 Hunting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction", "Flax Typhoon"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell 4104 Hunting - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction", "Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 | eval DoIt = if(match(ScriptBlockText,"(?i)(\$doit)"), "4", 0) | eval enccom=if(match(ScriptBlockText,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4}|[A-Za-z0-9+\/]{3}=|[A-Za-z0-9+\/]{2}==)") OR match(ScriptBlockText, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) | eval suspcmdlet=if(match(ScriptBlockText, "(?i)Add-Exfiltration|Add-Persistence|Add-RegBackdoor|Add-ScrnSaveBackdoor|Check-VM|Do-Exfiltration|Enabled-DuplicateToken|Exploit-Jboss|Find-Fruit|Find-GPOLocation|Find-TrustedDocuments|Get-ApplicationHost|Get-ChromeDump|Get-ClipboardContents|Get-FoxDump|Get-GPPPassword|Get-IndexedItem|Get-Keystrokes|LSASecret|Get-PassHash|Get-RegAlwaysInstallElevated|Get-RegAutoLogon|Get-RickAstley|Get-Screenshot|Get-SecurityPackages|Get-ServiceFilePermission|Get-ServicePermission|Get-ServiceUnquoted|Get-SiteListPassword|Get-System|Get-TimedScreenshot|Get-UnattendedInstallFile|Get-Unconstrained|Get-VaultCredential|Get-VulnAutoRun|Get-VulnSchTask|Gupt-Backdoor|HTTP-Login|Install-SSP|Install-ServiceBinary|Invoke-ACLScanner|Invoke-ADSBackdoor|Invoke-ARPScan|Invoke-AllChecks|Invoke-BackdoorLNK|Invoke-BypassUAC|Invoke-CredentialInjection|Invoke-DCSync|Invoke-DllInjection|Invoke-DowngradeAccount|Invoke-EgressCheck|Invoke-Inveigh|Invoke-InveighRelay|Invoke-Mimikittenz|Invoke-NetRipper|Invoke-NinjaCopy|Invoke-PSInject|Invoke-Paranoia|Invoke-PortScan|Invoke-PoshRat|Invoke-PostExfil|Invoke-PowerDump|Invoke-PowerShellTCP|Invoke-PsExec|Invoke-PsUaCme|Invoke-ReflectivePEInjection|Invoke-ReverseDNSLookup|Invoke-RunAs|Invoke-SMBScanner|Invoke-SSHCommand|Invoke-Service|Invoke-Shellcode|Invoke-Tater|Invoke-ThunderStruck|Invoke-Token|Invoke-UserHunter|Invoke-VoiceTroll|Invoke-WScriptBypassUAC|Invoke-WinEnum|MailRaider|New-HoneyHash|Out-Minidump|Port-Scan|PowerBreach|PowerUp|PowerView|Remove-Update|Set-MacAttribute|Set-Wallpaper|Show-TargetScreen|Start-CaptureServer|VolumeShadowCopyTools|NEEEEWWW|(Computer|User)Property|CachedRDPConnection|get-net\S+|invoke-\S+hunter|Install-Service|get-\S+(credent|password)|remoteps|Kerberos.*(policy|ticket)|netfirewall|Uninstall-Windows|Verb\s+Runas|AmsiBypass|nishang|Invoke-Interceptor|EXEonRemote|NetworkRelay|PowerShelludp|PowerShellIcmp|CreateShortcut|copy-vss|invoke-dll|invoke-mass|out-shortcut|Invoke-ShellCommand"),1,0) | eval base64 = if(match(lower(ScriptBlockText),"frombase64"), "4", 0) | eval empire=if(match(lower(ScriptBlockText),"system.net.webclient") AND match(lower(ScriptBlockText), "frombase64string") ,5,0) | eval mimikatz=if(match(lower(ScriptBlockText),"mimikatz") OR match(lower(ScriptBlockText), "-dumpcr") OR match(lower(ScriptBlockText), "SEKURLSA::Pth") OR match(lower(ScriptBlockText), "kerberos::ptt") OR match(lower(ScriptBlockText), "kerberos::golden") ,5,0) | eval iex=if(match(ScriptBlockText, "(?i)iex|invoke-expression"),2,0) | eval webclient=if(match(lower(ScriptBlockText),"http") OR match(lower(ScriptBlockText),"web(client|request)") OR match(lower(ScriptBlockText),"socket") OR match(lower(ScriptBlockText),"download(file|string)") OR match(lower(ScriptBlockText),"bitstransfer") OR match(lower(ScriptBlockText),"internetexplorer.application") OR match(lower(ScriptBlockText),"xmlhttp"),5,0) | eval get = if(match(lower(ScriptBlockText),"get-"), "1", 0) | eval rundll32 = if(match(lower(ScriptBlockText),"rundll32"), "4", 0) | eval suspkeywrd=if(match(ScriptBlockText, "(?i)(bitstransfer|mimik|metasp|AssemblyBuilderAccess|Reflection\.Assembly|shellcode|injection|cnvert|shell\.application|start-process|Rc4ByteStream|System\.Security\.Cryptography|lsass\.exe|localadmin|LastLoggedOn|hijack|BackupPrivilege|ngrok|comsvcs|backdoor|brute.?force|Port.?Scan|Exfiltration|exploit|DisableRealtimeMonitoring|beacon)"),1,0) | eval syswow64 = if(match(lower(ScriptBlockText),"syswow64"), "3", 0) | eval httplocal = if(match(lower(ScriptBlockText),"http://127.0.0.1"), "4", 0) | eval reflection = if(match(lower(ScriptBlockText),"reflection"), "1", 0) | eval invokewmi=if(match(lower(ScriptBlockText), "(?i)(wmiobject|WMIMethod|RemoteWMI|PowerShellWmi|wmicommand)"),5,0) | eval downgrade=if(match(ScriptBlockText, "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(ScriptBlockText),"powershell -version"),3,0) | eval compressed=if(match(ScriptBlockText, "(?i)GZipStream|::Decompress|IO.Compression|write-zip|(expand|compress)-Archive"),5,0) | eval invokecmd = if(match(lower(ScriptBlockText),"invoke-command"), "4", 0) | addtotals fieldname=Score DoIt, enccom, suspcmdlet, suspkeywrd, compressed, downgrade, mimikatz, iex, empire, rundll32, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get | stats values(Score) by UserID, Computer, DoIt, enccom, compressed, downgrade, iex, mimikatz, rundll32, empire, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get, suspcmdlet, suspkeywrd | rename Computer as dest, UserID as user | `powershell_4104_hunting_filter`

[ESCU - PowerShell - Connect To Internet With Hidden Window - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies PowerShell commands utilizing the WindowStyle parameter to hide the window on the compromised endpoint. This combination of command-line options is suspicious because it is overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Removed in this version of the query is New-Object. The analytic identifies all variations of WindowStyle, as PowerShell allows the ability to shorten the parameter. For example w, win, windowsty and so forth. In addition, through our research it was identified that PowerShell will interpret different command switch types beyond the hyphen. We have added endash, emdash, horizontal bar, and forward slash.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies PowerShell commands utilizing the WindowStyle parameter to hide the window on the compromised endpoint. This combination of command-line options is suspicious because it is overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Removed in this version of the query is New-Object. The analytic identifies all variations of WindowStyle, as PowerShell allows the ability to shorten the parameter. For example w, win, windowsty and so forth. In addition, through our research it was identified that PowerShell will interpret different command switch types beyond the hyphen. We have added endash, emdash, horizontal bar, and forward slash.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell - Connect To Internet With Hidden Window - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AgentTesla", "HAFNIUM Group", "Hermetic Wiper", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Malicious PowerShell", "Data Destruction", "Log4Shell CVE-2021-44228"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell - Connect To Internet With Hidden Window - Rule
action.correlationsearch.annotations = {"analytic_story": ["AgentTesla", "HAFNIUM Group", "Hermetic Wiper", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Malicious PowerShell", "Data Destruction", "Log4Shell CVE-2021-44228"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-44228"], "impact": 90, "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/|||]w(in*d*o*w*s*t*y*l*e*)*\s+[^-]") | `powershell___connect_to_internet_with_hidden_window_filter`

[ESCU - Powershell COM Hijacking InprocServer32 Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell ScriptBlock Logging to identify a script that is attempting to modify or add a component object model to inprocserver32 path within the registry.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.015", "T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell ScriptBlock Logging to identify a script that is attempting to modify or add a component object model to inprocserver32 path within the registry.
action.escu.how_to_implement = The following analytic requires PowerShell operational logs to be imported. Modify the PowerShell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = False positives will be present if any scripts are adding to inprocserver32. Filter as needed.
action.escu.creation_date = 2022-09-26
action.escu.modification_date = 2022-09-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell COM Hijacking InprocServer32 Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A PowerShell script has been identified with InProcServer32 within the script code on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell COM Hijacking InprocServer32 Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1546.015", "T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell ScriptBlock Logging to identify a script that is attempting to modify or add a component object model to inprocserver32 path within the registry.
action.notable.param.rule_title = Powershell COM Hijacking InprocServer32 Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Software\\Classes\\CLSID\\*\\InProcServer32*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_com_hijacking_inprocserver32_modification_filter`

[ESCU - Powershell Creating Thread Mutex - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using the `mutex` function. This function is commonly seen in some obfuscated PowerShell scripts to make sure that only one instance of there process is running on a compromise machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027", "T1027.005", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using the `mutex` function. This function is commonly seen in some obfuscated PowerShell scripts to make sure that only one instance of there process is running on a compromise machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = powershell developer may used this function in their script for instance checking too.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Creating Thread Mutex - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains Thread Mutex in $ScriptBlockText$ with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Creating Thread Mutex - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1027", "T1027.005", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using the `mutex` function. This function is commonly seen in some obfuscated PowerShell scripts to make sure that only one instance of there process is running on a compromise machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.notable.param.rule_title = Powershell Creating Thread Mutex
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Threading.Mutex*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_creating_thread_mutex_filter`

[ESCU - Powershell Disable Security Monitoring - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search identifies a modification in registry to disable the windows defender real time behavior monitoring. This event or technique is commonly seen in RATs, bots, or Trojans to disable AV to evade detections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search identifies a modification in registry to disable the windows defender real time behavior monitoring. This event or technique is commonly seen in RATs, bots, or Trojans to disable AV to evade detections.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives. However, tune based on scripts that may perform this action.
action.escu.creation_date = 2022-07-15
action.escu.modification_date = 2022-07-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Disable Security Monitoring - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
action.risk = 1
action.risk.param._risk_message = Windows Defender Real-time Behavior Monitoring disabled on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Disable Security Monitoring - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search identifies a modification in registry to disable the windows defender real time behavior monitoring. This event or technique is commonly seen in RATs, bots, or Trojans to disable AV to evade detections.
action.notable.param.rule_title = Powershell Disable Security Monitoring
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="*set-mppreference*" AND Processes.process IN ("*disablerealtimemonitoring*","*disableioavprotection*","*disableintrusionpreventionsystem*","*disablescriptscanning*","*disableblockatfirstseen*","*DisableBehaviorMonitoring*","*drtm *","*dioavp *","*dscrptsc *","*dbaf *","*dbm *") by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_disable_security_monitoring_filter`

[ESCU - PowerShell Domain Enumeration - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies specific PowerShell modules typically used to enumerate an organizations domain or users. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies specific PowerShell modules typically used to enumerate an organizations domain or users. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible there will be false positives, filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Domain Enumeration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains domain enumeration command in $ScriptBlockText$ with EventCode $EventCode$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Domain Enumeration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies specific PowerShell modules typically used to enumerate an organizations domain or users. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = PowerShell Domain Enumeration
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN (*get-netdomaintrust*, *get-netforesttrust*, *get-addomain*, *get-adgroupmember*, *get-domainuser*) | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_domain_enumeration_filter`

[ESCU - PowerShell Enable PowerShell Remoting - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic utilizes PowerShell Script Block Logging (EventCode 4104) to identify the use of Enable-PSRemoting cmdlet. This cmdlet allows users to enable PowerShell remoting on a local or remote computer, which allows other computers to run commands on the target computer. The ability to remotely execute commands can be abused by attackers to take control of compromised systems and pivot to other systems on the network. By detecting the use of Enable-PSRemoting cmdlet via script block logging, this analytic can help organizations identify potential malicious activity related to attackers attempting to gain remote control of compromised systems.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic utilizes PowerShell Script Block Logging (EventCode 4104) to identify the use of Enable-PSRemoting cmdlet. This cmdlet allows users to enable PowerShell remoting on a local or remote computer, which allows other computers to run commands on the target computer. The ability to remotely execute commands can be abused by attackers to take control of compromised systems and pivot to other systems on the network. By detecting the use of Enable-PSRemoting cmdlet via script block logging, this analytic can help organizations identify potential malicious activity related to attackers attempting to gain remote control of compromised systems.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Note that false positives may occur due to the use of the Enable-PSRemoting cmdlet by legitimate users, such as system administrators. It is recommended to apply appropriate filters as needed to minimize the number of false positives.
action.escu.creation_date = 2023-03-22
action.escu.modification_date = 2023-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Enable PowerShell Remoting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = PowerShell was identified running a Invoke-PSremoting on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Enable PowerShell Remoting - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*Enable-PSRemoting*" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `powershell_enable_powershell_remoting_filter`

[ESCU - Powershell Enable SMB1Protocol Feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious enabling of smb1protocol through `powershell.exe`. This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027", "T1027.005"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious enabling of smb1protocol through `powershell.exe`. This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
action.escu.known_false_positives = network operator may enable or disable this windows feature.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Enable SMB1Protocol Feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Ransomware", "Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Powershell Enable SMB1Protocol Feature on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Enable SMB1Protocol Feature - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1027", "T1027.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious enabling of smb1protocol through `powershell.exe`. This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system.
action.notable.param.rule_title = Powershell Enable SMB1Protocol Feature
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Enable-WindowsOptionalFeature*" ScriptBlockText = "*SMB1Protocol*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_enable_smb1protocol_feature_filter`

[ESCU - Powershell Execute COM Object - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a COM CLSID execution through powershell. This technique was seen in several adversaries and malware like ransomware conti where it has a feature to execute command using COM Object. This technique may use by network operator at some cases but a good indicator if some application want to gain privilege escalation or bypass uac.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.015", "T1546", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a COM CLSID execution through powershell. This technique was seen in several adversaries and malware like ransomware conti where it has a feature to execute command using COM Object. This technique may use by network operator at some cases but a good indicator if some application want to gain privilege escalation or bypass uac.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = network operrator may use this command.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Execute COM Object - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Ransomware", "Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains COM CLSID command in $ScriptBlockText$ with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 5}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Execute COM Object - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 50, "impact": 10, "mitre_attack": ["T1546.015", "T1546", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a COM CLSID execution through powershell. This technique was seen in several adversaries and malware like ransomware conti where it has a feature to execute command using COM Object. This technique may use by network operator at some cases but a good indicator if some application want to gain privilege escalation or bypass uac.
action.notable.param.rule_title = Powershell Execute COM Object
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*CreateInstance([type]::GetTypeFromCLSID*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_execute_com_object_filter`

[ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `GetProcAddress` in the script block. This is not normal to be used by most PowerShell scripts and is typically unsafe/malicious. Many attack toolkits use GetProcAddress to obtain code execution. \
In use, `$var_gpa = $var_unsafe_native_methods.GetMethod(GetProcAddress` and later referenced/executed elsewhere. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1055", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `GetProcAddress` in the script block. This is not normal to be used by most PowerShell scripts and is typically unsafe/malicious. Many attack toolkits use GetProcAddress to obtain code execution. \
In use, `$var_gpa = $var_unsafe_native_methods.GetMethod(GetProcAddress` and later referenced/executed elsewhere. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Limited false positives. Filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains GetProcAddress API in $ScriptBlockText$ with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1059", "T1055", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `GetProcAddress` in the script block. This is not normal to be used by most PowerShell scripts and is typically unsafe/malicious. Many attack toolkits use GetProcAddress to obtain code execution. \
In use, `$var_gpa = $var_unsafe_native_methods.GetMethod(GetProcAddress` and later referenced/executed elsewhere. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Powershell Fileless Process Injection via GetProcAddress
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText=*getprocaddress* | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_fileless_process_injection_via_getprocaddress_filter`

[ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `FromBase64String` within the script block. A typical malicious instance will include additional code. \
Command example - `[Byte[]]$var_code = [System.Convert]::FromBase64String(38uqIyMjQ6rG....` \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1027", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `FromBase64String` within the script block. A typical malicious instance will include additional code. \
Command example - `[Byte[]]$var_code = [System.Convert]::FromBase64String(38uqIyMjQ6rG....` \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited. Filter as needed.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Hermetic Wiper", "Malicious PowerShell", "Winter Vivern", "AsyncRAT", "Data Destruction", "IcedID", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains base64 command in $ScriptBlockText$ with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Malicious PowerShell", "Winter Vivern", "AsyncRAT", "Data Destruction", "IcedID", "NjRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1059", "T1027", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `FromBase64String` within the script block. A typical malicious instance will include additional code. \
Command example - `[Byte[]]$var_code = [System.Convert]::FromBase64String(38uqIyMjQ6rG....` \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Powershell Fileless Script Contains Base64 Encoded Content
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*frombase64string*" OR ScriptBlockText = "*gnirtS46esaBmorF*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_fileless_script_contains_base64_encoded_content_filter`

[ESCU - PowerShell Get LocalGroup Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies the use of `get-localgroup` being used with PowerShell to identify local groups on the endpoint. During triage, review parallel processes and identify any further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies the use of `get-localgroup` being used with PowerShell to identify local groups on the endpoint. During triage, review parallel processes and identify any further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present. Tune as needed.
action.escu.creation_date = 2021-09-14
action.escu.modification_date = 2021-09-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Get LocalGroup Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Get LocalGroup Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) (Processes.process="*get-localgroup*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_filter`

[ESCU - Powershell Get LocalGroup Discovery with Script Block Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies PowerShell cmdlet - `get-localgroup` being ran. Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies PowerShell cmdlet - `get-localgroup` being ran. Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may be present. Tune as needed.
action.escu.creation_date = 2022-04-26
action.escu.modification_date = 2022-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Get LocalGroup Discovery with Script Block Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Get LocalGroup Discovery with Script Block Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*get-localgroup*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText  | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_with_script_block_logging_filter`

[ESCU - PowerShell Invoke CIMMethod CIMSession - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the use of the New-CIMSession cmdlet being created along with the Invoke-CIMMethod cmdlet being used within PowerShell. This particular behavior is similar to the usage of the Invoke-WMIMethod cmdlet, which is known for executing WMI commands on targets using NTLMv2 pass-the-hash authentication. The New-CIMSession cmdlet allows users to create a new CIM session object for a specified computer system, which can then be used to execute CIM operations remotely. Similarly, the Invoke-CIMMethod cmdlet is used to invoke a specified method on one or more CIM objects. Therefore, the combination of New-CIMSession and Invoke-CIMMethod cmdlets in PowerShell can potentially indicate malicious behavior, and this analytic can help detect such activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the use of the New-CIMSession cmdlet being created along with the Invoke-CIMMethod cmdlet being used within PowerShell. This particular behavior is similar to the usage of the Invoke-WMIMethod cmdlet, which is known for executing WMI commands on targets using NTLMv2 pass-the-hash authentication. The New-CIMSession cmdlet allows users to create a new CIM session object for a specified computer system, which can then be used to execute CIM operations remotely. Similarly, the Invoke-CIMMethod cmdlet is used to invoke a specified method on one or more CIM objects. Therefore, the combination of New-CIMSession and Invoke-CIMMethod cmdlets in PowerShell can potentially indicate malicious behavior, and this analytic can help detect such activity.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may be present based on third-party applications or administrators using CIM. It is recommended to apply appropriate filters as needed to minimize the number of false positives.
action.escu.creation_date = 2023-03-22
action.escu.modification_date = 2023-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Invoke CIMMethod CIMSession - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = PowerShell was identified running a Invoke-CIMMethod Invoke-CIMSession on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Invoke CIMMethod CIMSession - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*invoke-CIMMethod*", "*New-CimSession*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_invoke_cimmethod_cimsession_filter`

[ESCU - PowerShell Invoke WmiExec Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the usage of the Invoke-WMIExec utility within PowerShell Script Block Logging (EventCode 4104). The utility is used for executing WMI commands on targets using NTLMv2 pass-the-hash authentication.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the usage of the Invoke-WMIExec utility within PowerShell Script Block Logging (EventCode 4104). The utility is used for executing WMI commands on targets using NTLMv2 pass-the-hash authentication.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited as this analytic is designed to detect a specific utility. It is recommended to apply appropriate filters as needed to minimize the number of false positives.
action.escu.creation_date = 2023-03-22
action.escu.modification_date = 2023-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Invoke WmiExec Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = PowerShell was identified running a Invoke-WmiExec on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Invoke WmiExec Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the usage of the Invoke-WMIExec utility within PowerShell Script Block Logging (EventCode 4104). The utility is used for executing WMI commands on targets using NTLMv2 pass-the-hash authentication.
action.notable.param.rule_title = PowerShell Invoke WmiExec Usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*invoke-wmiexec*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_invoke_wmiexec_usage_filter`

[ESCU - Powershell Load Module in Meterpreter - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies "MSF.Powershell","MSF.Powershell.Meterpreter","MSF.Powershell.Meterpreter.Kiwi","MSF.Powershell.Meterpreter.Transport" being used. This behavior is related to when a Meterpreter session is started and the operator runs load_kiwi. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies "MSF.Powershell","MSF.Powershell.Meterpreter","MSF.Powershell.Meterpreter.Kiwi","MSF.Powershell.Meterpreter.Transport" being used. This behavior is related to when a Meterpreter session is started and the operator runs load_kiwi. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = False positives should be very limited as this is strict to MetaSploit behavior.
action.escu.creation_date = 2022-11-22
action.escu.modification_date = 2022-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Load Module in Meterpreter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["MetaSploit"]
action.risk = 1
action.risk.param._risk_message = PowerShell was identified running a script utilized by Meterpreter from MetaSploit on endpoint $Computer$ by user $user_id$.
action.risk.param._risk = [{"risk_object_field": "user_id", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Load Module in Meterpreter - Rule
action.correlationsearch.annotations = {"analytic_story": ["MetaSploit"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies "MSF.Powershell","MSF.Powershell.Meterpreter","MSF.Powershell.Meterpreter.Kiwi","MSF.Powershell.Meterpreter.Transport" being used. This behavior is related to when a Meterpreter session is started and the operator runs load_kiwi. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Powershell Load Module in Meterpreter
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*MSF.Powershell*","*MSF.Powershell.Meterpreter*","*MSF.Powershell.Meterpreter.Kiwi*","*MSF.Powershell.Meterpreter.Transport*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_load_module_in_meterpreter_filter`

[ESCU - PowerShell Loading DotNET into Memory via Reflection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies the use of PowerShell loading .net assembly via reflection. This is commonly found in malicious PowerShell usage, including Empire and Cobalt Strike. In addition, the `load(` value may be modifed by removing `(` and it will identify more events to review. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies the use of PowerShell loading .net assembly via reflection. This is commonly found in malicious PowerShell usage, including Empire and Cobalt Strike. In addition, the `load(` value may be modifed by removing `(` and it will identify more events to review. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited as day to day scripts do not use this method.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Loading DotNET into Memory via Reflection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Winter Vivern", "AgentTesla", "AsyncRAT", "Hermetic Wiper", "Malicious PowerShell", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains reflective class assembly command in $ScriptBlockText$ to load .net code in memory with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Loading DotNET into Memory via Reflection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern", "AgentTesla", "AsyncRAT", "Hermetic Wiper", "Malicious PowerShell", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies the use of PowerShell loading .net assembly via reflection. This is commonly found in malicious PowerShell usage, including Empire and Cobalt Strike. In addition, the `load(` value may be modifed by removing `(` and it will identify more events to review. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = PowerShell Loading DotNET into Memory via Reflection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*", "*reflection.assembly*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_loading_dotnet_into_memory_via_reflection_filter`

[ESCU - Powershell Processing Stream Of Data - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is processing compressed stream data. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are stream flattened and will be deflated durnig execution. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is processing compressed stream data. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are stream flattened and will be deflated durnig execution. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = powershell may used this function to process compressed data.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Processing Stream Of Data - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "AsyncRAT", "Hermetic Wiper", "Data Destruction", "IcedID"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains stream command in $ScriptBlockText$ commonly for processing compressed or to decompressed binary file with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Processing Stream Of Data - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "AsyncRAT", "Hermetic Wiper", "Data Destruction", "IcedID"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is processing compressed stream data. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are stream flattened and will be deflated durnig execution. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.notable.param.rule_title = Powershell Processing Stream Of Data
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*IO.Compression.*" OR ScriptBlockText = "*IO.StreamReader*" OR ScriptBlockText = "*]::Decompress*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_processing_stream_of_data_filter`

[ESCU - Powershell Remote Thread To Known Windows Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is designed to detect suspicious powershell process that tries to inject code and to known/critical windows process and execute it using CreateRemoteThread. This technique is seen in several malware like trickbot and offensive tooling like cobaltstrike where it load a shellcode to svchost.exe to execute reverse shell to c2 and download another payload
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = this search is designed to detect suspicious powershell process that tries to inject code and to known/critical windows process and execute it using CreateRemoteThread. This technique is seen in several malware like trickbot and offensive tooling like cobaltstrike where it load a shellcode to svchost.exe to execute reverse shell to c2 and download another payload
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, Create Remote thread from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of create remote thread may be used.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Remote Thread To Known Windows Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Trickbot"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell process $process_name$ that tries to create a remote thread on target process $TargetImage$ with eventcode $EventCode$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Remote Thread To Known Windows Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is designed to detect suspicious powershell process that tries to inject code and to known/critical windows process and execute it using CreateRemoteThread. This technique is seen in several malware like trickbot and offensive tooling like cobaltstrike where it load a shellcode to svchost.exe to execute reverse shell to c2 and download another payload
action.notable.param.rule_title = Powershell Remote Thread To Known Windows Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode = 8 parent_process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") | stats  min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress dest EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_thread_to_known_windows_process_filter`

[ESCU - Powershell Remove Windows Defender Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify a suspicious PowerShell command used to delete the Windows Defender folder. This technique was seen used by the WhisperGate malware campaign where it used Nirsofts advancedrun.exe to gain administrative privileges to then execute a PowerShell command to delete the Windows Defender folder. This is a good indicator the offending process is trying corrupt a Windows Defender installation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will identify a suspicious PowerShell command used to delete the Windows Defender folder. This technique was seen used by the WhisperGate malware campaign where it used Nirsofts advancedrun.exe to gain administrative privileges to then execute a PowerShell command to delete the Windows Defender folder. This is a good indicator the offending process is trying corrupt a Windows Defender installation.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Remove Windows Defender Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Data Destruction", "WhisperGate"]
action.risk = 1
action.risk.param._risk_message = suspicious powershell script $ScriptBlockText$ was executed on the $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Remove Windows Defender Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify a suspicious PowerShell command used to delete the Windows Defender folder. This technique was seen used by the WhisperGate malware campaign where it used Nirsofts advancedrun.exe to gain administrative privileges to then execute a PowerShell command to delete the Windows Defender folder. This is a good indicator the offending process is trying corrupt a Windows Defender installation.
action.notable.param.rule_title = Powershell Remove Windows Defender Directory
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*rmdir *" AND ScriptBlockText = "*\\Microsoft\\Windows Defender*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remove_windows_defender_directory_filter` 

[ESCU - PowerShell Script Block With URL Chain - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious PowerShell script execution via EventCode 4104 that contains multiple URLs within a function or array. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are attempting to download 2nd stage payloads. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1105"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious PowerShell script execution via EventCode 4104 that contains multiple URLs within a function or array. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are attempting to download 2nd stage payloads. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Unknown, possible custom scripting.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Script Block With URL Chain - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script used by $user$ on host $dest$ contains $url_count$ URLs in an array, this is commonly used for malware.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"threat_object_field": "file_name", "threat_object_type": "file name"}, {"threat_object_field": "url", "threat_object_type": "url string"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Script Block With URL Chain - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1059.001", "T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious PowerShell script execution via EventCode 4104 that contains multiple URLs within a function or array. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are attempting to download 2nd stage payloads. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.notable.param.rule_title = PowerShell Script Block With URL Chain
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*http:*","*https:*") | regex ScriptBlockText="(\"?(https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))\"?(?:,|\))?){2,}" | rex max_match=20 field=ScriptBlockText "(?<url>https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))" | eval Path = case(isnotnull(Path),Path,true(),"unknown") | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Path) as file_name values(UserID) as user values(url) as url dc(url) as url_count by ActivityID, Computer, EventCode | rename Computer as dest, EventCode as signature_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_script_block_with_url_chain_filter`

[ESCU - PowerShell Start-BitsTransfer - Rule]
action.escu = 0
action.escu.enabled = 1
description = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation?
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1197"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation?
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments.
action.escu.creation_date = 2021-03-29
action.escu.modification_date = 2021-03-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Start-BitsTransfer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BITS Jobs"]
action.risk = 1
action.risk.param._risk_message = A suspicious process $process_name$ with commandline $process$ that are related to bittransfer functionality in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Start-BitsTransfer - Rule
action.correlationsearch.annotations = {"analytic_story": ["BITS Jobs"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1197"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation?
action.notable.param.rule_title = PowerShell Start-BitsTransfer
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*start-bitstransfer* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_start_bitstransfer_filter`

[ESCU - PowerShell Start or Stop Service - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the use of PowerShell's Start-Service or Stop-Service cmdlets on an endpoint. These cmdlets allow users to start or stop a specified Windows service. The ability to manipulate services can be leveraged by attackers to disable or stop critical services, which can cause system instability or disrupt business operations. By detecting the use of Start-Service or Stop-Service cmdlets via PowerShell, this analytic can help organizations identify potential malicious activity related to attackers attempting to manipulate services on compromised systems. However, note that this behavior may be noisy, as these cmdlets are commonly used by system administrators or other legitimate users to manage services. Therefore, it is recommended not to enable this analytic as a direct notable or TTP. Instead, it should be used as part of a broader set of security controls to detect and investigate potential threats.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the use of PowerShell's Start-Service or Stop-Service cmdlets on an endpoint. These cmdlets allow users to start or stop a specified Windows service. The ability to manipulate services can be leveraged by attackers to disable or stop critical services, which can cause system instability or disrupt business operations. By detecting the use of Start-Service or Stop-Service cmdlets via PowerShell, this analytic can help organizations identify potential malicious activity related to attackers attempting to manipulate services on compromised systems. However, note that this behavior may be noisy, as these cmdlets are commonly used by system administrators or other legitimate users to manage services. Therefore, it is recommended not to enable this analytic as a direct notable or TTP. Instead, it should be used as part of a broader set of security controls to detect and investigate potential threats.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = This behavior may be noisy, as these cmdlets are commonly used by system administrators or other legitimate users to manage services. Therefore, it is recommended not to enable this analytic as a direct notable or TTP. Instead, it should be used as part of a broader set of security controls to detect and investigate potential threats.
action.escu.creation_date = 2023-03-24
action.escu.modification_date = 2023-03-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Start or Stop Service - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = PowerShell was identified attempting to start or stop a service on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 10}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell Start or Stop Service - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 20, "impact": 50, "mitre_attack": ["T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*start-service*", "*stop-service*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_start_or_stop_service_filter`

[ESCU - Powershell Using memory As Backing Store - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using memory stream as new object backstore. The malicious PowerShell script will contain stream flate data and will be decompressed in memory to run or drop the actual payload. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using memory stream as new object backstore. The malicious PowerShell script will contain stream flate data and will be decompressed in memory to run or drop the actual payload. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = powershell may used this function to store out object into memory.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Using memory As Backing Store - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction", "IcedID"]
action.risk = 1
action.risk.param._risk_message = A PowerShell script contains memorystream command in $ScriptBlockText$ as new object backstore with EventCode $EventCode$ on host $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Using memory As Backing Store - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction", "IcedID"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using memory stream as new object backstore. The malicious PowerShell script will contain stream flate data and will be decompressed in memory to run or drop the actual payload. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.notable.param.rule_title = Powershell Using memory As Backing Store
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = *New-Object* ScriptBlockText = *IO.MemoryStream* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_using_memory_as_backing_store_filter`

[ESCU - PowerShell WebRequest Using Memory Stream - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a common fileless malware staging technique of using .NET classes to directly download a URL payload into memory. The analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1105", "T1027.011"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies a common fileless malware staging technique of using .NET classes to directly download a URL payload into memory. The analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution.
action.escu.how_to_implement = The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Unknown, possible custom scripting.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell WebRequest Using Memory Stream - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = Powershell webrequest to memory stream behavior. Possible fileless malware staging on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PowerShell WebRequest Using Memory Stream - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1059.001", "T1105", "T1027.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a common fileless malware staging technique of using .NET classes to directly download a URL payload into memory. The analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution.
action.notable.param.rule_title = PowerShell WebRequest Using Memory Stream
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104  ScriptBlockText IN ("*system.net.webclient*","*system.net.webrequest*") AND ScriptBlockText="*IO.MemoryStream*" | eval Path = case(isnotnull(Path),Path,true(),"unknown") | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Path) as file_name values(UserID) as user by ActivityID, Computer, EventCode | rename Computer as dest, EventCode as signature_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_webrequest_using_memory_stream_filter`

[ESCU - Powershell Windows Defender Exclusion Commands - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect a suspicious process commandline related to windows defender exclusion feature. This command is abused by adversaries, malware author and red teams to bypassed Windows Defender Anti-Virus product by excluding  folder path, file path, process, extensions and etc. from its real time or schedule scan to execute their malicious code. This is a good indicator for defense evasion and to look further for events after this behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will detect a suspicious process commandline related to windows defender exclusion feature. This command is abused by adversaries, malware author and red teams to bypassed Windows Defender Anti-Virus product by excluding  folder path, file path, process, extensions and etc. from its real time or schedule scan to execute their malicious code. This is a good indicator for defense evasion and to look further for events after this behavior.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = admin or user may choose to use this windows features.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Powershell Windows Defender Exclusion Commands - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["CISA AA22-320A", "AgentTesla", "Remcos", "Windows Defense Evasion Tactics", "Data Destruction", "WhisperGate", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = exclusion command $Message$ executed on $ComputerName$
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Powershell Windows Defender Exclusion Commands - Rule
action.correlationsearch.annotations = {"analytic_story": ["CISA AA22-320A", "AgentTesla", "Remcos", "Windows Defense Evasion Tactics", "Data Destruction", "WhisperGate", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will detect a suspicious process commandline related to windows defender exclusion feature. This command is abused by adversaries, malware author and red teams to bypassed Windows Defender Anti-Virus product by excluding  folder path, file path, process, extensions and etc. from its real time or schedule scan to execute their malicious code. This is a good indicator for defense evasion and to look further for events after this behavior.
action.notable.param.rule_title = Powershell Windows Defender Exclusion Commands
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (Message = "*Add-MpPreference *" OR Message = "*Set-MpPreference *") AND Message = "*-exclusion*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_windows_defender_exclusion_commands_filter`

[ESCU - Prevent Automatic Repair Mode using Bcdedit - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious bcdedit.exe execution to ignore all failures. This technique was used by ransomware to prevent the compromise machine automatically boot in repair mode.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious bcdedit.exe execution to ignore all failures. This technique was used by ransomware to prevent the compromise machine automatically boot in repair mode.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may modify the boot configuration ignore failure during testing and debugging.
action.escu.creation_date = 2021-06-10
action.escu.modification_date = 2021-06-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Prevent Automatic Repair Mode using Bcdedit - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Chaos Ransomware"]
action.risk = 1
action.risk.param._risk_message = A suspicious process $process_name$ with process id $process_id$ contains commandline $process$ to ignore all bcdedit execution failure in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Prevent Automatic Repair Mode using Bcdedit - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Chaos Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious bcdedit.exe execution to ignore all failures. This technique was used by ransomware to prevent the compromise machine automatically boot in repair mode.
action.notable.param.rule_title = Prevent Automatic Repair Mode using Bcdedit
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe" Processes.process = "*bootstatuspolicy*"  Processes.process = "*ignoreallfailures*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `prevent_automatic_repair_mode_using_bcdedit_filter`

[ESCU - Print Processor Registry Autostart - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification or new registry entry regarding print processor. This registry is known to be abuse by turla or other APT to gain persistence and privilege escalation to the compromised machine. This is done by adding the malicious dll payload on the new created key in this registry that will be executed as it restarted the spoolsv.exe process and services.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification or new registry entry regarding print processor. This registry is known to be abuse by turla or other APT to gain persistence and privilege escalation to the compromised machine. This is done by adding the malicious dll payload on the new created key in this registry that will be executed as it restarted the spoolsv.exe process and services.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = possible new printer installation may add driver component on this registry.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Print Processor Registry Autostart - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Windows Privilege Escalation", "Hermetic Wiper", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $Registry.registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Print Processor Registry Autostart - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Windows Privilege Escalation", "Hermetic Wiper", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious modification or new registry entry regarding print processor. This registry is known to be abuse by turla or other APT to gain persistence and privilege escalation to the compromised machine. This is done by adding the malicious dll payload on the new created key in this registry that will be executed as it restarted the spoolsv.exe process and services.
action.notable.param.rule_title = Print Processor Registry Autostart
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\Control\\Print\\Environments\\Windows x64\\Print Processors*" by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `print_processor_registry_autostart_filter`

[ESCU - Print Spooler Adding A Printer Driver - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began.
action.escu.how_to_implement = You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems.
action.escu.known_false_positives = Unknown. This may require filtering.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Print Spooler Adding A Printer Driver - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = Suspicious print driver was loaded on endpoint $ComputerName$.
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Print Spooler Adding A Printer Driver - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527", "CVE-2021-1675"], "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began.
action.notable.param.rule_title = Print Spooler Adding A Printer Driver
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `printservice` EventCode=316 category = "Adding a printer driver" Message = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" | stats  count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_adding_a_printer_driver_filter`

[ESCU - Print Spooler Failed to Load a Plug-in - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \
The analytic is based on file path and failure to load the plug-in. \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \
The analytic is based on file path and failure to load the plug-in. \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.how_to_implement = You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems.
action.escu.known_false_positives = False positives are unknown and filtering may be required.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Print Spooler Failed to Load a Plug-in - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = Suspicious printer spooler errors have occured on endpoint $ComputerName$ with EventCode $EventCode$.
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Print Spooler Failed to Load a Plug-in - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527", "CVE-2021-1675"], "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \
The analytic is based on file path and failure to load the plug-in. \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.notable.param.rule_title = Print Spooler Failed to Load a Plug-in
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_failed_to_load_a_plug_in_filter`

[ESCU - Process Creating LNK file in Suspicious Location - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1566", "T1566.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools.
action.escu.how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
action.escu.known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories.
action.escu.creation_date = 2021-08-26
action.escu.modification_date = 2021-08-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Process Creating LNK file in Suspicious Location - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Qakbot", "IcedID"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ that launching .lnk file in $file_path$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Process Creating LNK file in Suspicious Location - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Qakbot", "IcedID"], "cis20": ["CIS 13"], "confidence": 90, "impact": 70, "mitre_attack": ["T1566", "T1566.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools.
action.notable.param.rule_title = Process Creating LNK file in Suspicious Location
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND (Filesystem.file_path="C:\\User\\*" OR Filesystem.file_path="*\\Temp\\*") by _time span=1h Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_guid as lnk_guid | join lnk_guid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_guid Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_guid as lnk_guid | fields _time lnk_guid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_guid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter`

[ESCU - Process Deleting Its Process File Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Process Deleting Its Process File Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Clop Ransomware", "Data Destruction", "WhisperGate", "Remcos"]
action.risk = 1
action.risk.param._risk_message = A process $Image$ tries to delete its process path in commandline $CommandLine$ as part of defense evasion in host $dest$ by user $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Process Deleting Its Process File Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware", "Data Destruction", "WhisperGate", "Remcos"], "cis20": ["CIS 10"], "confidence": 100, "impact": 60, "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect.
action.notable.param.rule_title = Process Deleting Its Process File Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=1 CommandLine = "* /c *" CommandLine = "* del*" Image = "*\\cmd.exe" | eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by dest user ParentImage ParentCommandLine Image CommandLine EventCode ProcessID result | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter`

[ESCU - Process Execution via WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes.
action.escu.creation_date = 2020-03-16
action.escu.modification_date = 2020-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Process Execution via WMI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = A remote instance execution of wmic.exe that will spawn $parent_process_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Process Execution via WMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary.
action.notable.param.rule_title = Process Execution via WMI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=WmiPrvSE.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_execution_via_wmi_filter` 

[ESCU - Process Kill Base On File Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of `wmic.exe` using `delete` to remove a executable path. This is typically ran via a batch file during beginning stages of an adversary setting up for mining on an endpoint.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of `wmic.exe` using `delete` to remove a executable path. This is typically ran via a batch file during beginning stages of an adversary setting up for mining on an endpoint.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown.
action.escu.creation_date = 2021-05-04
action.escu.modification_date = 2021-05-04
action.escu.confidence = high
action.escu.full_search_name = ESCU - Process Kill Base On File Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ attempt to kill process by its file path using commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Process Kill Base On File Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of `wmic.exe` using `delete` to remove a executable path. This is typically ran via a batch file during beginning stages of an adversary setting up for mining on an endpoint.
action.notable.param.rule_title = Process Kill Base On File Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_wmic` AND Processes.process="*process*" AND Processes.process="*executablepath*" AND Processes.process="*delete*" by  Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_kill_base_on_file_path_filter`

[ESCU - Process Writing DynamicWrapperX - Rule]
action.escu = 0
action.escu.enabled = 1
description = DynamicWrapperX is an ActiveX component that can be used in a script to call Windows API functions, but it requires the dynwrapx.dll to be installed and registered. With that, a binary writing dynwrapx.dll to disk and registering it into the registry is highly suspect. Why is it needed? In most malicious instances, it will be written to disk at a non-standard location. During triage, review parallel processes and pivot on the process_guid. Review the registry for any suspicious modifications meant to load dynwrapx.dll. Identify any suspicious module loads of dynwrapx.dll. This will identify the process that will invoke vbs/wscript/cscript.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1559.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = DynamicWrapperX is an ActiveX component that can be used in a script to call Windows API functions, but it requires the dynwrapx.dll to be installed and registered. With that, a binary writing dynwrapx.dll to disk and registering it into the registry is highly suspect. Why is it needed? In most malicious instances, it will be written to disk at a non-standard location. During triage, review parallel processes and pivot on the process_guid. Review the registry for any suspicious modifications meant to load dynwrapx.dll. Identify any suspicious module loads of dynwrapx.dll. This will identify the process that will invoke vbs/wscript/cscript.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives should be limited, however it is possible to filter by Processes.process_name and specific processes (ex. wscript.exe). Filter as needed. This may need modification based on EDR telemetry and how it brings in registry data. For example, removal of (Default).
action.escu.creation_date = 2021-10-05
action.escu.modification_date = 2021-10-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Process Writing DynamicWrapperX - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Process Writing DynamicWrapperX - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1059", "T1559.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="dynwrapx.dll" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.user | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time) as lastTime by dest process_name process_guid file_name file_path file_create_time user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_writing_dynamicwrapperx_filter`

[ESCU - Processes launching netsh - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Processes launching netsh - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Netsh Abuse", "Disabling Security Tools", "DHS Report TA18-074A", "Azorult", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ has launched netsh with command-line $process$ on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 14}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 14}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Processes launching netsh - Rule
action.correlationsearch.annotations = {"analytic_story": ["Netsh Abuse", "Disabling Security Tools", "DHS Report TA18-074A", "Azorult", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 20, "mitre_attack": ["T1562.004", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` by Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter`

[ESCU - Processes Tapping Keyboard Events - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input
action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model.
action.escu.known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment.
action.escu.creation_date = 2019-01-25
action.escu.modification_date = 2019-01-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Processes Tapping Keyboard Events - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["ColdRoot MacOS RAT"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Processes Tapping Keyboard Events - Rule
action.correlationsearch.annotations = {"analytic_story": ["ColdRoot MacOS RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input
action.notable.param.rule_title = Processes Tapping Keyboard Events
action.notable.param.security_domain = threat
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`

[ESCU - Randomly Generated Scheduled Task Name - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic leverages Event ID 4698, `A scheduled task was created`, to identify the creation of a Scheduled Task with a suspicious, high entropy, Task Name. To achieve this, this analytic also leverages the `ut_shannon` function from the URL ToolBox Splunk application. Red teams and adversaries alike may abuse the Task Scheduler to create and start a remote Scheduled Task and obtain remote code execution. To achieve this goal, tools like Impacket or Crapmapexec, typically create a Scheduled Task with a random task name on the victim host. This hunting analytic may help defenders identify Scheduled Tasks created as part of a lateral movement attack. The entropy threshold `ut_shannon > 3` should be customized by users. The Command field can be used to determine if the task has malicious intent or not.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic leverages Event ID 4698, `A scheduled task was created`, to identify the creation of a Scheduled Task with a suspicious, high entropy, Task Name. To achieve this, this analytic also leverages the `ut_shannon` function from the URL ToolBox Splunk application. Red teams and adversaries alike may abuse the Task Scheduler to create and start a remote Scheduled Task and obtain remote code execution. To achieve this goal, tools like Impacket or Crapmapexec, typically create a Scheduled Task with a random task name on the victim host. This hunting analytic may help defenders identify Scheduled Tasks created as part of a lateral movement attack. The entropy threshold `ut_shannon > 3` should be customized by users. The Command field can be used to determine if the task has malicious intent or not.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well as the URL ToolBox application are also required.
action.escu.known_false_positives = Legitimate applications may use random Scheduled Task names.
action.escu.creation_date = 2021-11-29
action.escu.modification_date = 2021-11-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Randomly Generated Scheduled Task Name - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "CISA AA22-257A", "Scheduled Tasks"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Randomly Generated Scheduled Task Name - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "CISA AA22-257A", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4698 | xmlkv Message | lookup ut_shannon_lookup word as Task_Name | where ut_shannon > 3 | table  _time, dest, Task_Name, ut_shannon, Command, Author, Enabled, Hidden | `randomly_generated_scheduled_task_name_filter`

[ESCU - Randomly Generated Windows Service Name - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic leverages Event ID 7045, `A new service was installed in the system`, to identify the installation of a Windows Service with a suspicious, high entropy, Service Name. To achieve this, this analytic also leverages the `ut_shannon` function from the URL ToolBox Splunk application. Red teams and adversaries alike may abuse the Service Control Manager to create and start a remote Windows Service and obtain remote code execution. To achieve this goal, some tools like Metasploit, Cobalt Strike and Impacket, typically create a Windows Service with a random service name on the victim host. This hunting analytic may help defenders identify Windows Services installed as part of a lateral movement attack. The entropy threshold `ut_shannon > 3` should be customized by users. The Service_File_Name field can be used to determine if the Windows Service has malicious intent or not.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic leverages Event ID 7045, `A new service was installed in the system`, to identify the installation of a Windows Service with a suspicious, high entropy, Service Name. To achieve this, this analytic also leverages the `ut_shannon` function from the URL ToolBox Splunk application. Red teams and adversaries alike may abuse the Service Control Manager to create and start a remote Windows Service and obtain remote code execution. To achieve this goal, some tools like Metasploit, Cobalt Strike and Impacket, typically create a Windows Service with a random service name on the victim host. This hunting analytic may help defenders identify Windows Services installed as part of a lateral movement attack. The entropy threshold `ut_shannon > 3` should be customized by users. The Service_File_Name field can be used to determine if the Windows Service has malicious intent or not.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. The Windows TA as well as the URL ToolBox application are also required.
action.escu.known_false_positives = Legitimate applications may use random Windows Service names.
action.escu.creation_date = 2021-11-29
action.escu.modification_date = 2021-11-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Randomly Generated Windows Service Name - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Lateral Movement"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Randomly Generated Windows Service Name - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_system` EventCode=7045 | lookup ut_shannon_lookup word as Service_Name | where ut_shannon > 3 | table EventCode ComputerName Service_Name ut_shannon Service_Start_Type Service_Type Service_File_Name | `randomly_generated_windows_service_name_filter` 

[ESCU - Ransomware Notes bulk creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry or the ransomware note filename is not in your ransomware lookup table list for monitoring.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry or the ransomware note filename is not in your ransomware lookup table list for monitoring.
action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-03-12
action.escu.modification_date = 2021-03-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ransomware Notes bulk creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Clop Ransomware", "DarkSide Ransomware", "BlackMatter Ransomware", "Chaos Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = A high frequency file creation of $file_name$ in different file path in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ransomware Notes bulk creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware", "DarkSide Ransomware", "BlackMatter Ransomware", "Chaos Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") |bin _time span=10s | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name | rename Computer as dest | where unique_readme_path_count >= 15 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ransomware_notes_bulk_creation_filter`

[ESCU - Recon AVProduct Through Pwh or WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 performing checks to identify anti-virus products installed on the endpoint. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1592"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 performing checks to identify anti-virus products installed on the endpoint. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = network administrator may used this command for checking purposes
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Recon AVProduct Through Pwh or WMI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Qakbot", "Windows Post-Exploitation", "Hermetic Wiper", "Ransomware", "Prestige Ransomware", "Malicious PowerShell", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains AV recon command in $ScriptBlockText$ with EventCode $EventCode$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Recon AVProduct Through Pwh or WMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot", "Windows Post-Exploitation", "Hermetic Wiper", "Ransomware", "Prestige Ransomware", "Malicious PowerShell", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1592"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 performing checks to identify anti-virus products installed on the endpoint. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.notable.param.rule_title = Recon AVProduct Through Pwh or WMI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*SELECT*" OR ScriptBlockText = "*WMIC*") AND (ScriptBlockText = "*AntiVirusProduct*" OR ScriptBlockText = "*AntiSpywareProduct*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_avproduct_through_pwh_or_wmi_filter`

[ESCU - Recon Using WMI Class - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell via EventCode 4104, where WMI is performing an event query looking for running processes or running services. This technique is commonly found where the adversary will identify services and system information on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1592", "T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell via EventCode 4104, where WMI is performing an event query looking for running processes or running services. This technique is commonly found where the adversary will identify services and system information on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = network administrator may used this command for checking purposes
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Recon Using WMI Class - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["AsyncRAT", "Qakbot", "Industroyer2", "Hermetic Wiper", "LockBit Ransomware", "Malicious PowerShell", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains host recon command in $ScriptBlockText$ with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Recon Using WMI Class - Rule
action.correlationsearch.annotations = {"analytic_story": ["AsyncRAT", "Qakbot", "Industroyer2", "Hermetic Wiper", "LockBit Ransomware", "Malicious PowerShell", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 75, "mitre_attack": ["T1592", "T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText= "*SELECT*" OR ScriptBlockText= "*Get-WmiObject*") AND (ScriptBlockText= "*Win32_Bios*" OR ScriptBlockText= "*Win32_OperatingSystem*" OR ScriptBlockText= "*Win32_Processor*" OR ScriptBlockText= "*Win32_ComputerSystem*" OR ScriptBlockText= "*Win32_PnPEntity*" OR ScriptBlockText= "*Win32_ShadowCopy*" OR ScriptBlockText= "*Win32_DiskDrive*" OR ScriptBlockText= "*Win32_PhysicalMemory*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_using_wmi_class_filter`

[ESCU - Recursive Delete of Directory In Batch CMD - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious commandline designed to delete files or directory recursive using batch command. This technique was seen in ransomware (reddot) where it it tries to delete the files in recycle bin to impaire user from recovering deleted files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070.004", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious commandline designed to delete files or directory recursive using batch command. This technique was seen in ransomware (reddot) where it it tries to delete the files in recycle bin to impaire user from recovering deleted files.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network operator may use this batch command to delete recursively a directory or files within directory
action.escu.creation_date = 2022-11-12
action.escu.modification_date = 2022-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Recursive Delete of Directory In Batch CMD - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = Recursive Delete of Directory In Batch CMD by $user$ on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Recursive Delete of Directory In Batch CMD - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1070.004", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious commandline designed to delete files or directory recursive using batch command. This technique was seen in ransomware (reddot) where it it tries to delete the files in recycle bin to impaire user from recovering deleted files.
action.notable.param.rule_title = Recursive Delete of Directory In Batch CMD
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*/c*  Processes.process="* rd *"  Processes.process="*/s*" Processes.process="*/q*" by Processes.user Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recursive_delete_of_directory_in_batch_cmd_filter`

[ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.011", "T1574"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate.
action.escu.creation_date = 2020-11-26
action.escu.modification_date = 2020-11-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Service Abuse", "Windows Persistence Techniques", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = A reg.exe process $process_name$ with commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Service Abuse", "Windows Persistence Techniques", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 60, "impact": 75, "mitre_attack": ["T1574.011", "T1574"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
action.notable.param.rule_title = Reg exe Manipulating Windows Services Registry Keys
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter`

[ESCU - Registry Keys for Creating SHIM Databases - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = A registry activity in $registry_path$ related to shim modication in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Registry Keys for Creating SHIM Databases - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.
action.notable.param.rule_title = Registry Keys for Creating SHIM Databases
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB*) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_for_creating_shim_databases_filter`

[ESCU - Registry Keys Used For Persistence - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for modifications or alterations made to registry keys that have the potential to initiate the launch of an application or service during system startup. By monitoring and detecting modifications in these registry keys, we can identify suspicious or unauthorized changes that could be indicative of malicious activity. This proactive approach helps in safeguarding the system's integrity and security by promptly identifying and mitigating potential threats that aim to gain persistence or execute malicious actions during the startup process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.001", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for modifications or alterations made to registry keys that have the potential to initiate the launch of an application or service during system startup. By monitoring and detecting modifications in these registry keys, we can identify suspicious or unauthorized changes that could be indicative of malicious activity. This proactive approach helps in safeguarding the system's integrity and security by promptly identifying and mitigating potential threats that aim to gain persistence or execute malicious actions during the startup process.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task.
action.escu.creation_date = 2023-06-06
action.escu.modification_date = 2023-06-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Registry Keys Used For Persistence - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Suspicious MSHTA Activity", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Windows Persistence Techniques", "Emotet Malware  DHS Report TA18-201A ", "IcedID", "Remcos", "Windows Registry Abuse", "Azorult", "Qakbot", "Chaos Ransomware", "AsyncRAT", "Amadey", "Sneaky Active Directory Persistence Tricks", "BlackByte Ransomware", "Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = A registry activity in $registry_path$ related to persistence in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 76}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 76}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Registry Keys Used For Persistence - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Suspicious MSHTA Activity", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Windows Persistence Techniques", "Emotet Malware  DHS Report TA18-201A ", "IcedID", "Remcos", "Windows Registry Abuse", "Azorult", "Qakbot", "Chaos Ransomware", "AsyncRAT", "Amadey", "Sneaky Active Directory Persistence Tricks", "BlackByte Ransomware", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 95, "impact": 80, "mitre_attack": ["T1547.001", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for modifications or alterations made to registry keys that have the potential to initiate the launch of an application or service during system startup. By monitoring and detecting modifications in these registry keys, we can identify suspicious or unauthorized changes that could be indicative of malicious activity. This proactive approach helps in safeguarding the system's integrity and security by promptly identifying and mitigating potential threats that aim to gain persistence or execute malicious actions during the startup process.
action.notable.param.rule_title = Registry Keys Used For Persistence
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\User Shell Folders\\*" OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\*" OR Registry.registry_path=*\\currentversion\\run* OR Registry.registry_path=*\\currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Notify* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*\\currentversion\\policies\\explorer\\run* OR Registry.registry_path=*\\currentversion\\runservices* OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\* OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\Common Startup" OR Registry.registry_path= *\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SharedTaskScheduler OR Registry.registry_path= *\\Classes\\htmlfile\\shell\\open\\command OR (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" AND Registry.registry_key_name=Debugger) OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa" AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa\\OSConfig" AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\Microsoft\\Windows NT\\CurrentVersion\\SilentProcessExit\\*") OR (Registry.registry_path="*currentVersion\\Windows" AND Registry.registry_key_name="Load") OR (Registry.registry_path="*\\CurrentVersion" AND Registry.registry_key_name="Svchost") OR (Registry.registry_path="*\\CurrentControlSet\Control\Session Manager"AND Registry.registry_key_name="BootExecute") OR (Registry.registry_path="*\\Software\\Run" AND Registry.registry_key_name="auto_update")) by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_used_for_persistence_filter`

[ESCU - Registry Keys Used For Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.012", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Registry Keys Used For Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Cloud Federated Credential Abuse", "Hermetic Wiper", "Windows Privilege Escalation", "Windows Registry Abuse", "Data Destruction", "Suspicious Windows Registry Activities"]
action.risk = 1
action.risk.param._risk_message = A registry activity in $registry_path$ related to privilege escalation in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 76}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 76}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Registry Keys Used For Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Federated Credential Abuse", "Hermetic Wiper", "Windows Privilege Escalation", "Windows Registry Abuse", "Data Destruction", "Suspicious Windows Registry Activities"], "cis20": ["CIS 10"], "confidence": 95, "impact": 80, "mitre_attack": ["T1546.012", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries.
action.notable.param.rule_title = Registry Keys Used For Privilege Escalation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_value_name=GlobalFlag OR Registry.registry_value_name=Debugger)) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_used_for_privilege_escalation_filter`

[ESCU - Regsvr32 Silent and Install Param Dll Loading - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a loading of dll using regsvr32 application with silent parameter and dllinstall execution. This technique was seen in several RAT malware similar to remcos, njrat and adversaries to load their malicious DLL on the compromised machine. This TTP may executed by normal 3rd party application so it is better to pivot by the parent process, parent command-line and command-line of the file that execute this regsvr32.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a loading of dll using regsvr32 application with silent parameter and dllinstall execution. This technique was seen in several RAT malware similar to remcos, njrat and adversaries to load their malicious DLL on the compromised machine. This TTP may executed by normal 3rd party application so it is better to pivot by the parent process, parent command-line and command-line of the file that execute this regsvr32.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Other third part application may used this parameter but not so common in base windows environment.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Regsvr32 Silent and Install Param Dll Loading - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AsyncRAT", "Hermetic Wiper", "Living Off The Land", "Data Destruction", "Remcos", "Suspicious Regsvr32 Activity"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a DLL using the silent and dllinstall parameter.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 36}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Regsvr32 Silent and Install Param Dll Loading - Rule
action.correlationsearch.annotations = {"analytic_story": ["AsyncRAT", "Hermetic Wiper", "Living Off The Land", "Data Destruction", "Remcos", "Suspicious Regsvr32 Activity"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` AND Processes.process="*/i*" by Processes.dest Processes.parent_process Processes.process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_silent_and_install_param_dll_loading_filter`

[ESCU - Regsvr32 with Known Silent Switch Cmdline - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies Regsvr32.exe utilizing the silent switch to load DLLs. This technique has most recently been seen in IcedID campaigns to load its initial dll that will download the 2nd stage loader that will download and decrypt the config payload. The switch type may be either a hyphen `-` or forward slash `/`. This behavior is typically found with `-s`, and it is possible there are more switch types that may be used. \ During triage, review parallel processes and capture any artifacts that may have landed on disk. Isolate and contain the endpoint as necessary.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies Regsvr32.exe utilizing the silent switch to load DLLs. This technique has most recently been seen in IcedID campaigns to load its initial dll that will download the 2nd stage loader that will download and decrypt the config payload. The switch type may be either a hyphen `-` or forward slash `/`. This behavior is typically found with `-s`, and it is possible there are more switch types that may be used. \ During triage, review parallel processes and capture any artifacts that may have landed on disk. Isolate and contain the endpoint as necessary.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = minimal. but network operator can use this application to load dll.
action.escu.creation_date = 2021-07-27
action.escu.modification_date = 2021-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Regsvr32 with Known Silent Switch Cmdline - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Suspicious Regsvr32 Activity", "Remcos", "Living Off The Land", "Qakbot", "AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a DLL using the silent parameter.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Regsvr32 with Known Silent Switch Cmdline - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Suspicious Regsvr32 Activity", "Remcos", "Living Off The Land", "Qakbot", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_with_known_silent_switch_cmdline_filter`

[ESCU - Remcos client registry install entry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the presence of a registry key related to the Remcos RAT agent on a host. This detection is made by a Splunk query to search for instances where the registry key "license" is found in the "Software\Remcos" path. This analytic combines information from two data models: Endpoint.Processes and Endpoint.Registry and retrieves process information such as user, process ID, process name, process path, destination, parent process name, parent process, and process GUID. This analytic also retrieves registry information such as registry path, registry key name, registry value name, registry value data, and process GUID. By joining the process GUID from the Endpoint.Processes data model with the process GUID from the Endpoint.Registry data model, the analytic identifies instances where the "license" registry key is found in the "Software\Remcos" path. This detection is important because it suggests that the host has been compromised by the Remcos RAT agent. Remcos is a well-known remote access Trojan that can be used by attackers to gain unauthorized access to systems and exfiltrate sensitive data. Identifying this behavior allows the SOC to take immediate action to remove the RAT agent and prevent further compromise. The impact of this attack can be severe, as the attacker can gain unauthorized access to the system, steal sensitive information, or use the compromised system as a launching point for further attacks. Next steps include using this analytic in conjunction with other security measures and threat intelligence to ensure accurate detection and response.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the presence of a registry key related to the Remcos RAT agent on a host. This detection is made by a Splunk query to search for instances where the registry key "license" is found in the "Software\Remcos" path. This analytic combines information from two data models: Endpoint.Processes and Endpoint.Registry and retrieves process information such as user, process ID, process name, process path, destination, parent process name, parent process, and process GUID. This analytic also retrieves registry information such as registry path, registry key name, registry value name, registry value data, and process GUID. By joining the process GUID from the Endpoint.Processes data model with the process GUID from the Endpoint.Registry data model, the analytic identifies instances where the "license" registry key is found in the "Software\Remcos" path. This detection is important because it suggests that the host has been compromised by the Remcos RAT agent. Remcos is a well-known remote access Trojan that can be used by attackers to gain unauthorized access to systems and exfiltrate sensitive data. Identifying this behavior allows the SOC to take immediate action to remove the RAT agent and prevent further compromise. The impact of this attack can be severe, as the attacker can gain unauthorized access to the system, steal sensitive information, or use the compromised system as a launching point for further attacks. Next steps include using this analytic in conjunction with other security measures and threat intelligence to ensure accurate detection and response.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remcos client registry install entry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = A registry entry $registry_path$ with registry keyname $registry_key_name$ related to Remcos RAT in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remcos client registry install entry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the presence of a registry key related to the Remcos RAT agent on a host. This detection is made by a Splunk query to search for instances where the registry key "license" is found in the "Software\Remcos" path. This analytic combines information from two data models: Endpoint.Processes and Endpoint.Registry and retrieves process information such as user, process ID, process name, process path, destination, parent process name, parent process, and process GUID. This analytic also retrieves registry information such as registry path, registry key name, registry value name, registry value data, and process GUID. By joining the process GUID from the Endpoint.Processes data model with the process GUID from the Endpoint.Registry data model, the analytic identifies instances where the "license" registry key is found in the "Software\Remcos" path. This detection is important because it suggests that the host has been compromised by the Remcos RAT agent. Remcos is a well-known remote access Trojan that can be used by attackers to gain unauthorized access to systems and exfiltrate sensitive data. Identifying this behavior allows the SOC to take immediate action to remove the RAT agent and prevent further compromise. The impact of this attack can be severe, as the attacker can gain unauthorized access to the system, steal sensitive information, or use the compromised system as a launching point for further attacks. Next steps include using this analytic in conjunction with other security measures and threat intelligence to ensure accurate detection and response.
action.notable.param.rule_title = Remcos client registry install entry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_key_name=*\\Software\\Remcos*) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`remcos_client_registry_install_entry_filter`

[ESCU - Remcos RAT File Creation in Remcos Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect file creation in remcos folder in appdata which is the keylog and clipboard logs that will be send to its c2 server. This is really a good TTP indicator that there is a remcos rat in the system that do keylogging, clipboard grabbing and audio recording.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect file creation in remcos folder in appdata which is the keylog and clipboard logs that will be send to its c2 server. This is really a good TTP indicator that there is a remcos rat in the system that do keylogging, clipboard grabbing and audio recording.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-21
action.escu.modification_date = 2021-09-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remcos RAT File Creation in Remcos Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = file $file_name$ created in $file_path$ of $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remcos RAT File Creation in Remcos Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect file creation in remcos folder in appdata which is the keylog and clipboard logs that will be send to its c2 server. This is really a good TTP indicator that there is a remcos rat in the system that do keylogging, clipboard grabbing and audio recording.
action.notable.param.rule_title = Remcos RAT File Creation in Remcos Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.dat") Filesystem.file_path = "*\\remcos\\*" by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remcos_rat_file_creation_in_remcos_folder_filter`

[ESCU - Remote Desktop Process Running On System - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Desktop Process Running On System - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hidden Cobra Malware", "Active Directory Lateral Movement"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Desktop Process Running On System - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` 

[ESCU - Remote Process Instantiation via DCOM and PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with arguments utilized to start a process on a remote endpoint by abusing the DCOM protocol. Specifically, this search looks for the abuse of ShellExecute and ExecuteShellCommand. Red Teams and adversaries alike may abuse DCOM and `powershell.exe` for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with arguments utilized to start a process on a remote endpoint by abusing the DCOM protocol. Specifically, this search looks for the abuse of ShellExecute and ExecuteShellCommand. Red Teams and adversaries alike may abuse DCOM and `powershell.exe` for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may leverage DCOM to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-15
action.escu.modification_date = 2021-11-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via DCOM and PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $dest by abusing DCOM using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via DCOM and PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1021", "T1021.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with arguments utilized to start a process on a remote endpoint by abusing the DCOM protocol. Specifically, this search looks for the abuse of ShellExecute and ExecuteShellCommand. Red Teams and adversaries alike may abuse DCOM and `powershell.exe` for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via DCOM and PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Document.ActiveView.ExecuteShellCommand*" OR Processes.process="*Document.Application.ShellExecute*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_filter`

[ESCU - Remote Process Instantiation via DCOM and PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of PowerShell with arguments utilized to start a process on a remote endpoint by abusing the DCOM protocol. Specifically, this search looks for the abuse of ShellExecute and ExecuteShellCommand. Red Teams and adversaries alike may abuse DCOM for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of PowerShell with arguments utilized to start a process on a remote endpoint by abusing the DCOM protocol. Specifically, this search looks for the abuse of ShellExecute and ExecuteShellCommand. Red Teams and adversaries alike may abuse DCOM for lateral movement and remote code execution.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators may leverage DCOM to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via DCOM and PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $Computer$ by abusing WMI using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via DCOM and PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1021", "T1021.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of PowerShell with arguments utilized to start a process on a remote endpoint by abusing the DCOM protocol. Specifically, this search looks for the abuse of ShellExecute and ExecuteShellCommand. Red Teams and adversaries alike may abuse DCOM for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via DCOM and PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText="*Document.Application.ShellExecute*" OR ScriptBlockText="*Document.ActiveView.ExecuteShellCommand*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_script_block_filter`

[ESCU - Remote Process Instantiation via WinRM and PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with arguments utilized to start a process on a remote endpoint by abusing the WinRM protocol. Specifically, this search looks for the abuse of the `Invoke-Command` commandlet. Red Teams and adversaries alike may abuse WinRM and `powershell.exe` for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with arguments utilized to start a process on a remote endpoint by abusing the WinRM protocol. Specifically, this search looks for the abuse of the `Invoke-Command` commandlet. Red Teams and adversaries alike may abuse WinRM and `powershell.exe` for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may leverage WinRM and `Invoke-Command` to start a process on remote systems for system administration or automation use cases. However, this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-16
action.escu.modification_date = 2021-11-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via WinRM and PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $dest by abusing WinRM using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via WinRM and PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` with arguments utilized to start a process on a remote endpoint by abusing the WinRM protocol. Specifically, this search looks for the abuse of the `Invoke-Command` commandlet. Red Teams and adversaries alike may abuse WinRM and `powershell.exe` for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via WinRM and PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-Command*" AND Processes.process="*-ComputerName*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_powershell_filter`

[ESCU - Remote Process Instantiation via WinRM and PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of PowerShell with arguments utilized to start a process on a remote endpoint by abusing the WinRM protocol. Specifically, this search looks for the abuse of the `Invoke-Command` commandlet. Red Teams and adversaries alike may abuse WinRM for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of PowerShell with arguments utilized to start a process on a remote endpoint by abusing the WinRM protocol. Specifically, this search looks for the abuse of the `Invoke-Command` commandlet. Red Teams and adversaries alike may abuse WinRM for lateral movement and remote code execution.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators may leverage WinRM and `Invoke-Command` to start a process on remote systems for system administration or automation use cases. This activity is usually limited to a small set of hosts or users. In certain environments, tuning may not be possible.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via WinRM and PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $Computer$ by abusing WinRM using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via WinRM and PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 90, "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of PowerShell with arguments utilized to start a process on a remote endpoint by abusing the WinRM protocol. Specifically, this search looks for the abuse of the `Invoke-Command` commandlet. Red Teams and adversaries alike may abuse WinRM for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via WinRM and PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText="*Invoke-Command*" AND ScriptBlockText="*-ComputerName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_powershell_script_block_filter`

[ESCU - Remote Process Instantiation via WinRM and Winrs - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `winrs.exe` with command-line arguments utilized to start a process on a remote endpoint. Red Teams and adversaries alike may abuse the WinRM protocol and this binary for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `winrs.exe` with command-line arguments utilized to start a process on a remote endpoint. Red Teams and adversaries alike may abuse the WinRM protocol and this binary for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may leverage WinRM and WinRs to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-11
action.escu.modification_date = 2021-11-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via WinRM and Winrs - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via WinRM and Winrs - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `winrs.exe` with command-line arguments utilized to start a process on a remote endpoint. Red Teams and adversaries alike may abuse the WinRM protocol and this binary for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via WinRM and Winrs
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=winrs.exe OR Processes.original_file_name=winrs.exe) (Processes.process="*-r:*" OR Processes.process="*-remote:*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_winrs_filter`

[ESCU - Remote Process Instantiation via WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies wmic.exe being launched with parameters to spawn a process on a remote system. Red Teams and adversaries alike may abuse WMI and this binary for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies wmic.exe being launched with parameters to spawn a process on a remote system. Red Teams and adversaries alike may abuse WMI and this binary for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via WMI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A wmic.exe process $process$ contain process spawn commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via WMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Suspicious WMI Use", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies wmic.exe being launched with parameters to spawn a process on a remote system. Red Teams and adversaries alike may abuse WMI and this binary for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via WMI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process="*/node:*" AND Processes.process="*process*" AND Processes.process="*call*" AND  Processes.process="*create*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter`

[ESCU - Remote Process Instantiation via WMI and PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` leveraging the `Invoke-WmiMethod` commandlet complemented with arguments utilized to start a process on a remote endpoint by abusing WMI. Red Teams and adversaries alike may abuse WMI and `powershell.exe` for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` leveraging the `Invoke-WmiMethod` commandlet complemented with arguments utilized to start a process on a remote endpoint by abusing WMI. Red Teams and adversaries alike may abuse WMI and `powershell.exe` for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may leverage WWMI and powershell.exe to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-15
action.escu.modification_date = 2021-11-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via WMI and PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $dest by abusing WMI using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via WMI and PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `powershell.exe` leveraging the `Invoke-WmiMethod` commandlet complemented with arguments utilized to start a process on a remote endpoint by abusing WMI. Red Teams and adversaries alike may abuse WMI and `powershell.exe` for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via WMI and PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-WmiMethod*" AND Processes.process="*-CN*" AND Processes.process="*-Class Win32_Process*" AND  Processes.process="*-Name create*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_filter`

[ESCU - Remote Process Instantiation via WMI and PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Invoke-WmiMethod` commandlet with arguments utilized to start a process on a remote endpoint by abusing WMI. Red Teams and adversaries alike may abuse WMI and this commandlet for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Invoke-WmiMethod` commandlet with arguments utilized to start a process on a remote endpoint by abusing WMI. Red Teams and adversaries alike may abuse WMI and this commandlet for lateral movement and remote code execution.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators may leverage WWMI and powershell.exe to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2022-11-15
action.escu.modification_date = 2022-11-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Process Instantiation via WMI and PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A process was started on a remote endpoint from $Computer$ by abusing WMI using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Process Instantiation via WMI and PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 70, "impact": 90, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Invoke-WmiMethod` commandlet with arguments utilized to start a process on a remote endpoint by abusing WMI. Red Teams and adversaries alike may abuse WMI and this commandlet for lateral movement and remote code execution.
action.notable.param.rule_title = Remote Process Instantiation via WMI and PowerShell Script Block
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*Invoke-WmiMethod*" AND (ScriptBlockText="*-CN*" OR ScriptBlockText="*-ComputerName*") AND ScriptBlockText="*-Class Win32_Process*" AND ScriptBlockText="*-Name create*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID|  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_script_block_filter`

[ESCU - Remote System Discovery with Adsisearcher - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain computers. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain computers. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain computers for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use Adsisearcher for troubleshooting.
action.escu.creation_date = 2022-06-29
action.escu.modification_date = 2022-06-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote System Discovery with Adsisearcher - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery enumeration on $Computer$ by $user$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote System Discovery with Adsisearcher - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain computers. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain computers for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Remote System Discovery with Adsisearcher
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*adsisearcher*" AND ScriptBlockText = "*objectcategory=computer*" AND ScriptBlockText IN ("*findAll()*","*findOne()*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `remote_system_discovery_with_adsisearcher_filter`

[ESCU - Remote System Discovery with Dsquery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `dsquery.exe` with command-line arguments utilized to discover remote systems. The `computer` argument returns a list of all computers registered in the domain. Red Teams and adversaries alike engage in remote system discovery for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `dsquery.exe` with command-line arguments utilized to discover remote systems. The `computer` argument returns a list of all computers registered in the domain. Red Teams and adversaries alike engage in remote system discovery for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-31
action.escu.modification_date = 2021-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote System Discovery with Dsquery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote System Discovery with Dsquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe") (Processes.process="*computer*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_dsquery_filter`

[ESCU - Remote System Discovery with Net - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to discover remote systems. The argument `domain computers /domain` returns a list of all domain computers. Red Teams and adversaries alike use net.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `net.exe` or `net1.exe` with command-line arguments utilized to discover remote systems. The argument `domain computers /domain` returns a list of all domain computers. Red Teams and adversaries alike use net.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-08-30
action.escu.modification_date = 2021-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote System Discovery with Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "IcedID"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote System Discovery with Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "IcedID"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process="*domain computers*" AND Processes.process=*/do*) OR (Processes.process="*view*" AND Processes.process=*/do*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_net_filter`

[ESCU - Remote System Discovery with Wmic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to discover remote systems. The arguments utilized in this command return a list of all the systems registered in the domain. Red Teams and adversaries alike may leverage WMI and wmic.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to discover remote systems. The arguments utilized in this command return a list of all the systems registered in the domain. Red Teams and adversaries alike may leverage WMI and wmic.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-01
action.escu.modification_date = 2021-09-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote System Discovery with Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote System Discovery with Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `wmic.exe` with command-line arguments utilized to discover remote systems. The arguments utilized in this command return a list of all the systems registered in the domain. Red Teams and adversaries alike may leverage WMI and wmic.exe to identify remote systems for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Remote System Discovery with Wmic
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_computer* AND Processes.process="*GET ds_samaccountname*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_wmic_filter`

[ESCU - Remote WMI Command Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. Filter as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Volt Typhoon", "Graceful Wipe Out Attack", "IcedID", "Suspicious WMI Use", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = A wmic.exe process $process$ contain node commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote WMI Command Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Volt Typhoon", "Graceful Wipe Out Attack", "IcedID", "Suspicious WMI Use", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed.
action.notable.param.rule_title = Remote WMI Command Attempt
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process=*node* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter`

[ESCU - Resize ShadowStorage volume - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network admin can resize the shadowstorage for valid purposes.
action.escu.creation_date = 2021-03-12
action.escu.modification_date = 2021-03-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Resize ShadowStorage volume - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Clop Ransomware", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = A process $parent_process_name$ attempt to resize shadow copy with commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Resize ShadowStorage volume - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible
action.notable.param.rule_title = Resize ShadowStorage volume
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name =  "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter`

[ESCU - Revil Common Exec Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious commandline parameter that are commonly used by REVIL ransomware to encrypts the compromise machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies suspicious commandline parameter that are commonly used by REVIL ransomware to encrypts the compromise machine.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = third party tool may have same command line parameters as revil ransomware.
action.escu.creation_date = 2021-06-02
action.escu.modification_date = 2021-06-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Revil Common Exec Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ with commandline $process$ related to revil ransomware in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Revil Common Exec Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 60, "mitre_attack": ["T1204"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies suspicious commandline parameter that are commonly used by REVIL ransomware to encrypts the compromise machine.
action.notable.param.rule_title = Revil Common Exec Parameter
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process = "* -full *" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_common_exec_parameter_filter`

[ESCU - Revil Registry Entry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious modification in registry entry to keep some malware data during its infection. This technique seen in several apt implant, malware and ransomware like REVIL where it keep some information like the random generated file extension it uses for all the encrypted files and ransomware notes file name in the compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies suspicious modification in registry entry to keep some malware data during its infection. This technique seen in several apt implant, malware and ransomware like REVIL where it keep some information like the random generated file extension it uses for all the encrypted files and ransomware notes file name in the compromised host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Revil Registry Entry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Revil Ransomware", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = A registry entry $registry_path$ with registry value $registry_value_name$ and $registry_value_name$ related to revil ransomware in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Revil Registry Entry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 60, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies suspicious modification in registry entry to keep some malware data during its infection. This technique seen in several apt implant, malware and ransomware like REVIL where it keep some information like the random generated file extension it uses for all the encrypted files and ransomware notes file name in the compromised host.
action.notable.param.rule_title = Revil Registry Entry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_registry_entry_filter`

[ESCU - Rubeus Command Line Parameters - Rule]
action.escu = 0
action.escu.enabled = 1
description = Rubeus is a C# toolset for raw Kerberos interaction and abuses. It is heavily adapted from Benjamin Delpys Kekeo project and Vincent LE TOUXs MakeMeEnterpriseAdmin project. This analytic looks for the use of Rubeus command line arguments utilized in common Kerberos attacks like exporting and importing tickets, forging silver and golden tickets, requesting a TGT or TGS, kerberoasting, password spraying, etc. Red teams and adversaries alike use Rubeus for Kerberos attacks within Active Directory networks. Defenders should be aware that adversaries may customize the source code of Rubeus and modify the command line parameters. This would effectively bypass this analytic.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550", "T1550.003", "T1558", "T1558.003", "T1558.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Rubeus is a C# toolset for raw Kerberos interaction and abuses. It is heavily adapted from Benjamin Delpys Kekeo project and Vincent LE TOUXs MakeMeEnterpriseAdmin project. This analytic looks for the use of Rubeus command line arguments utilized in common Kerberos attacks like exporting and importing tickets, forging silver and golden tickets, requesting a TGT or TGS, kerberoasting, password spraying, etc. Red teams and adversaries alike use Rubeus for Kerberos attacks within Active Directory networks. Defenders should be aware that adversaries may customize the source code of Rubeus and modify the command line parameters. This would effectively bypass this analytic.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, legitimate applications may use the same command line parameters as Rubeus. Filter as needed.
action.escu.creation_date = 2022-02-01
action.escu.modification_date = 2022-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rubeus Command Line Parameters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = Rubeus command line parameters were used on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rubeus Command Line Parameters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1550", "T1550.003", "T1558", "T1558.003", "T1558.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Rubeus is a C# toolset for raw Kerberos interaction and abuses. It is heavily adapted from Benjamin Delpys Kekeo project and Vincent LE TOUXs MakeMeEnterpriseAdmin project. This analytic looks for the use of Rubeus command line arguments utilized in common Kerberos attacks like exporting and importing tickets, forging silver and golden tickets, requesting a TGT or TGS, kerberoasting, password spraying, etc. Red teams and adversaries alike use Rubeus for Kerberos attacks within Active Directory networks. Defenders should be aware that adversaries may customize the source code of Rubeus and modify the command line parameters. This would effectively bypass this analytic.
action.notable.param.rule_title = Rubeus Command Line Parameters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*ptt /ticket*" OR Processes.process = "* monitor /interval*" OR Processes.process ="* asktgt* /user:*" OR Processes.process ="* asktgs* /service:*" OR Processes.process ="* golden* /user:*" OR Processes.process ="* silver* /service:*" OR Processes.process ="* kerberoast*" OR Processes.process ="* asreproast*" OR Processes.process = "* renew* /ticket:*" OR Processes.process = "* brute* /password:*" OR Processes.process = "* brute* /passwords:*" OR Processes.process ="* harvest*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rubeus_command_line_parameters_filter`

[ESCU - Rubeus Kerberos Ticket Exports Through Winlogon Access - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic looks for a process accessing the winlogon.exe system process. The Splunk Threat Research team identified this behavior when using the Rubeus tool to monitor for and export kerberos tickets from memory. Before being able to export tickets. Rubeus will try to escalate privileges to SYSTEM by obtaining a handle to winlogon.exe before trying to monitor for kerberos tickets. Exporting tickets from memory is typically the first step for pass the ticket attacks. Red teams and adversaries alike may use the pass the ticket technique using stolen Kerberos tickets to move laterally within an environment, bypassing normal system access controls. Defenders should be aware that adversaries may customize the source code of Rubeus to potentially bypass this analytic.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550", "T1550.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic looks for a process accessing the winlogon.exe system process. The Splunk Threat Research team identified this behavior when using the Rubeus tool to monitor for and export kerberos tickets from memory. Before being able to export tickets. Rubeus will try to escalate privileges to SYSTEM by obtaining a handle to winlogon.exe before trying to monitor for kerberos tickets. Exporting tickets from memory is typically the first step for pass the ticket attacks. Red teams and adversaries alike may use the pass the ticket technique using stolen Kerberos tickets to move laterally within an environment, bypassing normal system access controls. Defenders should be aware that adversaries may customize the source code of Rubeus to potentially bypass this analytic.
action.escu.how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment.
action.escu.known_false_positives = Legitimate applications may obtain a handle for winlogon.exe. Filter as needed
action.escu.creation_date = 2022-02-07
action.escu.modification_date = 2022-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rubeus Kerberos Ticket Exports Through Winlogon Access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Winlogon.exe was accessed by $SourceImage$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rubeus Kerberos Ticket Exports Through Winlogon Access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1550", "T1550.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic looks for a process accessing the winlogon.exe system process. The Splunk Threat Research team identified this behavior when using the Rubeus tool to monitor for and export kerberos tickets from memory. Before being able to export tickets. Rubeus will try to escalate privileges to SYSTEM by obtaining a handle to winlogon.exe before trying to monitor for kerberos tickets. Exporting tickets from memory is typically the first step for pass the ticket attacks. Red teams and adversaries alike may use the pass the ticket technique using stolen Kerberos tickets to move laterally within an environment, bypassing normal system access controls. Defenders should be aware that adversaries may customize the source code of Rubeus to potentially bypass this analytic.
action.notable.param.rule_title = Rubeus Kerberos Ticket Exports Through Winlogon Access
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `sysmon` EventCode=10 TargetImage=C:\\Windows\\system32\\winlogon.exe (GrantedAccess=0x1f3fff) (SourceImage!=C:\\Windows\\system32\\svchost.exe AND SourceImage!=C:\\Windows\\system32\\lsass.exe AND SourceImage!=C:\\Windows\\system32\\LogonUI.exe AND SourceImage!=C:\\Windows\\system32\\smss.exe AND SourceImage!=C:\\Windows\\system32\\wbem\\wmiprvse.exe) | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `rubeus_kerberos_ticket_exports_through_winlogon_access_filter`

[ESCU - Runas Execution in CommandLine - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic look for a spawned runas.exe process with a administrator user option parameter. This parameter was abused by adversaries, malware author or even red teams to gain elevated privileges in target host. This is a good hunting query to figure out privilege escalation tactics that may used for different stages like lateral movement but take note that administrator may use this command in purpose so its better to see other event context before and after this analytic.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134", "T1134.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic look for a spawned runas.exe process with a administrator user option parameter. This parameter was abused by adversaries, malware author or even red teams to gain elevated privileges in target host. This is a good hunting query to figure out privilege escalation tactics that may used for different stages like lateral movement but take note that administrator may use this command in purpose so its better to see other event context before and after this analytic.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = A network operator or systems administrator may utilize an automated or manual execute this command that may generate false positives. filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Runas Execution in CommandLine - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Runas Execution in CommandLine - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Hermetic Wiper", "Windows Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1134", "T1134.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_runas` AND Processes.process = "*/user:*" AND Processes.process = "*admin*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `runas_execution_in_commandline_filter`

[ESCU - Rundll32 Control RunDLL Hunt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting detection identifies rundll32.exe with `control_rundll` within the command-line, loading a .cpl or another file type. Developed in relation to CVE-2021-40444. Rundll32.exe can also be used to execute Control Panel Item files (.cpl) through the undocumented shell32.dll functions Control_RunDLL and Control_RunDLLAsUser. Double-clicking a .cpl file also causes rundll32.exe to execute. \ This is written to be a bit more broad by not including .cpl. \ During triage, review parallel processes to identify any further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting detection identifies rundll32.exe with `control_rundll` within the command-line, loading a .cpl or another file type. Developed in relation to CVE-2021-40444. Rundll32.exe can also be used to execute Control Panel Item files (.cpl) through the undocumented shell32.dll functions Control_RunDLL and Control_RunDLLAsUser. Double-clicking a .cpl file also causes rundll32.exe to execute. \ This is written to be a bit more broad by not including .cpl. \ During triage, review parallel processes to identify any further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This is a hunting detection, meant to provide a understanding of how voluminous control_rundll is within the environment.
action.escu.creation_date = 2021-09-08
action.escu.modification_date = 2021-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 Control RunDLL Hunt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Microsoft MSHTML Remote Code Execution CVE-2021-40444", "Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 Control RunDLL Hunt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Microsoft MSHTML Remote Code Execution CVE-2021-40444", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-40444"], "impact": 30, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL*  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_control_rundll_hunt_filter`

[ESCU - Rundll32 Control RunDLL World Writable Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies rundll32.exe with `control_rundll` within the command-line, loading a .cpl or another file type from windows\temp, programdata, or appdata. Developed in relation to CVE-2021-40444. Rundll32.exe can also be used to execute Control Panel Item files (.cpl) through the undocumented shell32.dll functions Control_RunDLL and Control_RunDLLAsUser. Double-clicking a .cpl file also causes rundll32.exe to execute. This is written to be a bit more broad by not including .cpl. The paths are specified, add more as needed. During triage, review parallel processes to identify any further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies rundll32.exe with `control_rundll` within the command-line, loading a .cpl or another file type from windows\temp, programdata, or appdata. Developed in relation to CVE-2021-40444. Rundll32.exe can also be used to execute Control Panel Item files (.cpl) through the undocumented shell32.dll functions Control_RunDLL and Control_RunDLLAsUser. Double-clicking a .cpl file also causes rundll32.exe to execute. This is written to be a bit more broad by not including .cpl. The paths are specified, add more as needed. During triage, review parallel processes to identify any further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This may be tuned, or a new one related, by adding .cpl to command-line. However, it's important to look for both. Tune/filter as needed.
action.escu.creation_date = 2021-09-08
action.escu.modification_date = 2021-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 Control RunDLL World Writable Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Microsoft MSHTML Remote Code Execution CVE-2021-40444", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 Control RunDLL World Writable Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Microsoft MSHTML Remote Code Execution CVE-2021-40444", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-40444"], "impact": 80, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies rundll32.exe with `control_rundll` within the command-line, loading a .cpl or another file type from windows\temp, programdata, or appdata. Developed in relation to CVE-2021-40444. Rundll32.exe can also be used to execute Control Panel Item files (.cpl) through the undocumented shell32.dll functions Control_RunDLL and Control_RunDLLAsUser. Double-clicking a .cpl file also causes rundll32.exe to execute. This is written to be a bit more broad by not including .cpl. The paths are specified, add more as needed. During triage, review parallel processes to identify any further suspicious behavior.
action.notable.param.rule_title = Rundll32 Control RunDLL World Writable Directory
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL* AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*")  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_control_rundll_world_writable_directory_filter`

[ESCU - Rundll32 Create Remote Thread To A Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe to any process. This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information in the compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the suspicious Remote Thread execution of rundll32.exe to any process. This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information in the compromised host.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-07-29
action.escu.modification_date = 2021-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 Create Remote Thread To A Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = rundl32 process $SourceImage$ create a remote thread to process $TargetImage$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "SourceImage", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 Create Remote Thread To A Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe to any process. This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information in the compromised host.
action.notable.param.rule_title = Rundll32 Create Remote Thread To A Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_create_remote_thread_to_a_process_filter`

[ESCU - Rundll32 CreateRemoteThread In Browser - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-07-26
action.escu.modification_date = 2021-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 CreateRemoteThread In Browser - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = rundl32 process $SourceImage$ create a remote thread to browser process $TargetImage$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"threat_object_field": "SourceImage", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 CreateRemoteThread In Browser - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process.
action.notable.param.rule_title = Rundll32 CreateRemoteThread In Browser
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_createremotethread_in_browser_filter`

[ESCU - Rundll32 DNSQuery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-02-18
action.escu.modification_date = 2022-02-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 DNSQuery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = rundll32 process $process_name$ made a DNS query for $query$ from host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 DNSQuery - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component.
action.notable.param.rule_title = Rundll32 DNSQuery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22 process_name="rundll32.exe" | stats count min(_time) as firstTime max(_time) as lastTime values(query) as query values(answer) as answer values(QueryResults) as query_results values(QueryStatus) as query_status by process_name process_guid Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_dnsquery_filter`

[ESCU - Rundll32 LockWorkStation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious rundll32 commandline to lock the workstation through command line. This technique was seen in CONTI leak tooling and script as part of its defense evasion. This technique is not a common practice to lock a screen and maybe a good indicator of compromise.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious rundll32 commandline to lock the workstation through command line. This technique was seen in CONTI leak tooling and script as part of its defense evasion. This technique is not a common practice to lock a screen and maybe a good indicator of compromise.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-09
action.escu.modification_date = 2021-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 LockWorkStation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = Process $process_name$ with cmdline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 LockWorkStation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process= "*user32.dll,LockWorkStation*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_lockworkstation_filter`

[ESCU - Rundll32 Process Creating Exe Dll Files - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious rundll32 process that drops executable (.exe or .dll) files. This behavior seen in rundll32 process of IcedID that tries to drop copy of itself in temp folder or download executable drop it either appdata or programdata as part of its execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious rundll32 process that drops executable (.exe or .dll) files. This behavior seen in rundll32 process of IcedID that tries to drop copy of itself in temp folder or download executable drop it either appdata or programdata as part of its execution.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, TargetFilename, and eventcode 11 executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-07-26
action.escu.modification_date = 2021-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 Process Creating Exe Dll Files - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = rundll32 process $process_name$ drops a file $TargetFilename$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 Process Creating Exe Dll Files - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious rundll32 process that drops executable (.exe or .dll) files. This behavior seen in rundll32 process of IcedID that tries to drop copy of itself in temp folder or download executable drop it either appdata or programdata as part of its execution.
action.notable.param.rule_title = Rundll32 Process Creating Exe Dll Files
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=11 process_name="rundll32.exe" TargetFilename IN ("*.exe", "*.dll",) | stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename ProcessGuid dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_process_creating_exe_dll_files_filter`

[ESCU - Rundll32 Shimcache Flush - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious rundll32 commandline to clear shim cache. This technique is a anti-forensic technique to clear the cache taht are one important artifacts in terms of digital forensic during attacks or incident. This TTP is a good indicator that someone tries to evade some tools and clear foothold on the machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious rundll32 commandline to clear shim cache. This technique is a anti-forensic technique to clear the cache taht are one important artifacts in terms of digital forensic during attacks or incident. This TTP is a good indicator that someone tries to evade some tools and clear foothold on the machine.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-10-05
action.escu.modification_date = 2021-10-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 Shimcache Flush - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = rundll32 process execute $process$ to clear shim cache in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 Shimcache Flush - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious rundll32 commandline to clear shim cache. This technique is a anti-forensic technique to clear the cache taht are one important artifacts in terms of digital forensic during attacks or incident. This TTP is a good indicator that someone tries to evade some tools and clear foothold on the machine.
action.notable.param.rule_title = Rundll32 Shimcache Flush
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_rundll32` AND Processes.process = "*apphelp.dll,ShimFlushCache*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_shimcache_flush_filter`

[ESCU - Rundll32 with no Command Line Arguments with Network - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Rundll32 with no Command Line Arguments with Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Cobalt Strike", "BlackByte Ransomware", "PrintNightmare CVE-2021-34527", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = A rundll32 process $process_name$ with no commandline argument like this process commandline $process$ in host $src$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Rundll32 with no Command Line Arguments with Network - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Cobalt Strike", "BlackByte Ransomware", "PrintNightmare CVE-2021-34527", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-34527"], "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Rundll32 with no Command Line Arguments with Network
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where `process_rundll32` AND Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(rundll32\.exe.{0,4}$)" | rename dest as src | join host process_id [| tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `rundll32_with_no_command_line_arguments_with_network_filter`

[ESCU - RunDLL Loading DLL By Ordinal - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe loading an export function by ordinal value. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. Utilizing ordinal values makes it a bit more complicated for analysts to understand the behavior until the DLL is reviewed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe loading an export function by ordinal value. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. Utilizing ordinal values makes it a bit more complicated for analysts to understand the behavior until the DLL is reviewed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are possible with native utilities and third party applications. Filtering may be needed based on command-line, or add world writeable paths to restrict query.
action.escu.creation_date = 2022-02-08
action.escu.modification_date = 2022-02-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - RunDLL Loading DLL By Ordinal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes", "Suspicious Rundll32 Activity", "Living Off The Land", "IcedID"]
action.risk = 1
action.risk.param._risk_message = A rundll32 process $process_name$ with ordinal parameter like this process commandline $process$ on host $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RunDLL Loading DLL By Ordinal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes", "Suspicious Rundll32 Activity", "Living Off The Land", "IcedID"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe loading an export function by ordinal value. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. Utilizing ordinal values makes it a bit more complicated for analysts to understand the behavior until the DLL is reviewed.
action.notable.param.rule_title = RunDLL Loading DLL By Ordinal
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"rundll32.+\#\d+") | `rundll_loading_dll_by_ordinal_filter`

[ESCU - Ryuk Test Files Detected - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.
action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
action.escu.known_false_positives = If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs.
action.escu.creation_date = 2020-11-06
action.escu.modification_date = 2020-11-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ryuk Test Files Detected - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware"]
action.risk = 1
action.risk.param._risk_message = A creation of ryuk test file $file_path$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ryuk Test Files Detected - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1486"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.
action.notable.param.rule_title = Ryuk Test Files Detected
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter`

[ESCU - Ryuk Wake on LAN Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public\). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public\). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited to no known false positives.
action.escu.creation_date = 2021-03-01
action.escu.modification_date = 2021-03-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ryuk Wake on LAN Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ with wake on LAN commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ryuk Wake on LAN Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1059", "T1059.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public\). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.
action.notable.param.rule_title = Ryuk Wake on LAN Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ryuk_wake_on_lan_command_filter`

[ESCU - SAM Database File Access Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = Natively, `dllhost.exe` will access the files. Every environment will have additional native processes that do as well. Filter by process_name. As an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - SAM Database File Access Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Credential Dumping", "Graceful Wipe Out Attack"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SAM Database File Access Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-36934"], "impact": 80, "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` (EventCode=4663)  process_name!=*\\dllhost.exe Object_Name IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") | stats values(Accesses) count by process_name Object_Name  dest user | `sam_database_file_access_attempt_filter`

[ESCU - Samsam Test File Write - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation.
action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
action.escu.known_false_positives = No false positives have been identified.
action.escu.creation_date = 2018-12-14
action.escu.modification_date = 2018-12-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Samsam Test File Write - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware"]
action.risk = 1
action.risk.param._risk_message = A samsam ransomware test file creation in $file_path$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 12}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Samsam Test File Write - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware"], "cis20": ["CIS 10"], "confidence": 20, "impact": 60, "mitre_attack": ["T1486"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation.
action.notable.param.rule_title = Samsam Test File Write
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`

[ESCU - Sc exe Manipulating Windows Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Sc exe Manipulating Windows Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Windows Persistence Techniques", "Disabling Security Tools", "NOBELIUM Group", "Azorult", "Windows Drivers"]
action.risk = 1
action.risk.param._risk_message = A sc process $process_name$ with commandline $process$ to create of configure services in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Sc exe Manipulating Windows Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Windows Persistence Techniques", "Disabling Security Tools", "NOBELIUM Group", "Azorult", "Windows Drivers"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.
action.notable.param.rule_title = Sc exe Manipulating Windows Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter`

[ESCU - SchCache Change By App Connect And Create ADSI Object - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect an application try to connect and create ADSI Object to do LDAP query. Every time an application connects to the directory and attempts to create an ADSI object, the Active Directory Schema is checked for changes. If it has changed since the last connection, the schema is downloaded and stored in a cache on the local computer either in %LOCALAPPDATA%\Microsoft\Windows\SchCache or %systemroot%\SchCache. We found this a good anomaly use case to detect suspicious application like blackmatter ransomware that use ADS object api to execute ldap query. having a good list of ldap or normal AD query tool used within the network is a good start to reduce the noise.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to detect an application try to connect and create ADSI Object to do LDAP query. Every time an application connects to the directory and attempts to create an ADSI object, the Active Directory Schema is checked for changes. If it has changed since the last connection, the schema is downloaded and stored in a cache on the local computer either in %LOCALAPPDATA%\Microsoft\Windows\SchCache or %systemroot%\SchCache. We found this a good anomaly use case to detect suspicious application like blackmatter ransomware that use ADS object api to execute ldap query. having a good list of ldap or normal AD query tool used within the network is a good start to reduce the noise.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = normal application like mmc.exe and other ldap query tool may trigger this detections.
action.escu.creation_date = 2021-09-07
action.escu.modification_date = 2021-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - SchCache Change By App Connect And Create ADSI Object - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["blackMatter ransomware"]
action.risk = 1
action.risk.param._risk_message = process $Image$ create a file $TargetFilename$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SchCache Change By App Connect And Create ADSI Object - Rule
action.correlationsearch.annotations = {"analytic_story": ["blackMatter ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=11  TargetFilename = "*\\Windows\\SchCache\\*" TargetFilename = "*.sch*" NOT (Image IN ("*\\Windows\\system32\\mmc.exe")) |stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schcache_change_by_app_connect_and_create_adsi_object_filter`

[ESCU - Schedule Task with HTTP Command Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the registration of suspicious tasks on Windows using the Windows Security EventCode 4698, "A scheduled task was created." It specifically looks for tasks registered through schtasks.exe or TaskService that have command arguments containing the string "HTTP." This behavior is often associated with malware or attacks that utilize Living off the Land binaries (lolbins) to download additional files or payloads to the compromised machine.\
The search returns information about the task, such as the task name, command, author, enabled status, hidden status, and arguments. Upon triage, it is important to identify the source of the scheduled task, whether it was registered through schtasks.exe or TaskService. Review the details of the created task and the command to be executed. Capture relevant artifacts on disk and examine them. Additionally, identify any parallel processes occurring within the same timeframe to determine the source of the attack.\
Implementing this analytic requires ingesting logs with information about task schedules, specifically Windows Security Log EventCode 4698, from your endpoints. It is recommended to tune and filter known instances of task schedules used in your environment to minimize false positives.\
Detecting the registration of suspicious tasks with HTTP command arguments is valuable for a SOC as it indicates potential malicious activity or an attempt to establish persistence on the system. If a true positive is found, further investigation is warranted to analyze the nature and purpose of the scheduled task, identify any downloaded files or payloads, and mitigate the associated risks. The impact of a true positive can vary but may include data exfiltration, malware propagation, or unauthorized access to sensitive information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the registration of suspicious tasks on Windows using the Windows Security EventCode 4698, "A scheduled task was created." It specifically looks for tasks registered through schtasks.exe or TaskService that have command arguments containing the string "HTTP." This behavior is often associated with malware or attacks that utilize Living off the Land binaries (lolbins) to download additional files or payloads to the compromised machine.\
The search returns information about the task, such as the task name, command, author, enabled status, hidden status, and arguments. Upon triage, it is important to identify the source of the scheduled task, whether it was registered through schtasks.exe or TaskService. Review the details of the created task and the command to be executed. Capture relevant artifacts on disk and examine them. Additionally, identify any parallel processes occurring within the same timeframe to determine the source of the attack.\
Implementing this analytic requires ingesting logs with information about task schedules, specifically Windows Security Log EventCode 4698, from your endpoints. It is recommended to tune and filter known instances of task schedules used in your environment to minimize false positives.\
Detecting the registration of suspicious tasks with HTTP command arguments is valuable for a SOC as it indicates potential malicious activity or an attempt to establish persistence on the system. If a true positive is found, further investigation is warranted to analyze the nature and purpose of the scheduled task, identify any downloaded files or payloads, and mitigate the associated risks. The impact of a true positive can vary but may include data exfiltration, malware propagation, or unauthorized access to sensitive information.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Schedule Task with HTTP Command Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Living Off The Land", "Winter Vivern", "Windows Persistence Techniques", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A schedule task process commandline arguments $Arguments$ with http string on it in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Schedule Task with HTTP Command Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land", "Winter Vivern", "Windows Persistence Techniques", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the registration of suspicious tasks on Windows using the Windows Security EventCode 4698, "A scheduled task was created." It specifically looks for tasks registered through schtasks.exe or TaskService that have command arguments containing the string "HTTP." This behavior is often associated with malware or attacks that utilize Living off the Land binaries (lolbins) to download additional files or payloads to the compromised machine.\
The search returns information about the task, such as the task name, command, author, enabled status, hidden status, and arguments. Upon triage, it is important to identify the source of the scheduled task, whether it was registered through schtasks.exe or TaskService. Review the details of the created task and the command to be executed. Capture relevant artifacts on disk and examine them. Additionally, identify any parallel processes occurring within the same timeframe to determine the source of the attack.\
Implementing this analytic requires ingesting logs with information about task schedules, specifically Windows Security Log EventCode 4698, from your endpoints. It is recommended to tune and filter known instances of task schedules used in your environment to minimize false positives.\
Detecting the registration of suspicious tasks with HTTP command arguments is valuable for a SOC as it indicates potential malicious activity or an attempt to establish persistence on the system. If a true positive is found, further investigation is warranted to analyze the nature and purpose of the scheduled task, identify any downloaded files or payloads, and mitigate the associated risks. The impact of a true positive can vary but may include data exfiltration, malware propagation, or unauthorized access to sensitive information.
action.notable.param.rule_title = Schedule Task with HTTP Command Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4698 | xmlkv Message| search Arguments IN ("*http*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_http_command_arguments_filter`

[ESCU - Schedule Task with Rundll32 Command Trigger - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of suspicious tasks in Windows, specifically tasks using the rundll32 command. It's implemented using Windows Security EventCode 4698 for A scheduled task was created, and looks for tasks executed either via schtasks.exe or TaskService. This behavior is worth identifying as it is commonly used by malware, such as TrickBot, that leverages rundll32 to execute its downloader.\
If a true positive is found, it suggests an attacker is trying to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes.\
To implement this analytic, ensure you are ingesting logs with task schedule information from your endpoints. Be aware of potential false positives - legitimate uses of Task Scheduler in your environment may cause benign activities to be flagged.\
Upon triage, review the scheduled task's source and the command to be executed. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the creation of suspicious tasks in Windows, specifically tasks using the rundll32 command. It's implemented using Windows Security EventCode 4698 for A scheduled task was created, and looks for tasks executed either via schtasks.exe or TaskService. This behavior is worth identifying as it is commonly used by malware, such as TrickBot, that leverages rundll32 to execute its downloader.\
If a true positive is found, it suggests an attacker is trying to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes.\
To implement this analytic, ensure you are ingesting logs with task schedule information from your endpoints. Be aware of potential false positives - legitimate uses of Task Scheduler in your environment may cause benign activities to be flagged.\
Upon triage, review the scheduled task's source and the command to be executed. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-04-19
action.escu.modification_date = 2021-04-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Schedule Task with Rundll32 Command Trigger - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Persistence Techniques", "Trickbot", "IcedID", "Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A schedule task process commandline rundll32 arguments $Arguments$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Schedule Task with Rundll32 Command Trigger - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Trickbot", "IcedID", "Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of suspicious tasks in Windows, specifically tasks using the rundll32 command. It's implemented using Windows Security EventCode 4698 for A scheduled task was created, and looks for tasks executed either via schtasks.exe or TaskService. This behavior is worth identifying as it is commonly used by malware, such as TrickBot, that leverages rundll32 to execute its downloader.\
If a true positive is found, it suggests an attacker is trying to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes.\
To implement this analytic, ensure you are ingesting logs with task schedule information from your endpoints. Be aware of potential false positives - legitimate uses of Task Scheduler in your environment may cause benign activities to be flagged.\
Upon triage, review the scheduled task's source and the command to be executed. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.notable.param.rule_title = Schedule Task with Rundll32 Command Trigger
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*rundll32*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_rundll32_command_trigger_filter`

[ESCU - Scheduled Task Creation on Remote Endpoint using At - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of suspicious tasks on a remote Windows endpoint using the at.exe command with command-line arguments. This technique is commonly used by red teams and adversaries for lateral movement and remote code execution. The at.exe binary leverages the deprecated AT protocol, which may still work on previous versions of Windows. Attackers can enable this protocol on demand by modifying a system registry key. It is important to consider potential false positives. While administrators may create scheduled tasks on remote systems, this activity is typically limited to a small set of hosts or users.\
Identifying the creation of scheduled tasks on remote endpoints is crucial for a Security Operations Center (SOC) because it indicates potential unauthorized activity or an attacker attempting to establish persistence or execute malicious code. The impact of a true positive can be significant, leading to unauthorized access, data theft, or other damaging outcomes. During triage, investigate the source and purpose of the scheduled task, inspect relevant on-disk artifacts, and analyze concurrent processes to identify the extent of the attack and take appropriate response actions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053", "T1053.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of suspicious tasks on a remote Windows endpoint using the at.exe command with command-line arguments. This technique is commonly used by red teams and adversaries for lateral movement and remote code execution. The at.exe binary leverages the deprecated AT protocol, which may still work on previous versions of Windows. Attackers can enable this protocol on demand by modifying a system registry key. It is important to consider potential false positives. While administrators may create scheduled tasks on remote systems, this activity is typically limited to a small set of hosts or users.\
Identifying the creation of scheduled tasks on remote endpoints is crucial for a Security Operations Center (SOC) because it indicates potential unauthorized activity or an attacker attempting to establish persistence or execute malicious code. The impact of a true positive can be significant, leading to unauthorized access, data theft, or other damaging outcomes. During triage, investigate the source and purpose of the scheduled task, inspect relevant on-disk artifacts, and analyze concurrent processes to identify the extent of the attack and take appropriate response actions.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may create scheduled tasks on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-11
action.escu.modification_date = 2021-11-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Scheduled Task Creation on Remote Endpoint using At - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A Windows Scheduled Task was created on a remote endpoint from $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Scheduled Task Creation on Remote Endpoint using At - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1053", "T1053.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of suspicious tasks on a remote Windows endpoint using the at.exe command with command-line arguments. This technique is commonly used by red teams and adversaries for lateral movement and remote code execution. The at.exe binary leverages the deprecated AT protocol, which may still work on previous versions of Windows. Attackers can enable this protocol on demand by modifying a system registry key. It is important to consider potential false positives. While administrators may create scheduled tasks on remote systems, this activity is typically limited to a small set of hosts or users.\
Identifying the creation of scheduled tasks on remote endpoints is crucial for a Security Operations Center (SOC) because it indicates potential unauthorized activity or an attacker attempting to establish persistence or execute malicious code. The impact of a true positive can be significant, leading to unauthorized access, data theft, or other damaging outcomes. During triage, investigate the source and purpose of the scheduled task, inspect relevant on-disk artifacts, and analyze concurrent processes to identify the extent of the attack and take appropriate response actions.
action.notable.param.rule_title = Scheduled Task Creation on Remote Endpoint using At
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=at.exe OR Processes.original_file_name=at.exe) (Processes.process=*\\\\*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_creation_on_remote_endpoint_using_at_filter`

[ESCU - Scheduled Task Deleted Or Created via CMD - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic focuses on identifying the creation or deletion of scheduled tasks using the schtasks.exe utility with the corresponding command-line flags (-create or -delete). This technique has been notably associated with threat actors like Dragonfly and the SUNBURST attack against SolarWinds. The purpose of this analytic is to detect suspicious activity related to scheduled tasks that could indicate malicious intent or unauthorized system manipulation. By monitoring for these specific command-line flags, we can enhance our ability to identify potential threats and prevent attacks similar to the use of scheduled tasks in the BadRabbit Ransomware incident.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic focuses on identifying the creation or deletion of scheduled tasks using the schtasks.exe utility with the corresponding command-line flags (-create or -delete). This technique has been notably associated with threat actors like Dragonfly and the SUNBURST attack against SolarWinds. The purpose of this analytic is to detect suspicious activity related to scheduled tasks that could indicate malicious intent or unauthorized system manipulation. By monitoring for these specific command-line flags, we can enhance our ability to identify potential threats and prevent attacks similar to the use of scheduled tasks in the BadRabbit Ransomware incident.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = While it is possible for legitimate scripts or administrators to trigger this behavior, filtering can be applied based on the parent process and application to reduce false positives. Analysts should reference the provided references to understand the context and threat landscape associated with this activity.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Scheduled Task Deleted Or Created via CMD - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot", "NOBELIUM Group", "Windows Persistence Techniques", "Winter Vivern", "Prestige Ransomware", "DarkCrystal RAT", "AgentTesla", "Trickbot", "AsyncRAT", "Sandworm Tools", "Living Off The Land", "CISA AA22-257A", "DHS Report TA18-074A", "Azorult", "Amadey", "Scheduled Tasks", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = A schedule task process $process_name$ with create or delete commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Scheduled Task Deleted Or Created via CMD - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot", "NOBELIUM Group", "Windows Persistence Techniques", "Winter Vivern", "Prestige Ransomware", "DarkCrystal RAT", "AgentTesla", "Trickbot", "AsyncRAT", "Sandworm Tools", "Living Off The Land", "CISA AA22-257A", "DHS Report TA18-074A", "Azorult", "Amadey", "Scheduled Tasks", "NjRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic focuses on identifying the creation or deletion of scheduled tasks using the schtasks.exe utility with the corresponding command-line flags (-create or -delete). This technique has been notably associated with threat actors like Dragonfly and the SUNBURST attack against SolarWinds. The purpose of this analytic is to detect suspicious activity related to scheduled tasks that could indicate malicious intent or unauthorized system manipulation. By monitoring for these specific command-line flags, we can enhance our ability to identify potential threats and prevent attacks similar to the use of scheduled tasks in the BadRabbit Ransomware incident.
action.notable.param.rule_title = Scheduled Task Deleted Or Created via CMD
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter` 

[ESCU - Scheduled Task Initiation on Remote Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects instances of 'schtasks.exe' being used to start a Scheduled Task on a remote endpoint. Adversaries often abuse the Task Scheduler for lateral movement and remote code execution. The search parameters include process details such as the process  name, parent process, and command-line executions. Although legitimate administrators may start scheduled tasks on remote systems, this activity is usually limited to a small set of hosts or users. The findings from this analytic provide valuable insight into potentially malicious activities on an endpoint.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects instances of 'schtasks.exe' being used to start a Scheduled Task on a remote endpoint. Adversaries often abuse the Task Scheduler for lateral movement and remote code execution. The search parameters include process details such as the process  name, parent process, and command-line executions. Although legitimate administrators may start scheduled tasks on remote systems, this activity is usually limited to a small set of hosts or users. The findings from this analytic provide valuable insight into potentially malicious activities on an endpoint.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may start scheduled tasks on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-11
action.escu.modification_date = 2021-11-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Scheduled Task Initiation on Remote Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A Windows Scheduled Task was ran on a remote endpoint from $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Scheduled Task Initiation on Remote Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects instances of 'schtasks.exe' being used to start a Scheduled Task on a remote endpoint. Adversaries often abuse the Task Scheduler for lateral movement and remote code execution. The search parameters include process details such as the process  name, parent process, and command-line executions. Although legitimate administrators may start scheduled tasks on remote systems, this activity is usually limited to a small set of hosts or users. The findings from this analytic provide valuable insight into potentially malicious activities on an endpoint.
action.notable.param.rule_title = Scheduled Task Initiation on Remote Endpoint
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=schtasks.exe OR Processes.original_file_name=schtasks.exe) (Processes.process=*/s* AND Processes.process=*/run*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_initiation_on_remote_endpoint_filter`

[ESCU - Schtasks Run Task On Demand - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect when a Windows Scheduled Task is executed on demand via shell or command line. Adversaries often force the execution of their created Scheduled Tasks for persistent access or lateral movement within a compromised machine. This analytic is driven by process-related data, specifically process name, parent process, and command-line executions, sourced from endpoint logs. The search criteria focus on 'schtasks.exe' with an associated 'run' command.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to detect when a Windows Scheduled Task is executed on demand via shell or command line. Adversaries often force the execution of their created Scheduled Tasks for persistent access or lateral movement within a compromised machine. This analytic is driven by process-related data, specifically process name, parent process, and command-line executions, sourced from endpoint logs. The search criteria focus on 'schtasks.exe' with an associated 'run' command.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Bear in mind, administrators debugging Scheduled Task entries may trigger this analytic, necessitating fine-tuning and filtering to distinguish between legitimate and potentially malicious use of 'schtasks.exe'.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Schtasks Run Task On Demand - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot", "Industroyer2", "XMRig", "CISA AA22-257A", "Data Destruction", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A "on demand" execution of schedule task process $process_name$  using commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Schtasks Run Task On Demand - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot", "Industroyer2", "XMRig", "CISA AA22-257A", "Data Destruction", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect when a Windows Scheduled Task is executed on demand via shell or command line. Adversaries often force the execution of their created Scheduled Tasks for persistent access or lateral movement within a compromised machine. This analytic is driven by process-related data, specifically process name, parent process, and command-line executions, sourced from endpoint logs. The search criteria focus on 'schtasks.exe' with an associated 'run' command.
action.notable.param.rule_title = Schtasks Run Task On Demand
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_run_task_on_demand_filter`

[ESCU - Schtasks scheduling job on remote system - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect suspicious command-line arguments executed through 'schtasks.exe' to create a scheduled task on a remote endpoint. The analytic scans process data, checking for instances where 'schtasks.exe' has been used with specific command-line flags that suggest an attempt at lateral movement or remote code execution, common techniques employed by adversaries and red teams. Key data points include the process name, the specific command line used, the parent process name, the target destination, and the user involved. Also, timestamp data gives context to when these activities occurred.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to detect suspicious command-line arguments executed through 'schtasks.exe' to create a scheduled task on a remote endpoint. The analytic scans process data, checking for instances where 'schtasks.exe' has been used with specific command-line flags that suggest an attempt at lateral movement or remote code execution, common techniques employed by adversaries and red teams. Key data points include the process name, the specific command line used, the parent process name, the target destination, and the user involved. Also, timestamp data gives context to when these activities occurred.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = While it is possible to have false positives, due to legitimate administrative tasks, these are usually limited and should still be validated and investigated as appropriate.
action.escu.creation_date = 2022-05-23
action.escu.modification_date = 2022-05-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "NOBELIUM Group", "Living Off The Land", "Prestige Ransomware", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A schedule task process $process_name$ with remote job command-line $process$ in host $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Schtasks scheduling job on remote system - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "NOBELIUM Group", "Living Off The Land", "Prestige Ransomware", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect suspicious command-line arguments executed through 'schtasks.exe' to create a scheduled task on a remote endpoint. The analytic scans process data, checking for instances where 'schtasks.exe' has been used with specific command-line flags that suggest an attempt at lateral movement or remote code execution, common techniques employed by adversaries and red teams. Key data points include the process name, the specific command line used, the parent process name, the target destination, and the user involved. Also, timestamp data gives context to when these activities occurred.
action.notable.param.rule_title = Schtasks scheduling job on remote system
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = schtasks.exe OR Processes.original_file_name=schtasks.exe) (Processes.process="*/create*" AND Processes.process="*/s*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter`

[ESCU - Schtasks used for forcing a reboot - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes a Splunk query to pinpoint potential threats by monitoring the 'schtasks.exe' command-line usage. This particular command, especially when used in tandem with 'shutdown' and '/create' flags, can suggest an adversarial force intending to schedule unwarranted system reboots. The query focuses on endpoint process data and retrieves details such as the process name, the parent process name, the destination, and the user involved. Essential to the investigation are the earliest and latest timestamps of these events, providing an activity timeline. Data such as the targeted host and initiating user offer valuable context for analyst.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic utilizes a Splunk query to pinpoint potential threats by monitoring the 'schtasks.exe' command-line usage. This particular command, especially when used in tandem with 'shutdown' and '/create' flags, can suggest an adversarial force intending to schedule unwarranted system reboots. The query focuses on endpoint process data and retrieves details such as the process name, the parent process name, the destination, and the user involved. Essential to the investigation are the earliest and latest timestamps of these events, providing an activity timeline. Data such as the targeted host and initiating user offer valuable context for analyst.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This analytic may also capture legitimate administrative activities such as system updates or maintenance tasks, which can be classified as false positives. Filter as needed.
action.escu.creation_date = 2020-12-07
action.escu.modification_date = 2020-12-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Schtasks used for forcing a reboot - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A schedule task process $process_name$ with force reboot commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Schtasks used for forcing a reboot - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Ransomware", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes a Splunk query to pinpoint potential threats by monitoring the 'schtasks.exe' command-line usage. This particular command, especially when used in tandem with 'shutdown' and '/create' flags, can suggest an adversarial force intending to schedule unwarranted system reboots. The query focuses on endpoint process data and retrieves details such as the process name, the parent process name, the destination, and the user involved. Essential to the investigation are the earliest and latest timestamps of these events, providing an activity timeline. Data such as the targeted host and initiating user offer valuable context for analyst.
action.notable.param.rule_title = Schtasks used for forcing a reboot
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter`

[ESCU - Screensaver Event Trigger Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect possible event trigger execution through screensaver registry entry modification for persistence or privilege escalation. This technique was seen in several APT and malware where they put the malicious payload path to the SCRNSAVE.EXE registry key to redirect the execution to their malicious payload path. This TTP is a good indicator that some attacker may modify this entry for their persistence and privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546", "T1546.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect possible event trigger execution through screensaver registry entry modification for persistence or privilege escalation. This technique was seen in several APT and malware where they put the malicious payload path to the SCRNSAVE.EXE registry key to redirect the execution to their malicious payload path. This TTP is a good indicator that some attacker may modify this entry for their persistence and privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Screensaver Event Trigger Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hermetic Wiper", "Windows Privilege Escalation", "Windows Persistence Techniques", "Windows Registry Abuse", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $Registry.registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Screensaver Event Trigger Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Windows Privilege Escalation", "Windows Persistence Techniques", "Windows Registry Abuse", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1546", "T1546.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is developed to detect possible event trigger execution through screensaver registry entry modification for persistence or privilege escalation. This technique was seen in several APT and malware where they put the malicious payload path to the SCRNSAVE.EXE registry key to redirect the execution to their malicious payload path. This TTP is a good indicator that some attacker may modify this entry for their persistence and privilege escalation.
action.notable.param.rule_title = Screensaver Event Trigger Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Control Panel\\Desktop\\SCRNSAVE.EXE*") by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `screensaver_event_trigger_execution_filter`

[ESCU - Script Execution via WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects any potential misuse of Windows Management Instrumentation (WMI) for malicious purposes since adversaries often use WMI to run scripts which allows them to carry out malicious activities without raising suspicion. The detection is made by monitoring the process 'scrcons.exe', which is essential to run WMI scripts. The detection is important because it proactively identifies and responds to potential threats that leverage WMI for malicious purposes that can lead to system compromise, data exfiltration, or the establishment of persistence within the environment. False positives might occur since administrators might occasionally use WMI to launch scripts for legitimate purposes. Therefore, you must distinguish between malicious and benign activities.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects any potential misuse of Windows Management Instrumentation (WMI) for malicious purposes since adversaries often use WMI to run scripts which allows them to carry out malicious activities without raising suspicion. The detection is made by monitoring the process 'scrcons.exe', which is essential to run WMI scripts. The detection is important because it proactively identifies and responds to potential threats that leverage WMI for malicious purposes that can lead to system compromise, data exfiltration, or the establishment of persistence within the environment. False positives might occur since administrators might occasionally use WMI to launch scripts for legitimate purposes. Therefore, you must distinguish between malicious and benign activities.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. Filter as needed.
action.escu.creation_date = 2020-03-16
action.escu.modification_date = 2020-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Script Execution via WMI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = A wmic.exe process $process_name$ that execute script in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Script Execution via WMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects any potential misuse of Windows Management Instrumentation (WMI) for malicious purposes since adversaries often use WMI to run scripts which allows them to carry out malicious activities without raising suspicion. The detection is made by monitoring the process 'scrcons.exe', which is essential to run WMI scripts. The detection is important because it proactively identifies and responds to potential threats that leverage WMI for malicious purposes that can lead to system compromise, data exfiltration, or the establishment of persistence within the environment. False positives might occur since administrators might occasionally use WMI to launch scripts for legitimate purposes. Therefore, you must distinguish between malicious and benign activities.
action.notable.param.rule_title = Script Execution via WMI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=scrcons.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `script_execution_via_wmi_filter` 

[ESCU - Sdclt UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious sdclt.exe registry modification. This technique is commonly seen when attacker try to bypassed UAC by using sdclt.exe application by modifying some registry that sdclt.exe tries to open or query with payload file path on it to be executed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious sdclt.exe registry modification. This technique is commonly seen when attacker try to bypassed UAC by using sdclt.exe application by modifying some registry that sdclt.exe tries to open or query with payload file path on it to be executed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited to no false positives are expected.
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Sdclt UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Sdclt UAC Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious sdclt.exe registry modification. This technique is commonly seen when attacker try to bypassed UAC by using sdclt.exe application by modifying some registry that sdclt.exe tries to open or query with payload file path on it to be executed.
action.notable.param.rule_title = Sdclt UAC Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "IsolatedCommand")) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdclt_uac_bypass_filter`

[ESCU - Sdelete Application Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect the execution of sdelete.exe application sysinternal tools. This tool is one of the most use tool of malware and adversaries to remove or clear their tracks and artifact in the targetted host. This tool is designed to delete securely a file in file system that remove the forensic evidence on the machine. A good TTP query to check why user execute this application which is not a common practice.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect the execution of sdelete.exe application sysinternal tools. This tool is one of the most use tool of malware and adversaries to remove or clear their tracks and artifact in the targetted host. This tool is designed to delete securely a file in file system that remove the forensic evidence on the machine. A good TTP query to check why user execute this application which is not a common practice.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = user may execute and use this application
action.escu.creation_date = 2021-10-06
action.escu.modification_date = 2021-10-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Sdelete Application Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Masquerading - Rename System Utilities"]
action.risk = 1
action.risk.param._risk_message = sdelete process $process_name$ executed in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Sdelete Application Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Masquerading - Rename System Utilities"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1485", "T1070.004", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect the execution of sdelete.exe application sysinternal tools. This tool is one of the most use tool of malware and adversaries to remove or clear their tracks and artifact in the targetted host. This tool is designed to delete securely a file in file system that remove the forensic evidence on the machine. A good TTP query to check why user execute this application which is not a common practice.
action.notable.param.rule_title = Sdelete Application Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_sdelete` by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdelete_application_execution_filter`

[ESCU - SearchProtocolHost with no Command Line with Network - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - SearchProtocolHost with no Command Line with Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = A searchprotocolhost.exe process $process_name$ with no commandline in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SearchProtocolHost with no Command Line with Network - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = SearchProtocolHost with no Command Line with Network
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time dest parent_process_name process_name process_path process process_id dest_port C2 | `searchprotocolhost_with_no_command_line_with_network_filter`

[ESCU - SecretDumps Offline NTDS Dumping Tool - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a potential usage of secretsdump.py tool for dumping credentials (ntlm hash) from a copy of ntds.dit and SAM.Security,SYSTEM registrry hive. This technique was seen in some attacker that dump ntlm hashes offline after having a copy of ntds.dit and SAM/SYSTEM/SECURITY registry hive.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects a potential usage of secretsdump.py tool for dumping credentials (ntlm hash) from a copy of ntds.dit and SAM.Security,SYSTEM registrry hive. This technique was seen in some attacker that dump ntlm hashes offline after having a copy of ntds.dit and SAM/SYSTEM/SECURITY registry hive.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - SecretDumps Offline NTDS Dumping Tool - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = A secretdump process $process_name$ with secretdump commandline $process$ to dump credentials in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SecretDumps Offline NTDS Dumping Tool - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a potential usage of secretsdump.py tool for dumping credentials (ntlm hash) from a copy of ntds.dit and SAM.Security,SYSTEM registrry hive. This technique was seen in some attacker that dump ntlm hashes offline after having a copy of ntds.dit and SAM/SYSTEM/SECURITY registry hive.
action.notable.param.rule_title = SecretDumps Offline NTDS Dumping Tool
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `secretdumps_offline_ntds_dumping_tool_filter`

[ESCU - ServicePrincipalNames Discovery with PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `powershell.exe` usage, using Script Block Logging EventCode 4104, related to querying the domain for Service Principle Names. typically, this is a precursor activity related to kerberoasting or the silver ticket attack. \
What is a ServicePrincipleName? \
A service principal name (SPN) is a unique identifier of a service instance. SPNs are used by Kerberos authentication to associate a service instance with a service logon account. This allows a client application to request that the service authenticate an account even if the client does not have the account name.\
The following analytic identifies the use of KerberosRequestorSecurityToken class within the script block. Using .NET System.IdentityModel.Tokens.KerberosRequestorSecurityToken class in PowerShell is the equivelant of using setspn.exe. \
During triage, review parallel processes for further suspicious activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies `powershell.exe` usage, using Script Block Logging EventCode 4104, related to querying the domain for Service Principle Names. typically, this is a precursor activity related to kerberoasting or the silver ticket attack. \
What is a ServicePrincipleName? \
A service principal name (SPN) is a unique identifier of a service instance. SPNs are used by Kerberos authentication to associate a service instance with a service logon account. This allows a client application to request that the service authenticate an account even if the client does not have the account name.\
The following analytic identifies the use of KerberosRequestorSecurityToken class within the script block. Using .NET System.IdentityModel.Tokens.KerberosRequestorSecurityToken class in PowerShell is the equivelant of using setspn.exe. \
During triage, review parallel processes for further suspicious activity.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited, however filter as needed.
action.escu.creation_date = 2022-02-26
action.escu.modification_date = 2022-02-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - ServicePrincipalNames Discovery with PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery", "Active Directory Kerberos Attacks", "Malicious PowerShell", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = An instance of attempting to identify service principle detected on $dest$ names.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ServicePrincipalNames Discovery with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Active Directory Kerberos Attacks", "Malicious PowerShell", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `powershell.exe` usage, using Script Block Logging EventCode 4104, related to querying the domain for Service Principle Names. typically, this is a precursor activity related to kerberoasting or the silver ticket attack. \
What is a ServicePrincipleName? \
A service principal name (SPN) is a unique identifier of a service instance. SPNs are used by Kerberos authentication to associate a service instance with a service logon account. This allows a client application to request that the service authenticate an account even if the client does not have the account name.\
The following analytic identifies the use of KerberosRequestorSecurityToken class within the script block. Using .NET System.IdentityModel.Tokens.KerberosRequestorSecurityToken class in PowerShell is the equivelant of using setspn.exe. \
During triage, review parallel processes for further suspicious activity.
action.notable.param.rule_title = ServicePrincipalNames Discovery with PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*KerberosRequestorSecurityToken*" | stats count min(_time) as firstTime max(_time) as lastTime by ScriptBlockText Opcode Computer UserID EventCode | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `serviceprincipalnames_discovery_with_powershell_filter`

[ESCU - ServicePrincipalNames Discovery with SetSPN - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `setspn.exe` usage related to querying the domain for Service Principle Names. typically, this is a precursor activity related to kerberoasting or the silver ticket attack. \
What is a ServicePrincipleName? \
A service principal name (SPN) is a unique identifier of a service instance. SPNs are used by Kerberos authentication to associate a service instance with a service logon account. This allows a client application to request that the service authenticate an account even if the client does not have the account name.\
Example usage includes the following \
1. setspn -T offense -Q */* 1. setspn -T attackrange.local -F -Q MSSQLSvc/* 1. setspn -Q */* > allspns.txt 1. setspn -q \
Values \
1. -F = perform queries at the forest, rather than domain level 1. -T = perform query on the specified domain or forest (when -F is also used) 1. -Q = query for existence of SPN \
During triage, review parallel processes for further suspicious activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `setspn.exe` usage related to querying the domain for Service Principle Names. typically, this is a precursor activity related to kerberoasting or the silver ticket attack. \
What is a ServicePrincipleName? \
A service principal name (SPN) is a unique identifier of a service instance. SPNs are used by Kerberos authentication to associate a service instance with a service logon account. This allows a client application to request that the service authenticate an account even if the client does not have the account name.\
Example usage includes the following \
1. setspn -T offense -Q */* 1. setspn -T attackrange.local -F -Q MSSQLSvc/* 1. setspn -Q */* > allspns.txt 1. setspn -q \
Values \
1. -F = perform queries at the forest, rather than domain level 1. -T = perform query on the specified domain or forest (when -F is also used) 1. -Q = query for existence of SPN \
During triage, review parallel processes for further suspicious activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be caused by Administrators resetting SPNs or querying for SPNs. Filter as needed.
action.escu.creation_date = 2021-10-14
action.escu.modification_date = 2021-10-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - ServicePrincipalNames Discovery with SetSPN - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to identify service principle names.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - ServicePrincipalNames Discovery with SetSPN - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `setspn.exe` usage related to querying the domain for Service Principle Names. typically, this is a precursor activity related to kerberoasting or the silver ticket attack. \
What is a ServicePrincipleName? \
A service principal name (SPN) is a unique identifier of a service instance. SPNs are used by Kerberos authentication to associate a service instance with a service logon account. This allows a client application to request that the service authenticate an account even if the client does not have the account name.\
Example usage includes the following \
1. setspn -T offense -Q */* 1. setspn -T attackrange.local -F -Q MSSQLSvc/* 1. setspn -Q */* > allspns.txt 1. setspn -q \
Values \
1. -F = perform queries at the forest, rather than domain level 1. -T = perform query on the specified domain or forest (when -F is also used) 1. -Q = query for existence of SPN \
During triage, review parallel processes for further suspicious activity.
action.notable.param.rule_title = ServicePrincipalNames Discovery with SetSPN
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_setspn` (Processes.process="*-t*" AND Processes.process="*-f*") OR (Processes.process="*-q*" AND Processes.process="**/**") OR (Processes.process="*-q*") OR (Processes.process="*-s*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `serviceprincipalnames_discovery_with_setspn_filter`

[ESCU - Services Escalate Exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of `svc-exe` with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using `svc-exe`, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within `C:\Windows\400619a.exe`. Following, the binary will be added to the registry under key `HKLM\System\CurrentControlSet\Services\400619a\` with multiple keys and values added to look like a legitimate service. Upon loading, `services.exe` will spawn the randomly named binary from `\\127.0.0.1\ADMIN$\400619a.exe`. The process lineage is completed with `400619a.exe` spawning rundll32.exe, which is the default `spawnto_` value for Cobalt Strike. The `spawnto_` value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The `spawnto_` process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of `svc-exe` with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using `svc-exe`, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within `C:\Windows\400619a.exe`. Following, the binary will be added to the registry under key `HKLM\System\CurrentControlSet\Services\400619a\` with multiple keys and values added to look like a legitimate service. Upon loading, `services.exe` will spawn the randomly named binary from `\\127.0.0.1\ADMIN$\400619a.exe`. The process lineage is completed with `400619a.exe` spawning rundll32.exe, which is the default `spawnto_` value for Cobalt Strike. The `spawnto_` value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The `spawnto_` process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as `services.exe` should never spawn a process from `ADMIN$`. Filter as needed.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Services Escalate Exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = A service process $parent_process_name$ with process path $process_path$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "Processes.dest", "risk_object_type": "system", "risk_score": 76}, {"risk_object_field": "Processes.user", "risk_object_type": "user", "risk_score": 76}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Services Escalate Exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 95, "impact": 80, "mitre_attack": ["T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of `svc-exe` with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using `svc-exe`, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within `C:\Windows\400619a.exe`. Following, the binary will be added to the registry under key `HKLM\System\CurrentControlSet\Services\400619a\` with multiple keys and values added to look like a legitimate service. Upon loading, `services.exe` will spawn the randomly named binary from `\\127.0.0.1\ADMIN$\400619a.exe`. The process lineage is completed with `400619a.exe` spawning rundll32.exe, which is the default `spawnto_` value for Cobalt Strike. The `spawnto_` value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The `spawnto_` process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications.
action.notable.param.rule_title = Services Escalate Exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe Processes.process_path=*admin$* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_escalate_exe_filter`

[ESCU - Services LOLBAS Execution Process Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `services.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Service Control Manager and creating a remote malicious service, the executed command is spawned as a child process of `services.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of services.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `services.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Service Control Manager and creating a remote malicious service, the executed command is spawned as a child process of `services.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of services.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate applications may trigger this behavior, filter as needed.
action.escu.creation_date = 2021-11-22
action.escu.modification_date = 2021-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Services LOLBAS Execution Process Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Living Off The Land", "Qakbot"]
action.risk = 1
action.risk.param._risk_message = Services.exe spawned a LOLBAS process on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Services LOLBAS Execution Process Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Living Off The Land", "Qakbot"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `services.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Service Control Manager and creating a remote malicious service, the executed command is spawned as a child process of `services.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of services.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.notable.param.rule_title = Services LOLBAS Execution Process Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=services.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_lolbas_execution_process_spawn_filter`

[ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["HAFNIUM Group", "Hermetic Wiper", "Credential Dumping", "Malicious PowerShell", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = A registry modification in $registry_path$ with reg key $registry_key_name$ and reg value $registry_value_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "registry_path", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group", "Hermetic Wiper", "Credential Dumping", "Malicious PowerShell", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.
action.notable.param.rule_title = Set Default PowerShell Execution Policy To Unrestricted or Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_value_name=ExecutionPolicy (Registry.registry_value_data=Unrestricted OR Registry.registry_value_data=Bypass)) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`

[ESCU - Shim Database File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
action.escu.known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.
action.escu.creation_date = 2020-12-08
action.escu.modification_date = 2020-12-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Shim Database File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A process that possibly write shim database in $file_path$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "file_path", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Shim Database File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
action.notable.param.rule_title = Shim Database File Creation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`

[ESCU - Shim Database Installation With Suspicious Parameters - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects the process execution and arguments required to silently create a shim database.  The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search detects the process execution and arguments required to silently create a shim database.  The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2020-11-23
action.escu.modification_date = 2020-11-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Shim Database Installation With Suspicious Parameters - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ that possible create a shim db silently in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Shim Database Installation With Suspicious Parameters - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects the process execution and arguments required to silently create a shim database.  The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
action.notable.param.rule_title = Shim Database Installation With Suspicious Parameters
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter`

[ESCU - Short Lived Scheduled Task - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Windows Security EventCode 4698, "A scheduled task was created," and EventCode 4699, "A scheduled task was deleted," to identify scheduled tasks that are created and deleted within a short time frame of less than 30 seconds. This behavior is indicative of a potential lateral movement attack where the Task Scheduler is abused to achieve code execution. Both red teams and adversaries may exploit the Task Scheduler for lateral movement and remote code execution.\
To implement this analytic, ensure that you are ingesting Windows Security Event Logs with EventCode 4698 enabled. Additionally, the Windows TA (Technology Add-on) is required to parse and extract the necessary information from the logs.\
It's important to note that while uncommon, legitimate applications may create and delete scheduled tasks within a short duration. Analysts should filter the results based on the specific context and environment to reduce false positives.\
Identifying short-lived scheduled tasks is valuable for a SOC as it can indicate malicious activities attempting to move laterally or execute unauthorized code on Windows systems. By detecting and investigating these events, security analysts can respond promptly to prevent further compromise and mitigate potential risks. The impact of a true positive could range from unauthorized access to data exfiltration or the execution of malicious payloads.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Windows Security EventCode 4698, "A scheduled task was created," and EventCode 4699, "A scheduled task was deleted," to identify scheduled tasks that are created and deleted within a short time frame of less than 30 seconds. This behavior is indicative of a potential lateral movement attack where the Task Scheduler is abused to achieve code execution. Both red teams and adversaries may exploit the Task Scheduler for lateral movement and remote code execution.\
To implement this analytic, ensure that you are ingesting Windows Security Event Logs with EventCode 4698 enabled. Additionally, the Windows TA (Technology Add-on) is required to parse and extract the necessary information from the logs.\
It's important to note that while uncommon, legitimate applications may create and delete scheduled tasks within a short duration. Analysts should filter the results based on the specific context and environment to reduce false positives.\
Identifying short-lived scheduled tasks is valuable for a SOC as it can indicate malicious activities attempting to move laterally or execute unauthorized code on Windows systems. By detecting and investigating these events, security analysts can respond promptly to prevent further compromise and mitigate potential risks. The impact of a true positive could range from unauthorized access to data exfiltration or the execution of malicious payloads.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = Although uncommon, legitimate applications may create and delete a Scheduled Task within 30 seconds. Filter as needed.
action.escu.creation_date = 2021-12-03
action.escu.modification_date = 2021-12-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Short Lived Scheduled Task - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "CISA AA22-257A", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A windows scheduled task was created and deleted in 30 seconds on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Short Lived Scheduled Task - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "CISA AA22-257A", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1053.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Windows Security EventCode 4698, "A scheduled task was created," and EventCode 4699, "A scheduled task was deleted," to identify scheduled tasks that are created and deleted within a short time frame of less than 30 seconds. This behavior is indicative of a potential lateral movement attack where the Task Scheduler is abused to achieve code execution. Both red teams and adversaries may exploit the Task Scheduler for lateral movement and remote code execution.\
To implement this analytic, ensure that you are ingesting Windows Security Event Logs with EventCode 4698 enabled. Additionally, the Windows TA (Technology Add-on) is required to parse and extract the necessary information from the logs.\
It's important to note that while uncommon, legitimate applications may create and delete scheduled tasks within a short duration. Analysts should filter the results based on the specific context and environment to reduce false positives.\
Identifying short-lived scheduled tasks is valuable for a SOC as it can indicate malicious activities attempting to move laterally or execute unauthorized code on Windows systems. By detecting and investigating these events, security analysts can respond promptly to prevent further compromise and mitigate potential risks. The impact of a true positive could range from unauthorized access to data exfiltration or the execution of malicious payloads.
action.notable.param.rule_title = Short Lived Scheduled Task
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4698 OR EventCode=4699 | xmlkv Message | transaction Task_Name  startswith=(EventCode=4698) endswith=(EventCode=4699) | eval short_lived=case((duration<30),"TRUE") | search  short_lived = TRUE | rename ComputerName as dest| table _time, dest, Account_Name, Command, Task_Name, short_lived | `short_lived_scheduled_task_filter` 

[ESCU - Short Lived Windows Accounts - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation and deletion of accounts in a short time period to identify potential threats earlier and take appropriate actions to mitigate the risks. Helps prevent or minimize the potential damage caused by unauthorized access or malicious activities within the environment. This detection is made by a Splunk query that searches for events with the result IDs 4720 and 4726 in the "Change" data model. The query then groups the results by time, user, and destination. The result is filtered to only include events with the specified result IDs. The "transaction" command is used to group events that occur within a specified time span and have the same user but are not connected. Finally, the relevant information such as the first and last time of the event, the count, user, destination, and result ID are displayed in a table. This detection is important because it suggests that an attacker is attempting to create and delete accounts rapidly, potentially to cover their tracks or gain unauthorized access. The impact of such an attack can include unauthorized access to sensitive data, privilege escalation, or the ability to carry out further malicious activities within the environment. Next steps include investigating the events flagged by the analytic, review the account creation and deletion activities, and analyze any associated logs or artifacts to determine the intent and impact of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic detects the creation and deletion of accounts in a short time period to identify potential threats earlier and take appropriate actions to mitigate the risks. Helps prevent or minimize the potential damage caused by unauthorized access or malicious activities within the environment. This detection is made by a Splunk query that searches for events with the result IDs 4720 and 4726 in the "Change" data model. The query then groups the results by time, user, and destination. The result is filtered to only include events with the specified result IDs. The "transaction" command is used to group events that occur within a specified time span and have the same user but are not connected. Finally, the relevant information such as the first and last time of the event, the count, user, destination, and result ID are displayed in a table. This detection is important because it suggests that an attacker is attempting to create and delete accounts rapidly, potentially to cover their tracks or gain unauthorized access. The impact of such an attack can include unauthorized access to sensitive data, privilege escalation, or the ability to carry out further malicious activities within the environment. Next steps include investigating the events flagged by the analytic, review the account creation and deletion activities, and analyze any associated logs or artifacts to determine the intent and impact of the attack.
action.escu.how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/
action.escu.known_false_positives = It is possible that an administrator created and deleted an account in a short time period.  Verifying activity with an administrator is advised.
action.escu.creation_date = 2020-07-06
action.escu.modification_date = 2020-07-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Short Lived Windows Accounts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.risk = 1
action.risk.param._risk_message = A user account created or delete shortly in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Short Lived Windows Accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation and deletion of accounts in a short time period to identify potential threats earlier and take appropriate actions to mitigate the risks. Helps prevent or minimize the potential damage caused by unauthorized access or malicious activities within the environment. This detection is made by a Splunk query that searches for events with the result IDs 4720 and 4726 in the "Change" data model. The query then groups the results by time, user, and destination. The result is filtered to only include events with the specified result IDs. The "transaction" command is used to group events that occur within a specified time span and have the same user but are not connected. Finally, the relevant information such as the first and last time of the event, the count, user, destination, and result ID are displayed in a table. This detection is important because it suggests that an attacker is attempting to create and delete accounts rapidly, potentially to cover their tracks or gain unauthorized access. The impact of such an attack can include unauthorized access to sensitive data, privilege escalation, or the ability to carry out further malicious activities within the environment. Next steps include investigating the events flagged by the analytic, review the account creation and deletion activities, and analyze any associated logs or artifacts to determine the intent and impact of the attack.
action.notable.param.rule_title = Short Lived Windows Accounts
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter`

[ESCU - SilentCleanup UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious modification of registry that may related to UAC bypassed. This registry will be trigger once the attacker abuse the silentcleanup task schedule to gain high privilege execution that will bypass User control account.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious modification of registry that may related to UAC bypassed. This registry will be trigger once the attacker abuse the silentcleanup task schedule to gain high privilege execution that will bypass User control account.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - SilentCleanup UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SilentCleanup UAC Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious modification of registry that may related to UAC bypassed. This registry will be trigger once the attacker abuse the silentcleanup task schedule to gain high privilege execution that will bypass User control account.
action.notable.param.rule_title = SilentCleanup UAC Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_data = "*.exe*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter`

[ESCU - Single Letter Process On Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a behavior where a process name consists only of a single letter that helps to detect potential threats earlier and mitigate the risks. This detection is important because it indicates the presence of malware or an attacker attempting to evade detection by using a process name that is difficult to identify or track so that he can carry out malicious activities such as data theft or ransomware attacks. False positives might occur since there might be legitimate uses of single-letter process names in your environment. Next steps include reviewing the process details and investigating any suspicious activity upon triage.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects a behavior where a process name consists only of a single letter that helps to detect potential threats earlier and mitigate the risks. This detection is important because it indicates the presence of malware or an attacker attempting to evade detection by using a process name that is difficult to identify or track so that he can carry out malicious activities such as data theft or ransomware attacks. False positives might occur since there might be legitimate uses of single-letter process names in your environment. Next steps include reviewing the process details and investigating any suspicious activity upon triage.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process.
action.escu.creation_date = 2020-12-08
action.escu.modification_date = 2020-12-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Single Letter Process On Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DHS Report TA18-074A"]
action.risk = 1
action.risk.param._risk_message = A suspicious process $process_name$ with single letter in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Single Letter Process On Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1204", "T1204.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a behavior where a process name consists only of a single letter that helps to detect potential threats earlier and mitigate the risks. This detection is important because it indicates the presence of malware or an attacker attempting to evade detection by using a process name that is difficult to identify or track so that he can carry out malicious activities such as data theft or ransomware attacks. False positives might occur since there might be legitimate uses of single-letter process names in your environment. Next steps include reviewing the process details and investigating any suspicious activity upon triage.
action.notable.param.rule_title = Single Letter Process On Endpoint
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter`

[ESCU - SLUI RunAs Elevated - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, elevating access using the `-verb runas` function. This particular bypass utilizes a registry key/value. Identified by two sources, the registry keys are `HKCU\Software\Classes\exefile\shell` and `HKCU\Software\Classes\launcher.Systemsettings\Shell\open\command`. To simulate this behavior, multiple POC are available. The analytic identifies the use of `runas` by `slui.exe`.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, elevating access using the `-verb runas` function. This particular bypass utilizes a registry key/value. Identified by two sources, the registry keys are `HKCU\Software\Classes\exefile\shell` and `HKCU\Software\Classes\launcher.Systemsettings\Shell\open\command`. To simulate this behavior, multiple POC are available. The analytic identifies the use of `runas` by `slui.exe`.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present as this is not commonly used by legitimate applications.
action.escu.creation_date = 2021-05-13
action.escu.modification_date = 2021-05-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - SLUI RunAs Elevated - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkSide Ransomware", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = A slui process $process_name$ with elevated commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SLUI RunAs Elevated - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, elevating access using the `-verb runas` function. This particular bypass utilizes a registry key/value. Identified by two sources, the registry keys are `HKCU\Software\Classes\exefile\shell` and `HKCU\Software\Classes\launcher.Systemsettings\Shell\open\command`. To simulate this behavior, multiple POC are available. The analytic identifies the use of `runas` by `slui.exe`.
action.notable.param.rule_title = SLUI RunAs Elevated
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=slui.exe (Processes.process=*-verb* Processes.process=*runas*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `slui_runas_elevated_filter`

[ESCU - SLUI Spawning a Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, spawning a child process. This behavior is associated with publicly known UAC bypass. `slui.exe` is commonly associated with software updates and is most often spawned by `svchost.exe`. The `slui.exe` process should not have child processes, and any processes spawning from it will be running with elevated privileges. During triage, review the child process and additional parallel processes. Identify any file modifications that may have lead to the bypass.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, spawning a child process. This behavior is associated with publicly known UAC bypass. `slui.exe` is commonly associated with software updates and is most often spawned by `svchost.exe`. The `slui.exe` process should not have child processes, and any processes spawning from it will be running with elevated privileges. During triage, review the child process and additional parallel processes. Identify any file modifications that may have lead to the bypass.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Certain applications may spawn from `slui.exe` that are legitimate. Filtering will be needed to ensure proper monitoring.
action.escu.creation_date = 2021-05-13
action.escu.modification_date = 2021-05-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - SLUI Spawning a Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkSide Ransomware", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = A slui process $parent_process_name$ spawning child process $process_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SLUI Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, spawning a child process. This behavior is associated with publicly known UAC bypass. `slui.exe` is commonly associated with software updates and is most often spawned by `svchost.exe`. The `slui.exe` process should not have child processes, and any processes spawning from it will be running with elevated privileges. During triage, review the child process and additional parallel processes. Identify any file modifications that may have lead to the bypass.
action.notable.param.rule_title = SLUI Spawning a Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=slui.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `slui_spawning_a_process_filter`

[ESCU - Spike in File Writes - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for a sharp increase in the number of files written to a particular host
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for a sharp increase in the number of files written to a particular host
action.escu.how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system.
action.escu.known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications.
action.escu.creation_date = 2020-03-16
action.escu.modification_date = 2020-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spike in File Writes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spike in File Writes - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime, "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"), count, null))) as stdev by "dest" | eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` 

[ESCU - Spoolsv Spawning Rundll32 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious child process, `rundll32.exe`, with no command-line arguments being spawned from `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to spawn a process. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious child process, `rundll32.exe`, with no command-line arguments being spawned from `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to spawn a process. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives have been identified. There are limited instances where `rundll32.exe` may be spawned by a legitimate print driver.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spoolsv Spawning Rundll32 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $parent_process_name$ has spawned $process_name$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "parent_process_id", "threat_object_type": "process"}, {"risk_object_field": "process_id", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spoolsv Spawning Rundll32 - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527"], "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious child process, `rundll32.exe`, with no command-line arguments being spawned from `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to spawn a process. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.notable.param.rule_title = Spoolsv Spawning Rundll32
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe `process_rundll32` by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_spawning_rundll32_filter`

[ESCU - Spoolsv Suspicious Loaded Modules - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect suspicious loading of dll in specific path relative to printnightmare exploitation. In this search we try to detect the loaded modules made by spoolsv.exe after the exploitation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect suspicious loading of dll in specific path relative to printnightmare exploitation. In this search we try to detect the loaded modules made by spoolsv.exe after the exploitation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spoolsv Suspicious Loaded Modules - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $Image$ with process id $ProcessId$ has loaded a driver from $ImageLoaded$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spoolsv Suspicious Loaded Modules - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527"], "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect suspicious loading of dll in specific path relative to printnightmare exploitation. In this search we try to detect the loaded modules made by spoolsv.exe after the exploitation.
action.notable.param.rule_title = Spoolsv Suspicious Loaded Modules
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) as ImgLoaded count min(_time) as firstTime max(_time) as lastTime by Image Computer ProcessId EventCode | rename Computer as dest | where countImgloaded >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter`

[ESCU - Spoolsv Suspicious Process Access - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a suspicious behavior related to PrintNightmare, or CVE-2021-34527 previously (CVE-2021-1675),  to gain privilege escalation on the vulnerable machine. This exploit attacks a critical Windows Print Spooler Vulnerability to elevate privilege. This detection is to look for suspicious process access made by the spoolsv.exe that may related to the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies a suspicious behavior related to PrintNightmare, or CVE-2021-34527 previously (CVE-2021-1675),  to gain privilege escalation on the vulnerable machine. This exploit attacks a critical Windows Print Spooler Vulnerability to elevate privilege. This detection is to look for suspicious process access made by the spoolsv.exe that may related to the attack.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with process access event where SourceImage, TargetImage, GrantedAccess and CallTrace executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of spoolsv.exe.
action.escu.known_false_positives = Unknown. Filter as needed.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spoolsv Suspicious Process Access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $SourceImage$ was GrantedAccess open access to $TargetImage$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "ProcessID", "risk_object_type": "other", "risk_score": 72}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spoolsv Suspicious Process Access - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527"], "impact": 80, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a suspicious behavior related to PrintNightmare, or CVE-2021-34527 previously (CVE-2021-1675),  to gain privilege escalation on the vulnerable machine. This exploit attacks a critical Windows Print Spooler Vulnerability to elevate privilege. This detection is to look for suspicious process access made by the spoolsv.exe that may related to the attack.
action.notable.param.rule_title = Spoolsv Suspicious Process Access
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff | stats  count min(_time) as firstTime max(_time) as lastTime by dest SourceImage TargetImage GrantedAccess CallTrace  EventCode ProcessID| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_process_access_filter`

[ESCU - Spoolsv Writing a DLL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
action.escu.known_false_positives = Unknown.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spoolsv Writing a DLL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "process_id", "risk_object_type": "other", "risk_score": 72}, {"risk_object_field": "file_path", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spoolsv Writing a DLL - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527"], "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.notable.param.rule_title = Spoolsv Writing a DLL
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=spoolsv.exe by _time Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `spoolsv_writing_a_dll_filter`

[ESCU - Spoolsv Writing a DLL - Sysmon - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously(CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously(CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
action.escu.known_false_positives = Limited false positives. Filter as needed.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spoolsv Writing a DLL - Sysmon - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "process_id", "risk_object_type": "other", "risk_score": 72}, {"risk_object_field": "file_path", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spoolsv Writing a DLL - Sysmon - Rule
action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2021-34527"], "impact": 80, "mitre_attack": ["T1547.012", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously(CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.
action.notable.param.rule_title = Spoolsv Writing a DLL - Sysmon
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by dest, UserID, process_name, file_path, file_name, TargetFilename, process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter`

[ESCU - Sqlite Module In Temp Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious file creation of sqlite3.dll in %temp% folder. This behavior was seen in IcedID malware where it download sqlite module to parse browser database like for chrome or firefox to stole browser information related to bank, credit card or credentials.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1005"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious file creation of sqlite3.dll in %temp% folder. This behavior was seen in IcedID malware where it download sqlite module to parse browser database like for chrome or firefox to stole browser information related to bank, credit card or credentials.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-08-03
action.escu.modification_date = 2021-08-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Sqlite Module In Temp Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = Process $process_name$ create a file $file_name$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Sqlite Module In Temp Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious file creation of sqlite3.dll in %temp% folder. This behavior was seen in IcedID malware where it download sqlite module to parse browser database like for chrome or firefox to stole browser information related to bank, credit card or credentials.
action.notable.param.rule_title = Sqlite Module In Temp Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id process_name file_name file_path action process_guid| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sqlite_module_in_temp_folder_filter`

[ESCU - Steal or Forge Authentication Certificates Behavior Identified - Rule]
action.escu = 0
action.escu.enabled = 1
description = This correlation rule focuses on detecting potential threats associated with MITRE ATT&CK T1649 (Steal or Forge Authentication Certificates). The rule is designed to identify instances where 5 or more analytics related to Windows Certificate Services analytic story that are triggered within a specified time frame, which may indicate a potential attack in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = This correlation rule focuses on detecting potential threats associated with MITRE ATT&CK T1649 (Steal or Forge Authentication Certificates). The rule is designed to identify instances where 5 or more analytics related to Windows Certificate Services analytic story that are triggered within a specified time frame, which may indicate a potential attack in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.
action.escu.how_to_implement = The Windows Certificate Services analytic story must have 5 or more analytics enabled. In addition, ensure data is being logged that is required. Modify the correlation as needed based on volume of noise related to the other analytics.
action.escu.known_false_positives = False positives may be present based on automated tooling or system administrators. Filter as needed.
action.escu.creation_date = 2023-05-01
action.escu.modification_date = 2023-05-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Steal or Forge Authentication Certificates Behavior Identified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Certificate Services"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Steal or Forge Authentication Certificates Behavior Identified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This correlation rule focuses on detecting potential threats associated with MITRE ATT&CK T1649 (Steal or Forge Authentication Certificates). The rule is designed to identify instances where 5 or more analytics related to Windows Certificate Services analytic story that are triggered within a specified time frame, which may indicate a potential attack in progress. By aggregating these analytics, security teams can swiftly respond to and investigate any suspicious activities, enhancing their ability to protect critical assets and prevent unauthorized access to sensitive information.
action.notable.param.rule_title = RBA: Steal or Forge Authentication Certificates Behavior Identified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Windows Certificate Services" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 5 | `steal_or_forge_authentication_certificates_behavior_identified_filter`

[ESCU - Sunburst Correlation DLL and Network Event - Rule]
action.escu = 0
action.escu.enabled = 1
description = The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events.
action.escu.how_to_implement = This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-12-14
action.escu.modification_date = 2020-12-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Sunburst Correlation DLL and Network Event - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["NOBELIUM Group"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Sunburst Correlation DLL and Network Event - Rule
action.correlationsearch.annotations = {"analytic_story": ["NOBELIUM Group"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events.
action.notable.param.rule_title = Sunburst Correlation DLL and Network Event
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = (`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter` 

[ESCU - Suspicious Computer Account Name Change - Rule]
action.escu = 0
action.escu.enabled = 1
description = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries need to create a new computer account name and rename it to match the name of a domain controller account without the ending '$'. In Windows Active Directory environments, computer account names always end with `$`. This analytic leverages Event Id 4781, `The name of an account was changed`, to identify a computer account rename event with a suspicious name that does not terminate with `$`. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries need to create a new computer account name and rename it to match the name of a domain controller account without the ending '$'. In Windows Active Directory environments, computer account names always end with `$`. This analytic leverages Event Id 4781, `The name of an account was changed`, to identify a computer account rename event with a suspicious name that does not terminate with `$`. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
action.escu.known_false_positives = Renaming a computer account name to a name that not end with '$' is highly unsual and may not have any legitimate scenarios.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Computer Account Name Change - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["sAMAccountName Spoofing and Domain Controller Impersonation", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = A computer account $Old_Account_Name$ was renamed with a suspicious computer name
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Computer Account Name Change - Rule
action.correlationsearch.annotations = {"analytic_story": ["sAMAccountName Spoofing and Domain Controller Impersonation", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-42287", "CVE-2021-42278"], "impact": 100, "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries need to create a new computer account name and rename it to match the name of a domain controller account without the ending '$'. In Windows Active Directory environments, computer account names always end with `$`. This analytic leverages Event Id 4781, `The name of an account was changed`, to identify a computer account rename event with a suspicious name that does not terminate with `$`. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.notable.param.rule_title = Suspicious Computer Account Name Change
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4781 Old_Account_Name="*$" New_Account_Name!="*$" | table _time, ComputerName, Account_Name, Old_Account_Name, New_Account_Name | `suspicious_computer_account_name_change_filter`

[ESCU - Suspicious Copy on System32 - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious copy of file from systemroot folder of the windows OS. This technique is commonly used by APT or other malware as part of execution (LOLBIN) to run its malicious code using the available legitimate tool in OS. this type of event may seen or may execute of normal user in some instance but this is really a anomaly that needs to be check within the network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036.003", "T1036"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious copy of file from systemroot folder of the windows OS. This technique is commonly used by APT or other malware as part of execution (LOLBIN) to run its malicious code using the available legitimate tool in OS. this type of event may seen or may execute of normal user in some instance but this is really a anomaly that needs to be check within the network.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = every user may do this event but very un-ussual.
action.escu.creation_date = 2023-08-17
action.escu.modification_date = 2023-08-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Copy on System32 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes", "Qakbot", "IcedID", "AsyncRAT", "Sandworm Tools", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Execution of copy exe to copy file from $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Copy on System32 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes", "Qakbot", "IcedID", "AsyncRAT", "Sandworm Tools", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1036.003", "T1036"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious copy of file from systemroot folder of the windows OS. This technique is commonly used by APT or other malware as part of execution (LOLBIN) to run its malicious code using the available legitimate tool in OS. this type of event may seen or may execute of normal user in some instance but this is really a anomaly that needs to be check within the network.
action.notable.param.rule_title = Suspicious Copy on System32
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN("cmd.exe", "powershell*","pwsh.exe", "sqlps.exe", "sqltoolsps.exe", "powershell_ise.exe") AND `process_copy` AND Processes.process IN("*\\Windows\\System32\\*", "*\\Windows\\SysWow64\\*") AND Processes.process = "*copy*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id temp | `drop_dm_object_name(Processes)` | eval splitted_commandline=split(process," ") | eval first_cmdline=lower(mvindex(splitted_commandline,0)) | where NOT LIKE(first_cmdline,"%\\windows\\system32\\%") AND NOT LIKE(first_cmdline,"%\\windows\\syswow64\\%") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`suspicious_copy_on_system32_filter`

[ESCU - Suspicious Curl Network Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to Command And Control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to Command And Control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. Filter as needed.
action.escu.creation_date = 2021-02-22
action.escu.modification_date = 2021-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Curl Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Silver Sparrow", "Ingress Tool Transfer", "Linux Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Curl Network Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow", "Ingress Tool Transfer", "Linux Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to Command And Control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software.
action.notable.param.rule_title = Suspicious Curl Network Connection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process=s3.amazonaws.com by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_curl_network_connection_filter`

[ESCU - Suspicious DLLHost no Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious DLLHost no Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Suspicious dllhost.exe process with no command line arguments executed on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious DLLHost no Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Suspicious DLLHost no Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_dllhost` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(dllhost\.exe.{0,4}$)" | `suspicious_dllhost_no_command_line_arguments_filter`

[ESCU - Suspicious Driver Loaded Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect suspicious driver loaded paths. This technique is commonly used by malicious software like coin miners (xmrig) to register its malicious driver from notable directories where executable or drivers do not commonly exist. During triage, validate this driver is for legitimate business use. Review the metadata and certificate information. Unsigned drivers from non-standard paths is not normal, but occurs. In addition, review driver loads into `ntoskrnl.exe` for possible other drivers of interest. Long tail analyze drivers by path (outside of default, and in default) for further review.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will detect suspicious driver loaded paths. This technique is commonly used by malicious software like coin miners (xmrig) to register its malicious driver from notable directories where executable or drivers do not commonly exist. During triage, validate this driver is for legitimate business use. Review the metadata and certificate information. Unsigned drivers from non-standard paths is not normal, but occurs. In addition, review driver loads into `ntoskrnl.exe` for possible other drivers of interest. Long tail analyze drivers by path (outside of default, and in default) for further review.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Limited false positives will be present. Some applications do load drivers
action.escu.creation_date = 2021-04-29
action.escu.modification_date = 2021-04-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Driver Loaded Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["XMRig", "CISA AA22-320A", "AgentTesla", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = Suspicious driver $ImageLoaded$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "ImageLoaded", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Driver Loaded Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "CISA AA22-320A", "AgentTesla", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will detect suspicious driver loaded paths. This technique is commonly used by malicious software like coin miners (xmrig) to register its malicious driver from notable directories where executable or drivers do not commonly exist. During triage, validate this driver is for legitimate business use. Review the metadata and certificate information. Unsigned drivers from non-standard paths is not normal, but occurs. In addition, review driver loads into `ntoskrnl.exe` for possible other drivers of interest. Long tail analyze drivers by path (outside of default, and in default) for further review.
action.notable.param.rule_title = Suspicious Driver Loaded Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*", "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) |  stats  min(_time) as firstTime max(_time) as lastTime count by dest ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_driver_loaded_path_filter`

[ESCU - Suspicious Event Log Service Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Windows Event ID 1100 to identify when Windows event log service is shutdown. Note that this is a voluminous analytic that will require tuning or restricted to specific endpoints based on criticality. This event generates every time Windows Event Log service has shut down. It also generates during normal system shutdown. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Windows Event ID 1100 to identify when Windows event log service is shutdown. Note that this is a voluminous analytic that will require tuning or restricted to specific endpoints based on criticality. This event generates every time Windows Event Log service has shut down. It also generates during normal system shutdown. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
action.escu.known_false_positives = It is possible the Event Logging service gets shut down due to system errors or legitimately administration tasks. Filter as needed.
action.escu.creation_date = 2021-06-17
action.escu.modification_date = 2021-06-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Event Log Service Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = The Windows Event Log Service shutdown on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Event Log Service Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Windows Event ID 1100 to identify when Windows event log service is shutdown. Note that this is a voluminous analytic that will require tuning or restricted to specific endpoints based on criticality. This event generates every time Windows Event Log service has shut down. It also generates during normal system shutdown. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.
action.notable.param.rule_title = Suspicious Event Log Service Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = (`wineventlog_security` EventCode=1100) | stats count min(_time) as firstTime max(_time) as lastTime by dest Message EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_event_log_service_behavior_filter`

[ESCU - Suspicious GPUpdate no Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious GPUpdate no Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Suspicious gpupdate.exe process with no command line arguments executed on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious GPUpdate no Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Suspicious GPUpdate no Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_gpupdate` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(gpupdate\.exe.{0,4}$)" | `suspicious_gpupdate_no_command_line_arguments_filter`

[ESCU - Suspicious IcedID Rundll32 Cmdline - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious rundll32.exe commandline to execute dll file. This technique was seen in IcedID malware to load its payload dll with the following parameter to load encrypted dll payload which is the license.dat.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious rundll32.exe commandline to execute dll file. This technique was seen in IcedID malware to load its payload dll with the following parameter to load encrypted dll payload which is the license.dat.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = limitted. this parameter is not commonly used by windows application but can be used by the network operator.
action.escu.creation_date = 2021-07-26
action.escu.modification_date = 2021-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious IcedID Rundll32 Cmdline - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = rundll32 process $process_name$ with commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious IcedID Rundll32 Cmdline - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe commandline to execute dll file. This technique was seen in IcedID malware to load its payload dll with the following parameter to load encrypted dll payload which is the license.dat.
action.notable.param.rule_title = Suspicious IcedID Rundll32 Cmdline
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*/i:* by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_icedid_rundll32_cmdline_filter`

[ESCU - Suspicious Image Creation In Appdata Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious creation of image in appdata folder made by process that also has a file reference in appdata folder. This technique was seen in remcos rat that capture screenshot of the compromised machine and place it in the appdata and will be send to its C2 server. This TTP is really a good indicator to check that process because it is in suspicious folder path and image files are not commonly created by user in this folder path.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious creation of image in appdata folder made by process that also has a file reference in appdata folder. This technique was seen in remcos rat that capture screenshot of the compromised machine and place it in the appdata and will be send to its C2 server. This TTP is really a good indicator to check that process because it is in suspicious folder path and image files are not commonly created by user in this folder path.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-07-07
action.escu.modification_date = 2022-07-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Image Creation In Appdata Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = Process $process_name$ creating image file $file_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Image Creation In Appdata Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious creation of image in appdata folder made by process that also has a file reference in appdata folder. This technique was seen in remcos rat that capture screenshot of the compromised machine and place it in the appdata and will be send to its C2 server. This TTP is really a good indicator to check that process because it is in suspicious folder path and image files are not commonly created by user in this folder path.
action.notable.param.rule_title = Suspicious Image Creation In Appdata Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid |join proc_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.png","*.jpg","*.bmp","*.gif","*.tiff") Filesystem.file_path= "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] | `suspicious_image_creation_in_appdata_folder_filter`

[ESCU - Suspicious Kerberos Service Ticket Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries will request and obtain a Kerberos Service Ticket (TGS) with a domain controller computer account as the Service Name. This Service Ticket can be then used to take control of the domain controller on the final part of the attack. This analytic leverages Event Id 4769, `A Kerberos service ticket was requested`, to identify an unusual TGS request where the Account_Name requesting the ticket matches the Service_Name field. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries will request and obtain a Kerberos Service Ticket (TGS) with a domain controller computer account as the Service Name. This Service Ticket can be then used to take control of the domain controller on the final part of the attack. This analytic leverages Event Id 4769, `A Kerberos service ticket was requested`, to identify an unusual TGS request where the Account_Name requesting the ticket matches the Service_Name field. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = We have tested this detection logic with ~2 million 4769 events and did not identify false positives. However, they may be possible in certain environments. Filter as needed.
action.escu.creation_date = 2021-12-20
action.escu.modification_date = 2021-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Kerberos Service Ticket Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["sAMAccountName Spoofing and Domain Controller Impersonation", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = A suspicious Kerberos Service Ticket was requested by $user$ on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Kerberos Service Ticket Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["sAMAccountName Spoofing and Domain Controller Impersonation", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "cve": ["CVE-2021-42287", "CVE-2021-42278"], "impact": 100, "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries will request and obtain a Kerberos Service Ticket (TGS) with a domain controller computer account as the Service Name. This Service Ticket can be then used to take control of the domain controller on the final part of the attack. This analytic leverages Event Id 4769, `A Kerberos service ticket was requested`, to identify an unusual TGS request where the Account_Name requesting the ticket matches the Service_Name field. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.notable.param.rule_title = Suspicious Kerberos Service Ticket Request
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4769 | eval isSuspicious = if(lower(Service_Name) = lower(mvindex(split(Account_Name,"@"),0)+"$"),1,0) | where isSuspicious = 1 | rename ComputerName as dest| rename Account_Name as user  | table _time, dest, Client_Address, Account_Name, Service_Name, Failure_Code, isSuspicious | `suspicious_kerberos_service_ticket_request_filter`

[ESCU - Suspicious Linux Discovery Commands - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search, detects execution of suspicious bash commands from various commonly leveraged bash scripts like (AutoSUID, LinEnum, LinPeas) to perform discovery of possible paths of privilege execution, password files, vulnerable directories, executables and file permissions on a Linux host.\
The search logic specifically looks for high number of distinct commands run in a short period of time.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search, detects execution of suspicious bash commands from various commonly leveraged bash scripts like (AutoSUID, LinEnum, LinPeas) to perform discovery of possible paths of privilege execution, password files, vulnerable directories, executables and file permissions on a Linux host.\
The search logic specifically looks for high number of distinct commands run in a short period of time.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unless an administrator is using these commands to troubleshoot or audit a system, the execution of these commands should be monitored.
action.escu.creation_date = 2021-12-06
action.escu.modification_date = 2021-12-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Linux Discovery Commands - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Linux Post-Exploitation"]
action.risk = 1
action.risk.param._risk_message = Suspicious Linux Discovery Commands detected on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Linux Discovery Commands - Rule
action.correlationsearch.annotations = {"analytic_story": ["Linux Post-Exploitation"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1059.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search, detects execution of suspicious bash commands from various commonly leveraged bash scripts like (AutoSUID, LinEnum, LinPeas) to perform discovery of possible paths of privilege execution, password files, vulnerable directories, executables and file permissions on a Linux host.\
The search logic specifically looks for high number of distinct commands run in a short period of time.
action.notable.param.rule_title = Suspicious Linux Discovery Commands
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) values(Processes.process_name) values(Processes.parent_process_name) dc(Processes.process) as distinct_commands dc(Processes.process_name) as distinct_process_names min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where [|inputlookup linux_tool_discovery_process.csv | rename process as Processes.process |table Processes.process] by _time span=5m Processes.user Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| where distinct_commands > 40 AND distinct_process_names > 3| `suspicious_linux_discovery_commands_filter`

[ESCU - Suspicious microsoft workflow compiler rename - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1127", "T1036.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious microsoft workflow compiler rename - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Masquerading - Rename System Utilities", "Living Off The Land", "Cobalt Strike", "Trusted Developer Utilities Proxy Execution", "BlackByte Ransomware", "Graceful Wipe Out Attack"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious microsoft workflow compiler rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Masquerading - Rename System Utilities", "Living Off The Land", "Cobalt Strike", "Trusted Developer Utilities Proxy Execution", "BlackByte Ransomware", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1036", "T1127", "T1036.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=microsoft.workflow.compiler.exe AND Processes.original_file_name=Microsoft.Workflow.Compiler.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_rename_filter`

[ESCU - Suspicious microsoft workflow compiler usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1127"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM.
action.escu.creation_date = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious microsoft workflow compiler usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = Suspicious microsoft.workflow.compiler.exe process ran on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious microsoft workflow compiler usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1127"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications.
action.notable.param.rule_title = Suspicious microsoft workflow compiler usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_microsoftworkflowcompiler` by Processes.dest Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter`

[ESCU - Suspicious msbuild path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious msbuild path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild", "Masquerading - Rename System Utilities", "Living Off The Land", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Msbuild.exe ran from an uncommon path on $dest$ execyted by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious msbuild path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild", "Masquerading - Rename System Utilities", "Living Off The Land", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild.
action.notable.param.rule_title = Suspicious msbuild path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msbuild` AND (Processes.process_path!=*\\framework*\\v*\\*) by Processes.dest Processes.original_file_name Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter`

[ESCU - Suspicious MSBuild Rename - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious MSBuild Rename - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild", "Masquerading - Rename System Utilities", "Living Off The Land", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious MSBuild Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild", "Masquerading - Rename System Utilities", "Living Off The Land", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=msbuild.exe AND Processes.original_file_name=MSBuild.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_rename_filter`

[ESCU - Suspicious MSBuild Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1127", "T1127.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
action.escu.creation_date = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious MSBuild Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = Suspicious msbuild.exe process executed on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious MSBuild Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1127", "T1127.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated.
action.notable.param.rule_title = Suspicious MSBuild Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND `process_msbuild` by Processes.dest Processes.parent_process Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter`

[ESCU - Suspicious mshta child process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies child processes spawning from  "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies child processes spawning from  "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
action.escu.creation_date = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious mshta child process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious MSHTA Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = suspicious mshta child process detected on host $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious mshta child process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies child processes spawning from  "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process.
action.notable.param.rule_title = Suspicious mshta child process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter`

[ESCU - Suspicious mshta spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
action.escu.creation_date = 2021-01-20
action.escu.modification_date = 2021-01-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious mshta spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious MSHTA Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = mshta.exe spawned by wmiprvse.exe on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious mshta spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1218", "T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe.
action.notable.param.rule_title = Suspicious mshta spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND `process_mshta` by Processes.dest Processes.parent_process Processes.user Processes.original_file_name| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter`

[ESCU - Suspicious PlistBuddy Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.001", "T1543"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.
action.escu.creation_date = 2021-02-22
action.escu.modification_date = 2021-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious PlistBuddy Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Silver Sparrow"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious PlistBuddy Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1543.001", "T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
action.notable.param.rule_title = Suspicious PlistBuddy Usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=PlistBuddy (Processes.process=*LaunchAgents* OR Processes.process=*RunAtLoad* OR Processes.process=*true*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |  `suspicious_plistbuddy_usage_filter`

[ESCU - Suspicious PlistBuddy Usage via OSquery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.001", "T1543"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
action.escu.how_to_implement = OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct.
action.escu.known_false_positives = Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.
action.escu.creation_date = 2021-02-22
action.escu.modification_date = 2021-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious PlistBuddy Usage via OSquery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Silver Sparrow"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious PlistBuddy Usage via OSquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1543.001", "T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
action.notable.param.rule_title = Suspicious PlistBuddy Usage via OSquery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" |  `suspicious_plistbuddy_usage_via_osquery_filter`

[ESCU - Suspicious Process DNS Query Known Abuse Web Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a suspicious process making a DNS query via known, abused text-paste web services, VoIP, instant messaging, and digital distribution platforms used to download external files. This technique is abused by adversaries, malware actors, and red teams to download a malicious file on the target host. This is a good TTP indicator for possible initial access techniques. A user will experience false positives if the following instant messaging is allowed or common applications like telegram or discord are allowed in the corporate network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.005", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects a suspicious process making a DNS query via known, abused text-paste web services, VoIP, instant messaging, and digital distribution platforms used to download external files. This technique is abused by adversaries, malware actors, and red teams to download a malicious file on the target host. This is a good TTP indicator for possible initial access techniques. A user will experience false positives if the following instant messaging is allowed or common applications like telegram or discord are allowed in the corporate network.
action.escu.how_to_implement = This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days.
action.escu.known_false_positives = Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Process DNS Query Known Abuse Web Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "WhisperGate", "Remcos"]
action.risk = 1
action.risk.param._risk_message = suspicious process $process_name$ has a dns query in $QueryName$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Process DNS Query Known Abuse Web Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "WhisperGate", "Remcos"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1059.005", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a suspicious process making a DNS query via known, abused text-paste web services, VoIP, instant messaging, and digital distribution platforms used to download external files. This technique is abused by adversaries, malware actors, and red teams to download a malicious file on the target host. This is a good TTP indicator for possible initial access techniques. A user will experience false positives if the following instant messaging is allowed or common applications like telegram or discord are allowed in the corporate network.
action.notable.param.rule_title = Suspicious Process DNS Query Known Abuse Web Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22 QueryName IN ("*pastebin*", "*discord*", "*telegram*", "*t.me*") process_name IN ("cmd.exe", "*powershell*", "pwsh.exe", "wscript.exe", "cscript.exe") | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_dns_query_known_abuse_web_services_filter`

[ESCU - Suspicious Process Executed From Container File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a suspicious process spawned by another process from within common container/archive file types. This technique was a common technique used by adversaries and malware to execute scripts or evade defenses. This TTP may detect some normal software installation or user behaviors where opening archive files is common.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204.002", "T1036.008"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a suspicious process spawned by another process from within common container/archive file types. This technique was a common technique used by adversaries and malware to execute scripts or evade defenses. This TTP may detect some normal software installation or user behaviors where opening archive files is common.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Various business process or userland applications and behavior.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Process Executed From Container File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = A suspicious process $process_name$ was launched from $file_name$ on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 16}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 16}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Process Executed From Container File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 20, "impact": 80, "mitre_attack": ["T1204.002", "T1036.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a suspicious process spawned by another process from within common container/archive file types. This technique was a common technique used by adversaries and malware to execute scripts or evade defenses. This TTP may detect some normal software installation or user behaviors where opening archive files is common.
action.notable.param.rule_title = Suspicious Process Executed From Container File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.ZIP\\*","*.ISO\\*","*.IMG\\*","*.CAB\\*","*.TAR\\*","*.GZ\\*","*.RAR\\*","*.7Z\\*") AND Processes.action="allowed" by Processes.dest Processes.parent_process Processes.process Processes.user| `drop_dm_object_name(Processes)`| regex process="(?i).*(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z)\\\\.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH)\"?$" | rex field=process "(?i).+\\\\(?<file_name>[^\\\]+\.(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z))\\\\((.+\\\\)+)?(?<process_name>.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH))\"?$"| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_executed_from_container_file_filter`

[ESCU - Suspicious Process File Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a suspicious processes running in file paths that are not typically associated with legitimate software. Adversaries often employ this technique to drop and execute malicious executables in accessible locations that do not require administrative privileges. By monitoring for processes running in such unconventional file paths, we can identify potential indicators of compromise and proactively respond to malicious activity. This analytic plays a crucial role in enhancing system security by pinpointing suspicious behaviors commonly associated with malware and unauthorized software execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a suspicious processes running in file paths that are not typically associated with legitimate software. Adversaries often employ this technique to drop and execute malicious executables in accessible locations that do not require administrative privileges. By monitoring for processes running in such unconventional file paths, we can identify potential indicators of compromise and proactively respond to malicious activity. This analytic plays a crucial role in enhancing system security by pinpointing suspicious behaviors commonly associated with malware and unauthorized software execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may allow execution of specific binaries in non-standard paths. Filter as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Process File Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["WhisperGate", "XMRig", "Industroyer2", "Remcos", "Data Destruction", "Hermetic Wiper", "Azorult", "DarkCrystal RAT", "Graceful Wipe Out Attack", "IcedID", "Swift Slicer", "Qakbot", "RedLine Stealer", "Brute Ratel C4", "Prestige Ransomware", "AsyncRAT", "LockBit Ransomware", "AgentTesla", "Double Zero Destructor", "Volt Typhoon", "Chaos Ransomware", "Trickbot", "Amadey", "BlackByte Ransomware", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = Suspicious process $process_name$ running from a suspicious process path- $process_path$ on host- $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"threat_object_field": "process_path", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Process File Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["WhisperGate", "XMRig", "Industroyer2", "Remcos", "Data Destruction", "Hermetic Wiper", "Azorult", "DarkCrystal RAT", "Graceful Wipe Out Attack", "IcedID", "Swift Slicer", "Qakbot", "RedLine Stealer", "Brute Ratel C4", "Prestige Ransomware", "AsyncRAT", "LockBit Ransomware", "AgentTesla", "Double Zero Destructor", "Volt Typhoon", "Chaos Ransomware", "Trickbot", "Amadey", "BlackByte Ransomware", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a suspicious processes running in file paths that are not typically associated with legitimate software. Adversaries often employ this technique to drop and execute malicious executables in accessible locations that do not require administrative privileges. By monitoring for processes running in such unconventional file paths, we can identify potential indicators of compromise and proactively respond to malicious activity. This analytic plays a crucial role in enhancing system security by pinpointing suspicious behaviors commonly associated with malware and unauthorized software execution.
action.notable.param.rule_title = Suspicious Process File Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path = "*\\Windows\\servicing\\*" OR Processes.process_path = "*\\Users\\Default\\*" OR Processes.process_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" OR Processes.process_path = "*\\PerfLogs\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_file_path_filter`

[ESCU - Suspicious Process With Discord DNS Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a process making a DNS query to Discord, a well known instant messaging and digital distribution platform. Discord can be abused by adversaries, as seen in the WhisperGate campaign, to host and download malicious. external files. A process resolving a Discord DNS name could be an indicator of malware trying to download files from Discord for further execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.005", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies a process making a DNS query to Discord, a well known instant messaging and digital distribution platform. Discord can be abused by adversaries, as seen in the WhisperGate campaign, to host and download malicious. external files. A process resolving a Discord DNS name could be an indicator of malware trying to download files from Discord for further execution.
action.escu.how_to_implement = his detection relies on sysmon logs with the Event ID 22, DNS Query.
action.escu.known_false_positives = Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Process With Discord DNS Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "WhisperGate"]
action.risk = 1
action.risk.param._risk_message = suspicious process $process_name$ has a dns query in $QueryName$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Process With Discord DNS Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1059.005", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22 QueryName IN ("*discord*") Image != "*\\AppData\\Local\\Discord\\*" AND Image != "*\\Program Files*" AND Image != "discord.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_with_discord_dns_query_filter`

[ESCU - Suspicious Reg exe Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out.
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Reg exe Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Disabling Security Tools", "DHS Report TA18-074A"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a registry entry.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Reg exe Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Disabling Security Tools", "DHS Report TA18-074A"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` 

[ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues.
action.escu.creation_date = 2023-03-02
action.escu.modification_date = 2023-03-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Regsvr32 Activity", "Icedid", "Living Off The Land", "Qakbot"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to evade detection by using a non-standard file extension.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvr32 Activity", "Icedid", "Living Off The Land", "Qakbot"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity.
action.notable.param.rule_title = Suspicious Regsvr32 Register Suspicious Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process IN ("*\\appdata\\*", "*\\programdata\\*","*\\windows\\temp\\*") NOT (Processes.process IN ("*.dll*", "*.ax*", "*.ocx*")) by Processes.dest Processes.user  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter`

[ESCU - Suspicious Rundll32 dllregisterserver - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.
action.escu.creation_date = 2021-02-09
action.escu.modification_date = 2021-02-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Rundll32 dllregisterserver - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Living Off The Land", "IcedID"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to register a DLL. code
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Rundll32 dllregisterserver - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Living Off The Land", "IcedID"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Suspicious Rundll32 dllregisterserver
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter`

[ESCU - Suspicious Rundll32 no Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Rundll32 no Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Cobalt Strike", "BlackByte Ransomware", "PrintNightmare CVE-2021-34527", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Suspicious rundll32.exe process with no command line arguments executed on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Rundll32 no Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Cobalt Strike", "BlackByte Ransomware", "PrintNightmare CVE-2021-34527", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2021-34527"], "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Suspicious Rundll32 no Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_rundll32` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(rundll32\.exe.{0,4}$)" | `suspicious_rundll32_no_command_line_arguments_filter`

[ESCU - Suspicious Rundll32 PluginInit - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious rundll32.exe process with plugininit parameter. This technique is commonly seen in IcedID malware to execute its initial dll stager to download another payload to the compromised machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious rundll32.exe process with plugininit parameter. This technique is commonly seen in IcedID malware to execute its initial dll stager to download another payload to the compromised machine.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = third party application may used this dll export name to execute function.
action.escu.creation_date = 2021-07-26
action.escu.modification_date = 2021-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Rundll32 PluginInit - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = rundll32 process $process_name$ with commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Rundll32 PluginInit - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe process with plugininit parameter. This technique is commonly seen in IcedID malware to execute its initial dll stager to download another payload to the compromised machine.
action.notable.param.rule_title = Suspicious Rundll32 PluginInit
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*PluginInit* by  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_plugininit_filter`

[ESCU - Suspicious Rundll32 StartW - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Rundll32 StartW - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trickbot", "Suspicious Rundll32 Activity", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = rundll32.exe running with suspicious StartW parameters on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Rundll32 StartW - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "Suspicious Rundll32 Activity", "Cobalt Strike", "BlackByte Ransomware", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218", "T1218.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not.
action.notable.param.rule_title = Suspicious Rundll32 StartW
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter`

[ESCU - Suspicious Scheduled Task from Public Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic, "Suspicious Scheduled Task from Public Directory", detects the registration of scheduled tasks aimed to execute a binary or script from public directories, a behavior often associated with malware deployment. It utilizes the Sysmon Event ID 1 data source, searching for instances where schtasks.exe is connected with the directories users\public, \programdata\, or \windows\temp and involves the /create command.\
The registration of such scheduled tasks in public directories could suggest that an attacker is trying to maintain persistence or execute malicious scripts. If confirmed as a true positive, this could lead to data compromise, unauthorized access, and potential lateral movement within the network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic, "Suspicious Scheduled Task from Public Directory", detects the registration of scheduled tasks aimed to execute a binary or script from public directories, a behavior often associated with malware deployment. It utilizes the Sysmon Event ID 1 data source, searching for instances where schtasks.exe is connected with the directories users\public, \programdata\, or \windows\temp and involves the /create command.\
The registration of such scheduled tasks in public directories could suggest that an attacker is trying to maintain persistence or execute malicious scripts. If confirmed as a true positive, this could lead to data compromise, unauthorized access, and potential lateral movement within the network.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = The main source of false positives could be the legitimate use of scheduled tasks from these directories. Careful tuning of this search may be necessary to suit the specifics of your environment, reducing the rate of false positives.
action.escu.creation_date = 2021-03-01
action.escu.modification_date = 2021-03-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Scheduled Task from Public Directory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Ryuk Ransomware", "Windows Persistence Techniques", "Living Off The Land", "Azorult", "DarkCrystal RAT", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = Suspicious scheduled task registered on $dest$ from Public Directory
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Scheduled Task from Public Directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Ryuk Ransomware", "Windows Persistence Techniques", "Living Off The Land", "Azorult", "DarkCrystal RAT", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR Processes.process=*windows\\temp*)  Processes.process=*/create* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_scheduled_task_from_public_directory_filter`

[ESCU - Suspicious SearchProtocolHost no Command Line Arguments - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious SearchProtocolHost no Command Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Suspicious searchprotocolhost.exe process with no command line arguments executed on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious SearchProtocolHost no Command Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware", "Cobalt Strike", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
action.notable.param.rule_title = Suspicious SearchProtocolHost no Command Line Arguments
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" | `suspicious_searchprotocolhost_no_command_line_arguments_filter`

[ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown.
action.escu.creation_date = 2021-02-22
action.escu.modification_date = 2021-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Silver Sparrow"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1074"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.
action.notable.param.rule_title = Suspicious SQLite3 LSQuarantine Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3 Processes.process=*LSQuarantine* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_sqlite3_lsquarantine_behavior_filter`

[ESCU - Suspicious Ticket Granting Ticket Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries will need to request a Kerberos Ticket Granting Ticket (TGT) on behalf of the newly created and renamed computer account. The TGT request will be preceded by a computer account name event. This analytic leverages Event Id 4781, `The name of an account was changed` and event Id 4768 `A Kerberos authentication ticket (TGT) was requested` to correlate a sequence of events where the new computer account on event id 4781 matches the request account on event id 4768. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = As part of the sAMAccountName Spoofing (CVE-2021-42278) and Domain Controller Impersonation (CVE-2021-42287) exploitation chain, adversaries will need to request a Kerberos Ticket Granting Ticket (TGT) on behalf of the newly created and renamed computer account. The TGT request will be preceded by a computer account name event. This analytic leverages Event Id 4781, `The name of an account was changed` and event Id 4768 `A Kerberos authentication ticket (TGT) was requested` to correlate a sequence of events where the new computer account on event id 4781 matches the request account on event id 4768. This behavior could represent an exploitation attempt of CVE-2021-42278 and CVE-2021-42287 for privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A computer account name change event inmediately followed by a kerberos TGT request with matching fields is unsual. However, legitimate behavior may trigger it. Filter as needed.
action.escu.creation_date = 2021-12-21
action.escu.modification_date = 2021-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious Ticket Granting Ticket Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["sAMAccountName Spoofing and Domain Controller Impersonation", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious Ticket Granting Ticket Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["sAMAccountName Spoofing and Domain Controller Impersonation", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "impact": 100, "mitre_attack": ["T1078", "T1078.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` (EventCode=4781 Old_Account_Name="*$" New_Account_Name!="*$") OR (EventCode=4768 Account_Name!="*$") | eval RenamedComputerAccount = coalesce(New_Account_Name, mvindex(Account_Name,0)) | transaction RenamedComputerAccount startswith=(EventCode=4781) endswith=(EventCode=4768) | eval short_lived=case((duration<2),"TRUE") | search short_lived = TRUE | table _time, ComputerName, EventCode, Account_Name,RenamedComputerAccount, short_lived |`suspicious_ticket_granting_ticket_request_filter`

[ESCU - Suspicious WAV file in Appdata Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious creation of .wav file in appdata folder. This behavior was seen in Remcos RAT malware where it put the audio recording in the appdata\audio folde as part of data collection. this recording can be send to its C2 server as part of its exfiltration to the compromised machine. creation of wav files in this folder path is not a ussual disk place used by user to save audio format file.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious creation of .wav file in appdata folder. This behavior was seen in Remcos RAT malware where it put the audio recording in the appdata\audio folde as part of data collection. this recording can be send to its C2 server as part of its exfiltration to the compromised machine. creation of wav files in this folder path is not a ussual disk place used by user to save audio format file.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, file_name, file_path and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-07-07
action.escu.modification_date = 2022-07-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious WAV file in Appdata Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ creating image file $file_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious WAV file in Appdata Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious creation of .wav file in appdata folder. This behavior was seen in Remcos RAT malware where it put the audio recording in the appdata\audio folde as part of data collection. this recording can be send to its C2 server as part of its exfiltration to the compromised machine. creation of wav files in this folder path is not a ussual disk place used by user to save audio format file.
action.notable.param.rule_title = Suspicious WAV file in Appdata Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.wav") Filesystem.file_path = "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields file_name file_path process_name process_path process dest file_create_time _time proc_guid] | `suspicious_wav_file_in_appdata_folder_filter`

[ESCU - Suspicious wevtutil Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, trace or system event logs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070.001", "T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, trace or system event logs.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs.
action.escu.creation_date = 2021-10-11
action.escu.modification_date = 2021-10-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious wevtutil Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = Wevtutil.exe being used to clear Event Logs on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 28}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious wevtutil Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1070.001", "T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, trace or system event logs.
action.notable.param.rule_title = Suspicious wevtutil Usage
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*") (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*" OR Processes.process="*trace*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter`

[ESCU - Suspicious writes to windows Recycle Bin - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects when a process other than explorer.exe writes to the Windows Recycle Bin to detect potential threats earlier and mitigate the risks. This detection is made by a Splunk query that utilizes the Endpoint.Filesystem data model and the Endpoint.Processes data model. The query looks for any process writing to the "*$Recycle.Bin*" file path, excluding explorer.exe. This detection is important because it suggests that an attacker is attempting to hide their activities by using the Recycle Bin, which can lead to data theft, ransomware, or other damaging outcomes. Detecting writes to the Recycle Bin by a process other than explorer.exe can help to investigate and determine if the activity is malicious or benign. False positives might occur since there might be legitimate uses of the Recycle Bin by processes other than explorer.exe. Next steps include reviewing the process writing to the Recycle Bin and any relevant on-disk artifacts upon triage.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects when a process other than explorer.exe writes to the Windows Recycle Bin to detect potential threats earlier and mitigate the risks. This detection is made by a Splunk query that utilizes the Endpoint.Filesystem data model and the Endpoint.Processes data model. The query looks for any process writing to the "*$Recycle.Bin*" file path, excluding explorer.exe. This detection is important because it suggests that an attacker is attempting to hide their activities by using the Recycle Bin, which can lead to data theft, ransomware, or other damaging outcomes. Detecting writes to the Recycle Bin by a process other than explorer.exe can help to investigate and determine if the activity is malicious or benign. False positives might occur since there might be legitimate uses of the Recycle Bin by processes other than explorer.exe. Next steps include reviewing the process writing to the Recycle Bin and any relevant on-disk artifacts upon triage.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
action.escu.known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate.
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Suspicious writes to windows Recycle Bin - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Collection and Staging"]
action.risk = 1
action.risk.param._risk_message = Suspicious writes to windows Recycle Bin process $Processes.process_name$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}, {"threat_object_field": "Processes.process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Suspicious writes to windows Recycle Bin - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects when a process other than explorer.exe writes to the Windows Recycle Bin to detect potential threats earlier and mitigate the risks. This detection is made by a Splunk query that utilizes the Endpoint.Filesystem data model and the Endpoint.Processes data model. The query looks for any process writing to the "*$Recycle.Bin*" file path, excluding explorer.exe. This detection is important because it suggests that an attacker is attempting to hide their activities by using the Recycle Bin, which can lead to data theft, ransomware, or other damaging outcomes. Detecting writes to the Recycle Bin by a process other than explorer.exe can help to investigate and determine if the activity is malicious or benign. False positives might occur since there might be legitimate uses of the Recycle Bin by processes other than explorer.exe. Next steps include reviewing the process writing to the Recycle Bin and any relevant on-disk artifacts upon triage.
action.notable.param.rule_title = Suspicious writes to windows Recycle Bin
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter`

[ESCU - Svchost LOLBAS Execution Process Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to spot instances of 'svchost.exe' initiating a Living Off The Land Binaries and Scripts (LOLBAS) execution process. Often, adversaries manipulate Task Scheduler to execute code on remote endpoints, resulting in the spawning of a malicious command as a child process of 'svchost.exe'. By tracking child processes of 'svchost.exe' that align with the LOLBAS project, potential lateral movement activity can be detected. The analytic examines process details, including the process name, parent process, and command-line executions. A comprehensive list of LOLBAS processes is included in the search parameters. Although the analytic might catch legitimate applications exhibiting this behavior, these instances should be filtered accordingly. The findings from this analytic offer valuable insight into potentially malicious activities on an endpoint.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to spot instances of 'svchost.exe' initiating a Living Off The Land Binaries and Scripts (LOLBAS) execution process. Often, adversaries manipulate Task Scheduler to execute code on remote endpoints, resulting in the spawning of a malicious command as a child process of 'svchost.exe'. By tracking child processes of 'svchost.exe' that align with the LOLBAS project, potential lateral movement activity can be detected. The analytic examines process details, including the process name, parent process, and command-line executions. A comprehensive list of LOLBAS processes is included in the search parameters. Although the analytic might catch legitimate applications exhibiting this behavior, these instances should be filtered accordingly. The findings from this analytic offer valuable insight into potentially malicious activities on an endpoint.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate applications may trigger this behavior, filter as needed.
action.escu.creation_date = 2021-11-22
action.escu.modification_date = 2021-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Svchost LOLBAS Execution Process Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Living Off The Land", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = Svchost.exe spawned a LOLBAS process on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Svchost LOLBAS Execution Process Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Living Off The Land", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to spot instances of 'svchost.exe' initiating a Living Off The Land Binaries and Scripts (LOLBAS) execution process. Often, adversaries manipulate Task Scheduler to execute code on remote endpoints, resulting in the spawning of a malicious command as a child process of 'svchost.exe'. By tracking child processes of 'svchost.exe' that align with the LOLBAS project, potential lateral movement activity can be detected. The analytic examines process details, including the process name, parent process, and command-line executions. A comprehensive list of LOLBAS processes is included in the search parameters. Although the analytic might catch legitimate applications exhibiting this behavior, these instances should be filtered accordingly. The findings from this analytic offer valuable insight into potentially malicious activities on an endpoint.
action.notable.param.rule_title = Svchost LOLBAS Execution Process Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `svchost_lolbas_execution_process_spawn_filter`

[ESCU - System Info Gathering Using Dxdiag Application - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious dxdiag.exe process command-line execution. Dxdiag is used to collect the system info of the target host. This technique has been used by Remcos RATS, various actors, and other malware to collect information as part of the recon or collection phase of an attack. This behavior should rarely be seen in a corporate network, but this command line can be used by a network administrator to audit host machine specifications. Thus in some rare cases, this detection will contain false positives in its results. To triage further, analyze what commands were passed after it pipes out the result to a file for further processing.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1592"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious dxdiag.exe process command-line execution. Dxdiag is used to collect the system info of the target host. This technique has been used by Remcos RATS, various actors, and other malware to collect information as part of the recon or collection phase of an attack. This behavior should rarely be seen in a corporate network, but this command line can be used by a network administrator to audit host machine specifications. Thus in some rare cases, this detection will contain false positives in its results. To triage further, analyze what commands were passed after it pipes out the result to a file for further processing.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This commandline can be used by a network administrator to audit host machine specifications. Thus, a filter is needed.
action.escu.creation_date = 2021-11-19
action.escu.modification_date = 2021-11-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - System Info Gathering Using Dxdiag Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - System Info Gathering Using Dxdiag Application - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1592"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_dxdiag` AND Processes.process = "* /t *" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_info_gathering_using_dxdiag_application_filter`

[ESCU - System Information Discovery Detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators debugging servers
action.escu.creation_date = 2021-09-07
action.escu.modification_date = 2021-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - System Information Discovery Detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Discovery Techniques"]
action.risk = 1
action.risk.param._risk_message = Potential system information discovery behavior on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - System Information Discovery Detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.
action.notable.param.rule_title = System Information Discovery Detection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) as process min(firstTime) as firstTime max(lastTime) as lastTime by user, dest parent_process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter`

[ESCU - System Processes Run From Unexpected Locations - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for system processes that typically execute from `C:\Windows\System32\` or `C:\Windows\SysWOW64`.  This may indicate a malicious process that is trying to hide as a legitimate process.\
This detection utilizes a lookup that is deduped `system32` and `syswow64` directories from Server 2016 and Windows 10.\
During triage, review the parallel processes - what process moved the native Windows binary? identify any artifacts on disk and review. If a remote destination is contacted, what is the reputation?
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1036.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for system processes that typically execute from `C:\Windows\System32\` or `C:\Windows\SysWOW64`.  This may indicate a malicious process that is trying to hide as a legitimate process.\
This detection utilizes a lookup that is deduped `system32` and `syswow64` directories from Server 2016 and Windows 10.\
During triage, review the parallel processes - what process moved the native Windows binary? identify any artifacts on disk and review. If a remote destination is contacted, what is the reputation?
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This detection may require tuning based on third party applications utilizing native Windows binaries in non-standard paths.
action.escu.creation_date = 2020-12-08
action.escu.modification_date = 2020-12-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - System Processes Run From Unexpected Locations - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware", "Masquerading - Rename System Utilities", "Qakbot", "Windows Error Reporting Service Elevation of Privilege Vulnerability"]
action.risk = 1
action.risk.param._risk_message = A System process $process_name$ is running from $process_path$ on $dest$, potentially non-standard.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_path", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - System Processes Run From Unexpected Locations - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware", "Masquerading - Rename System Utilities", "Qakbot", "Windows Error Reporting Service Elevation of Privilege Vulnerability"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1036", "T1036.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user Processes.parent_process Processes.process_path Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter`

[ESCU - System User Discovery With Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `query.exe` with command-line arguments utilized to discover the logged user. Red Teams and adversaries alike may leverage `query.exe` to identify system users on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `query.exe` with command-line arguments utilized to discover the logged user. Red Teams and adversaries alike may leverage `query.exe` to identify system users on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - System User Discovery With Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - System User Discovery With Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="query.exe") (Processes.process=*user*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_user_discovery_with_query_filter`

[ESCU - System User Discovery With Whoami - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `whoami.exe` without any arguments. This windows native binary prints out the current logged user. Red Teams and adversaries alike may leverage `whoami.exe` to identify system users on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `whoami.exe` without any arguments. This windows native binary prints out the current logged user. Red Teams and adversaries alike may leverage `whoami.exe` to identify system users on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - System User Discovery With Whoami - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Winter Vivern", "Active Directory Discovery", "Qakbot"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - System User Discovery With Whoami - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern", "Active Directory Discovery", "Qakbot"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="whoami.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_user_discovery_with_whoami_filter`

[ESCU - Time Provider Persistence Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification of time provider registry for persistence and autostart. This technique can allow the attacker to persist on the compromised host and autostart as soon as the machine boot up. This TTP can be a good indicator of suspicious behavior since this registry is not commonly modified by normal user or even an admin.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.003", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification of time provider registry for persistence and autostart. This technique can allow the attacker to persist on the compromised host and autostart as soon as the machine boot up. This TTP can be a good indicator of suspicious behavior since this registry is not commonly modified by normal user or even an admin.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Time Provider Persistence Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Hermetic Wiper", "Windows Privilege Escalation", "Windows Persistence Techniques", "Windows Registry Abuse", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = modified/added/deleted registry entry $registry_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Time Provider Persistence Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hermetic Wiper", "Windows Privilege Escalation", "Windows Persistence Techniques", "Windows Registry Abuse", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1547.003", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious modification of time provider registry for persistence and autostart. This technique can allow the attacker to persist on the compromised host and autostart as soon as the machine boot up. This TTP can be a good indicator of suspicious behavior since this registry is not commonly modified by normal user or even an admin.
action.notable.param.rule_title = Time Provider Persistence Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `time_provider_persistence_registry_filter`

[ESCU - Trickbot Named Pipe - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is to detect potential trickbot infection through the create/connected named pipe to the system. This technique is used by trickbot to communicate to its c2 to post or get command during infection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = this search is to detect potential trickbot infection through the create/connected named pipe to the system. This technique is used by trickbot to communicate to its c2 to post or get command during infection.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and pipename from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. .
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-04-26
action.escu.modification_date = 2021-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Trickbot Named Pipe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Trickbot"]
action.risk = 1
action.risk.param._risk_message = Possible Trickbot namedpipe created on $dest$ by $Image$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"threat_object_field": "Image", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Trickbot Named Pipe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is to detect potential trickbot infection through the create/connected named pipe to the system. This technique is used by trickbot to communicate to its c2 to post or get command during infection.
action.notable.param.rule_title = Trickbot Named Pipe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats  min(_time) as firstTime max(_time) as lastTime count by dest user_id EventCode PipeName signature Image process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `trickbot_named_pipe_filter`

[ESCU - UAC Bypass MMC Load Unsigned Dll - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious loaded unsigned dll by MMC.exe application. This technique is commonly seen in attacker that tries to bypassed UAC feature or gain privilege escalation. This is done by modifying some CLSID registry that will trigger the mmc.exe to load the dll path
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548", "T1218.014"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious loaded unsigned dll by MMC.exe application. This technique is commonly seen in attacker that tries to bypassed UAC feature or gain privilege escalation. This is done by modifying some CLSID registry that will trigger the mmc.exe to load the dll path
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown. all of the dll loaded by mmc.exe is microsoft signed dll.
action.escu.creation_date = 2021-07-12
action.escu.modification_date = 2021-07-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - UAC Bypass MMC Load Unsigned Dll - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Suspicious unsigned $ImageLoaded$ loaded by $Image$ on endpoint $dest$ with EventCode $EventCode$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - UAC Bypass MMC Load Unsigned Dll - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548", "T1218.014"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious loaded unsigned dll by MMC.exe application. This technique is commonly seen in attacker that tries to bypassed UAC feature or gain privilege escalation. This is done by modifying some CLSID registry that will trigger the mmc.exe to load the dll path
action.notable.param.rule_title = UAC Bypass MMC Load Unsigned Dll
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7  ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false Company != "Microsoft Corporation" | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed ProcessId OriginalFileName dest EventCode Company | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_mmc_load_unsigned_dll_filter`

[ESCU - UAC Bypass With Colorui COM Object - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a possible uac bypass using the colorui.dll COM Object. this technique was seen in so many malware and ransomware like lockbit where it make use of the colorui.dll COM CLSID to bypass UAC.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a possible uac bypass using the colorui.dll COM Object. this technique was seen in so many malware and ransomware like lockbit where it make use of the colorui.dll COM CLSID to bypass UAC.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = not so common. but 3rd part app may load this dll.
action.escu.creation_date = 2021-08-13
action.escu.modification_date = 2021-08-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - UAC Bypass With Colorui COM Object - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "ImageLoaded", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - UAC Bypass With Colorui COM Object - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1218", "T1218.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a possible uac bypass using the colorui.dll COM Object. this technique was seen in so many malware and ransomware like lockbit where it make use of the colorui.dll COM CLSID to bypass UAC.
action.notable.param.rule_title = UAC Bypass With Colorui COM Object
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 ImageLoaded="*\\colorui.dll" process_name != "colorcpl.exe" NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest user_id EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_with_colorui_com_object_filter`

[ESCU - Uninstall App Using MsiExec - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious un-installation of application using msiexec. This technique was seen in conti leak tool and script where it tries to uninstall AV product using this commandline. This commandline to uninstall product is not a common practice in enterprise network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.007", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious un-installation of application using msiexec. This technique was seen in conti leak tool and script where it tries to uninstall AV product using this commandline. This commandline to uninstall product is not a common practice in enterprise network.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown.
action.escu.creation_date = 2021-08-09
action.escu.modification_date = 2021-08-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Uninstall App Using MsiExec - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ with a cmdline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Uninstall App Using MsiExec - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1218.007", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious un-installation of application using msiexec. This technique was seen in conti leak tool and script where it tries to uninstall AV product using this commandline. This commandline to uninstall product is not a common practice in enterprise network.
action.notable.param.rule_title = Uninstall App Using MsiExec
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msiexec.exe Processes.process= "* /qn *" Processes.process= "*/X*" Processes.process= "*REBOOT=*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uninstall_app_using_msiexec_filter`

[ESCU - Unknown Process Using The Kerberos Protocol - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process performing an outbound connection on port 88 used by default by the network authentication protocol Kerberos. Typically, on a regular Windows endpoint, only the lsass.exe process is the one tasked with connecting to the Kerberos Distribution Center to obtain Kerberos tickets. Identifying an unknown process using this protocol may be evidence of an adversary abusing the Kerberos protocol.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1550"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies a process performing an outbound connection on port 88 used by default by the network authentication protocol Kerberos. Typically, on a regular Windows endpoint, only the lsass.exe process is the one tasked with connecting to the Kerberos Distribution Center to obtain Kerberos tickets. Identifying an unknown process using this protocol may be evidence of an adversary abusing the Kerberos protocol.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Custom applications may leverage the Kerberos protocol. Filter as needed.
action.escu.creation_date = 2022-03-09
action.escu.modification_date = 2022-03-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unknown Process Using The Kerberos Protocol - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Unknown process $process_name$ using the kerberos protocol detected on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unknown Process Using The Kerberos Protocol - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1550"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a process performing an outbound connection on port 88 used by default by the network authentication protocol Kerberos. Typically, on a regular Windows endpoint, only the lsass.exe process is the one tasked with connecting to the Kerberos Distribution Center to obtain Kerberos tickets. Identifying an unknown process using this protocol may be evidence of an adversary abusing the Kerberos protocol.
action.notable.param.rule_title = Unknown Process Using The Kerberos Protocol
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name!=lsass.exe by _time Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port = 88 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` ] | table _time dest parent_process_name process_name process_path process process_id dest_port | `unknown_process_using_the_kerberos_protocol_filter`

[ESCU - Unload Sysmon Filter Driver - Rule]
action.escu = 0
action.escu.enabled = 1
description = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown at the moment
action.escu.creation_date = 2022-06-01
action.escu.modification_date = 2022-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unload Sysmon Filter Driver - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Disabling Security Tools"]
action.risk = 1
action.risk.param._risk_message = Possible Sysmon filter driver unloading on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unload Sysmon Filter Driver - Rule
action.correlationsearch.annotations = {"analytic_story": ["Disabling Security Tools"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data.
action.notable.param.rule_title = Unload Sysmon Filter Driver
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv*  by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process

[ESCU - Unloading AMSI via Reflection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1059.001", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Potential for some third party applications to disable AMSI upon invocation. Filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unloading AMSI via Reflection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Possible AMSI Unloading via Reflection using PowerShell on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unloading AMSI via Reflection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1562", "T1059.001", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.notable.param.rule_title = Unloading AMSI via Reflection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = *system.management.automation.amsi* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unloading_amsi_via_reflection_filter`

[ESCU - Unusual Number of Computer Service Tickets Requested - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic leverages Event ID 4769, `A Kerberos service ticket was requested`, to identify an unusual number of computer service ticket requests from one source. When a domain joined endpoint connects to a remote endpoint, it first will request a Kerberos Ticket with the computer name as the Service Name. An endpoint requesting a large number of computer service tickets for different endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of service requests. To customize this analytic, users can try different combinations of the `bucket` span time,  the calculation of the `upperBound` field as well as the Outlier calculation. This logic can be used for real time security monitoring as well as threat hunting exercises.\
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic leverages Event ID 4769, `A Kerberos service ticket was requested`, to identify an unusual number of computer service ticket requests from one source. When a domain joined endpoint connects to a remote endpoint, it first will request a Kerberos Ticket with the computer name as the Service Name. An endpoint requesting a large number of computer service tickets for different endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of service requests. To customize this analytic, users can try different combinations of the `bucket` span time,  the calculation of the `upperBound` field as well as the Outlier calculation. This logic can be used for real time security monitoring as well as threat hunting exercises.\
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = An single endpoint requesting a large number of computer service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systeams and missconfigured systems.
action.escu.creation_date = 2021-12-01
action.escu.modification_date = 2021-12-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unusual Number of Computer Service Tickets Requested - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unusual Number of Computer Service Tickets Requested - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4769 Service_Name="*$" Account_Name!="*$*" | bucket span=2m _time | stats dc(Service_Name) AS unique_targets values(Service_Name) as host_targets by _time, Client_Address, Account_Name | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std by Client_Address, Account_Name | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) | `unusual_number_of_computer_service_tickets_requested_filter`

[ESCU - Unusual Number of Kerberos Service Tickets Requested - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number service ticket requests. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number service ticket requests. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = An single endpoint requesting a large number of kerberos service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems.
action.escu.creation_date = 2022-02-08
action.escu.modification_date = 2022-02-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unusual Number of Kerberos Service Tickets Requested - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unusual Number of Kerberos Service Tickets Requested - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4769 Service_Name!="*$" Ticket_Encryption_Type=0x17 | bucket span=2m _time | stats dc(Service_Name) AS unique_services values(Service_Name) as requested_services by _time, Client_Address | eventstats avg(unique_services) as comp_avg , stdev(unique_services) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_services > 2 and unique_services >= upperBound, 1, 0) | search isOutlier=1 | `unusual_number_of_kerberos_service_tickets_requested_filter`

[ESCU - Unusual Number of Remote Endpoint Authentication Events - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic leverages Event ID 4624, `An account was successfully logged on`, to identify an unusual number of remote authentication attempts coming from one source. An endpoint authenticating to a large number of remote endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual high number of authentication events. To customize this analytic, users can try different combinations of the `bucket` span time,  the calculation of the `upperBound` field as well as the Outlier calculation. This logic can be used for real time security monitoring as well as threat hunting exercises.\
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic leverages Event ID 4624, `An account was successfully logged on`, to identify an unusual number of remote authentication attempts coming from one source. An endpoint authenticating to a large number of remote endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual high number of authentication events. To customize this analytic, users can try different combinations of the `bucket` span time,  the calculation of the `upperBound` field as well as the Outlier calculation. This logic can be used for real time security monitoring as well as threat hunting exercises.\
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = An single endpoint authenticating to a large number of hosts is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, jump servers and missconfigured systems.
action.escu.creation_date = 2021-12-01
action.escu.modification_date = 2021-12-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unusual Number of Remote Endpoint Authentication Events - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Active Directory Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unusual Number of Remote Endpoint Authentication Events - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "mitre_attack": ["T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4624 Logon_Type=3 Account_Name!="*$" | eval Source_Account = mvindex(Account_Name, 1) | bucket span=2m _time | stats dc(ComputerName) AS unique_targets values(ComputerName) as target_hosts by _time, Source_Network_Address, Source_Account | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std by Source_Network_Address, Source_Account | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) | `unusual_number_of_remote_endpoint_authentication_events_filter`

[ESCU - Unusually Long Command Line - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects command lines that are extremely long, which might be indicative of malicious activity on your hosts because attackers often use obfuscated or complex command lines to hide their actions and evade detection. This helps to mitigate the risks associated with long command lines to enhance your overall security posture and reduce the impact of attacks. This detection is important because it suggests that an attacker might be attempting to execute a malicious command or payload on the host, which can lead to various damaging outcomes such as data theft, ransomware, or further compromise of the system. False positives might occur since legitimate processes or commands can sometimes result in long command lines. Next steps include conducting extensive triage and investigation to differentiate between legitimate and malicious activities. Review the source of the command line and the command itself during the triage. Additionally, capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects command lines that are extremely long, which might be indicative of malicious activity on your hosts because attackers often use obfuscated or complex command lines to hide their actions and evade detection. This helps to mitigate the risks associated with long command lines to enhance your overall security posture and reduce the impact of attacks. This detection is important because it suggests that an attacker might be attempting to execute a malicious command or payload on the host, which can lead to various damaging outcomes such as data theft, ransomware, or further compromise of the system. False positives might occur since legitimate processes or commands can sometimes result in long command lines. Next steps include conducting extensive triage and investigation to differentiate between legitimate and malicious activities. Review the source of the command line and the command itself during the triage. Additionally, capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the source of the attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate applications start with long command lines.
action.escu.creation_date = 2020-12-08
action.escu.modification_date = 2020-12-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unusually Long Command Line - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Unusually long command line $process_name$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unusually Long Command Line - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"], "cis20": ["CIS 10"], "confidence": 60, "impact": 70, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|  eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost)

[ESCU - Unusually Long Command Line - MLTK - Rule]
action.escu = 0
action.escu.enabled = 1
description = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user.
action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model.
action.escu.creation_date = 2019-05-08
action.escu.modification_date = 2019-05-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unusually Long Command Line - MLTK - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unusually Long Command Line - MLTK - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter`

[ESCU - User Discovery With Env Vars PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `powershell.exe` with command-line arguments that leverage PowerShell environment variables to identify the current logged user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `powershell.exe` with command-line arguments that leverage PowerShell environment variables to identify the current logged user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - User Discovery With Env Vars PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - User Discovery With Env Vars PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process="*$env:UserName*" OR Processes.process="*[System.Environment]::UserName*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `user_discovery_with_env_vars_powershell_filter`

[ESCU - User Discovery With Env Vars PowerShell Script Block - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the use of PowerShell environment variables to identify the current logged user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the use of PowerShell environment variables to identify the current logged user. Red Teams and adversaries may leverage this method to identify the logged user on a compromised endpoint for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators or power users may use this PowerShell commandlet for troubleshooting.
action.escu.creation_date = 2022-03-22
action.escu.modification_date = 2022-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - User Discovery With Env Vars PowerShell Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - User Discovery With Env Vars PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText = "*$env:UserName*" OR ScriptBlockText = "*[System.Environment]::UserName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `user_discovery_with_env_vars_powershell_script_block_filter`

[ESCU - USN Journal Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2018-12-03
action.escu.modification_date = 2018-12-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - USN Journal Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = Possible USN journal deletion on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - USN Journal Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 50, "mitre_attack": ["T1070"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal.
action.notable.param.rule_title = USN Journal Deletion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter`

[ESCU - Vbscript Execution Using Wscript App - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious wscript commandline to execute vbscript. This technique was seen in several malware to execute malicious vbs file using wscript application. commonly vbs script is associated to cscript process and this can be a technique to evade process parent child detections or even some av script emulation system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.005", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious wscript commandline to execute vbscript. This technique was seen in several malware to execute malicious vbs file using wscript application. commonly vbs script is associated to cscript process and this can be a technique to evade process parent child detections or even some av script emulation system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-10-01
action.escu.modification_date = 2021-10-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Vbscript Execution Using Wscript App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["FIN7", "Remcos", "AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ with commandline $process$ to execute vbsscript
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Vbscript Execution Using Wscript App - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7", "Remcos", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1059.005", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to detect a suspicious wscript commandline to execute vbscript. This technique was seen in several malware to execute malicious vbs file using wscript application. commonly vbs script is associated to cscript process and this can be a technique to evade process parent child detections or even some av script emulation system.
action.notable.param.rule_title = Vbscript Execution Using Wscript App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "wscript.exe" AND Processes.parent_process = "*//e:vbscript*") OR (Processes.process_name = "wscript.exe" AND Processes.process = "*//e:vbscript*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vbscript_execution_using_wscript_app_filter`

[ESCU - Verclsid CLSID Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a possible abuse of verclsid to execute malicious file through generate CLSID. This process is a normal application of windows to verify the CLSID COM object before it is instantiated by Windows Explorer. This hunting query can be a good pivot point to analyze what is he CLSID or COM object pointing too to check if it is a valid application or not.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.012", "T1218"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a possible abuse of verclsid to execute malicious file through generate CLSID. This process is a normal application of windows to verify the CLSID COM object before it is instantiated by Windows Explorer. This hunting query can be a good pivot point to analyze what is he CLSID or COM object pointing too to check if it is a valid application or not.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = windows can used this application for its normal COM object validation.
action.escu.creation_date = 2021-09-29
action.escu.modification_date = 2021-09-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Verclsid CLSID Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Verclsid CLSID Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1218.012", "T1218"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_verclsid` AND Processes.process="*/S*" Processes.process="*/C*" AND  Processes.process="*{*" AND Processes.process="*}*" by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `verclsid_clsid_execution_filter`

[ESCU - W3WP Spawning Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Baseline your environment before production. It is possible build systems using IIS will spawn cmd.exe to perform a software build. Filter as needed.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - W3WP Spawning Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["ProxyNotShell", "Data Destruction", "ProxyShell", "Hermetic Wiper", "CISA AA22-257A", "HAFNIUM Group", "BlackByte Ransomware", "CISA AA22-264A", "Flax Typhoon", "WS FTP Server Critical Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Possible Web Shell execution on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - W3WP Spawning Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyNotShell", "Data Destruction", "ProxyShell", "Hermetic Wiper", "CISA AA22-257A", "HAFNIUM Group", "BlackByte Ransomware", "CISA AA22-264A", "Flax Typhoon", "WS FTP Server Critical Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 80, "cve": ["CVE-2021-34473", "CVE-2021-34523", "CVE-2021-31207"], "impact": 70, "mitre_attack": ["T1505", "T1505.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable.
action.notable.param.rule_title = W3WP Spawning Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=w3wp.exe AND `process_cmd` OR `process_powershell` by Processes.dest Processes.parent_process Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `w3wp_spawning_shell_filter`

[ESCU - WBAdmin Delete System Backups - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may modify the boot configuration.
action.escu.creation_date = 2021-01-22
action.escu.modification_date = 2021-01-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - WBAdmin Delete System Backups - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware", "Ransomware", "Prestige Ransomware", "Chaos Ransomware"]
action.risk = 1
action.risk.param._risk_message = System backups deletion on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WBAdmin Delete System Backups - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Ransomware", "Prestige Ransomware", "Chaos Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1490"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery.
action.notable.param.rule_title = WBAdmin Delete System Backups
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter`

[ESCU - Wbemprox COM Object Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a potential suspicious process loading a COM object from wbemprox.dll or faskprox.dll. The Microsoft Component Object Model (COM) is a platform-independent, distributed, object-oriented system for creating binary software components that can interact. This feature is being abused by several threat actors, adversaries or even red teamers to gain privilege escalation or even to evade detections. This TTP is a good indicator that a process is loading possible known .dll modules that were known for its COM object.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218", "T1218.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a potential suspicious process loading a COM object from wbemprox.dll or faskprox.dll. The Microsoft Component Object Model (COM) is a platform-independent, distributed, object-oriented system for creating binary software components that can interact. This feature is being abused by several threat actors, adversaries or even red teamers to gain privilege escalation or even to evade detections. This TTP is a good indicator that a process is loading possible known .dll modules that were known for its COM object.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = legitimate process that are not in the exception list may trigger this event.
action.escu.creation_date = 2021-06-02
action.escu.modification_date = 2021-06-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wbemprox COM Object Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ransomware", "Revil Ransomware", "LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = Suspicious COM Object Execution on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wbemprox COM Object Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware", "LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218", "T1218.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a potential suspicious process loading a COM object from wbemprox.dll or faskprox.dll. The Microsoft Component Object Model (COM) is a platform-independent, distributed, object-oriented system for creating binary software components that can interact. This feature is being abused by several threat actors, adversaries or even red teamers to gain privilege escalation or even to evade detections. This TTP is a good indicator that a process is loading possible known .dll modules that were known for its COM object.
action.notable.param.rule_title = Wbemprox COM Object Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7  ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_filter`

[ESCU - Wermgr Process Connecting To IP Check Web Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is designed to detect suspicious wermgr.exe process that tries to connect to known IP web services. This technique is know for trickbot and other trojan spy malware to recon the infected machine and look for its ip address without so much finger print on the commandline process. Since wermgr.exe is designed for error handling process of windows it is really suspicious that this process is trying to connect to this IP web services cause that maybe cause of some malicious code injection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1590", "T1590.005"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is designed to detect suspicious wermgr.exe process that tries to connect to known IP web services. This technique is know for trickbot and other trojan spy malware to recon the infected machine and look for its ip address without so much finger print on the commandline process. Since wermgr.exe is designed for error handling process of windows it is really suspicious that this process is trying to connect to this IP web services cause that maybe cause of some malicious code injection.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-06-01
action.escu.modification_date = 2022-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wermgr Process Connecting To IP Check Web Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Trickbot"]
action.risk = 1
action.risk.param._risk_message = Wermgr.exe process connecting IP location web services on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wermgr Process Connecting To IP Check Web Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1590", "T1590.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is designed to detect suspicious wermgr.exe process that tries to connect to known IP web services. This technique is know for trickbot and other trojan spy malware to recon the infected machine and look for its ip address without so much finger print on the commandline process. Since wermgr.exe is designed for error handling process of windows it is really suspicious that this process is trying to connect to this IP web services cause that maybe cause of some malicious code injection.
action.notable.param.rule_title = Wermgr Process Connecting To IP Check Web Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") |  stats  min(_time) as firstTime max(_time) as lastTime count by Image process_name ProcessId QueryName QueryStatus QueryResults EventCode Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_connecting_to_ip_check_web_services_filter`

[ESCU - Wermgr Process Create Executable File - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is designed to detect potential malicious wermgr.exe process that drops or create executable file. Since wermgr.exe is an application trigger when error encountered in a process, it is really un ussual to this process to drop executable file. This technique is commonly seen in trickbot malware where it injects it code to this process to execute it malicious behavior like downloading other payload
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = this search is designed to detect potential malicious wermgr.exe process that drops or create executable file. Since wermgr.exe is an application trigger when error encountered in a process, it is really un ussual to this process to drop executable file. This technique is commonly seen in trickbot malware where it injects it code to this process to execute it malicious behavior like downloading other payload
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of wermgr.exe may be used.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-04-19
action.escu.modification_date = 2021-04-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wermgr Process Create Executable File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Trickbot"]
action.risk = 1
action.risk.param._risk_message = Wermgr.exe writing executable files on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wermgr Process Create Executable File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1027"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = this search is designed to detect potential malicious wermgr.exe process that drops or create executable file. Since wermgr.exe is an application trigger when error encountered in a process, it is really un ussual to this process to drop executable file. This technique is commonly seen in trickbot malware where it injects it code to this process to execute it malicious behavior like downloading other payload
action.notable.param.rule_title = Wermgr Process Create Executable File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" | stats  min(_time) as firstTime max(_time) as lastTime count by  Image TargetFilename process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_create_executable_file_filter`

[ESCU - Wermgr Process Spawned CMD Or Powershell Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is designed to detect suspicious cmd and powershell process spawned by wermgr.exe process. This suspicious behavior are commonly seen in code injection technique technique like trickbot to execute a shellcode, dll modules to run malicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is designed to detect suspicious cmd and powershell process spawned by wermgr.exe process. This suspicious behavior are commonly seen in code injection technique technique like trickbot to execute a shellcode, dll modules to run malicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-04-19
action.escu.modification_date = 2021-04-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wermgr Process Spawned CMD Or Powershell Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Trickbot", "Qakbot"]
action.risk = 1
action.risk.param._risk_message = Wermgr.exe spawning suspicious processes on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wermgr Process Spawned CMD Or Powershell Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "Qakbot"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is designed to detect suspicious cmd and powershell process spawned by wermgr.exe process. This suspicious behavior are commonly seen in code injection technique technique like trickbot to execute a shellcode, dll modules to run malicious behavior.
action.notable.param.rule_title = Wermgr Process Spawned CMD Or Powershell Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" `process_cmd` OR `process_powershell` by Processes.parent_process_name  Processes.original_file_name Processes.parent_process_id  Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_spawned_cmd_or_powershell_process_filter`

[ESCU - Wget Download and Bash Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of wget on Linux or MacOS attempting to download a file from a remote source and pipe it to bash. This is typically found with coinminers and most recently with CVE-2021-44228, a vulnerability in Log4j.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of wget on Linux or MacOS attempting to download a file from a remote source and pipe it to bash. This is typically found with coinminers and most recently with CVE-2021-44228, a vulnerability in Log4j.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, however filtering may be required.
action.escu.creation_date = 2021-12-11
action.escu.modification_date = 2021-12-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wget Download and Bash Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ingress Tool Transfer", "Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ attempting to download a remote file and run it with bash.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wget Download and Bash Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "Log4Shell CVE-2021-44228"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2021-44228"], "impact": 80, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of wget on Linux or MacOS attempting to download a file from a remote source and pipe it to bash. This is typically found with coinminers and most recently with CVE-2021-44228, a vulnerability in Log4j.
action.notable.param.rule_title = Wget Download and Bash Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wget (Processes.process="*-q *" OR Processes.process="*--quiet*"  AND Processes.process="*-O- *") OR (Processes.process="*|*" AND Processes.process="*bash*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wget_download_and_bash_execution_filter`

[ESCU - Windows Abused Web Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a suspicious process making a DNS query via known, abused text-paste web services, VoIP, internet via secure tunneling,instant messaging, and digital distribution platforms used to download external files. This technique is abused by adversaries, malware actors, and red teams to download a malicious file on the target host. This is a good TTP indicator for possible initial access techniques. A user will experience false positives if the following instant messaging is allowed or common applications like telegram or discord are allowed in the corporate network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1102"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects a suspicious process making a DNS query via known, abused text-paste web services, VoIP, internet via secure tunneling,instant messaging, and digital distribution platforms used to download external files. This technique is abused by adversaries, malware actors, and red teams to download a malicious file on the target host. This is a good TTP indicator for possible initial access techniques. A user will experience false positives if the following instant messaging is allowed or common applications like telegram or discord are allowed in the corporate network.
action.escu.how_to_implement = This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days.
action.escu.known_false_positives = Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
action.escu.creation_date = 2023-09-20
action.escu.modification_date = 2023-09-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Abused Web Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = a network connection on known abused web services from $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Abused Web Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1102"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects a suspicious process making a DNS query via known, abused text-paste web services, VoIP, internet via secure tunneling,instant messaging, and digital distribution platforms used to download external files. This technique is abused by adversaries, malware actors, and red teams to download a malicious file on the target host. This is a good TTP indicator for possible initial access techniques. A user will experience false positives if the following instant messaging is allowed or common applications like telegram or discord are allowed in the corporate network.
action.notable.param.rule_title = Windows Abused Web Services
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22 QueryName IN ("*pastebin*",""*textbin*"", "*ngrok.io*", "*discord*", "*duckdns.org*", "*pasteio.com*") | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_abused_web_services_filter`

[ESCU - Windows Access Token Manipulation SeDebugPrivilege - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious process enabling the "SeDebugPrivilege" privilege token. SeDebugPrivilege allows a process to inspect and adjust the memory of other processes, and has long been a security concern. SeDebugPrivilege allows the token bearer to access any process or thread, regardless of security descriptors, per Palantir. This technique is abused by adversaries to gain debug privileges with their malicious software to be able to access or debug a process to dump credentials or to inject malicious code.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134.002", "T1134"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious process enabling the "SeDebugPrivilege" privilege token. SeDebugPrivilege allows a process to inspect and adjust the memory of other processes, and has long been a security concern. SeDebugPrivilege allows the token bearer to access any process or thread, regardless of security descriptors, per Palantir. This technique is abused by adversaries to gain debug privileges with their malicious software to be able to access or debug a process to dump credentials or to inject malicious code.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4703 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = Some native binaries and browser applications may request SeDebugPrivilege. Filter as needed.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Access Token Manipulation SeDebugPrivilege - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Brute Ratel C4", "AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = A process $ProcessName$ adjust its privileges with SeDebugPrivilege on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Access Token Manipulation SeDebugPrivilege - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1134.002", "T1134"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4703 EnabledPrivilegeList = "*SeDebugPrivilege*" AND NOT(ProcessName IN ("*\\Program File*", "*\\System32\\lsass.exe*", "*\\SysWOW64\\lsass.exe*", "*\\SysWOW64\\svchost.exe*", "*\\System32\\svchost.exe*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer ProcessName ProcessId SubjectDomainName SubjectUserName SubjectUserSid TargetUserName TargetLogonId TargetDomainName EnabledPrivilegeList action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_sedebugprivilege_filter`

[ESCU - Windows Access Token Manipulation Winlogon Duplicate Token Handle - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process requesting access to winlogon.exe attempting to duplicate its handle. This technique was seen in several adversaries to gain privileges for their process. Winlogon.exe is the common targeted process of this technique because it contains high privileges and security tokens.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134.001", "T1134"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a process requesting access to winlogon.exe attempting to duplicate its handle. This technique was seen in several adversaries to gain privileges for their process. Winlogon.exe is the common targeted process of this technique because it contains high privileges and security tokens.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = It is possible legitimate applications will request access to winlogon, filter as needed.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Access Token Manipulation Winlogon Duplicate Token Handle - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Access Token Manipulation Winlogon Duplicate Token Handle - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1134.001", "T1134"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") GrantedAccess = 0x1040 | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId GrantedAccess CallTrace dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_winlogon_duplicate_token_handle_filter`

[ESCU - Windows Access Token Winlogon Duplicate Handle In Uncommon Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process requesting access in winlogon.exe to duplicate its handle with a non-common or public process source path. This technique was seen where adversaries attempt to gain privileges to their process. This duplicate handle access technique, may refer to a malicious process duplicating the process token of winlogon.exe and using it to a new process instance. Winlogon.exe is the common targeted process of this technique because it contains high privileges and security tokens.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134.001", "T1134"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a process requesting access in winlogon.exe to duplicate its handle with a non-common or public process source path. This technique was seen where adversaries attempt to gain privileges to their process. This duplicate handle access technique, may refer to a malicious process duplicating the process token of winlogon.exe and using it to a new process instance. Winlogon.exe is the common targeted process of this technique because it contains high privileges and security tokens.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = It is possible legitimate applications will request access to winlogon, filter as needed.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Access Token Winlogon Duplicate Handle In Uncommon Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
action.risk = 1
action.risk.param._risk_message = A process $SourceImage$ is duplicating the handle token of winlogon.exe on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "SourceImage", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Access Token Winlogon Duplicate Handle In Uncommon Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1134.001", "T1134"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10  TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") AND GrantedAccess = 0x1040 AND NOT (SourceImage IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId GrantedAccess CallTrace | rename Computer as dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_winlogon_duplicate_handle_in_uncommon_path_filter`

[ESCU - Windows AD Abnormal Object Access Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = Windows Active Directory contains numerous objects. A statistically significant increase in access to these objects may be evidence of attacker enumeration of Active Directory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = Windows Active Directory contains numerous objects. A statistically significant increase in access to these objects may be evidence of attacker enumeration of Active Directory.
action.escu.how_to_implement = Enable Audit Directory Service Access via GPO and collect event code 4662. The required SACLs need to be created for the relevant objects. Be aware Splunk filters this event by default on the Windows TA. Recommend pre-filtering any known service accounts that frequently query AD to make detection more accurate. Setting wide search window of 48~72hr may smooth out misfires.
action.escu.known_false_positives = Service accounts or applications that routinely query Active Directory for information.
action.escu.creation_date = 2023-06-01
action.escu.modification_date = 2023-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Abnormal Object Access Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = The account $user$ accessed an abnormal amount ($ObjectName_count$) of [$ObjectType$] AD object(s) between $firstTime$ and $lastTime$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Abnormal Object Access Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4662 | stats min(_time) AS firstTime, max(_time) AS lastTime, dc(ObjectName) AS ObjectName_count, values(ObjectType) AS ObjectType, latest(Computer) AS dest count BY SubjectUserName | eventstats avg(ObjectName_count) AS average stdev(ObjectName_count) AS standarddev | eval limit = round((average+(standarddev*3)),0), user = SubjectUserName | where ObjectName_count > limit | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_ad_abnormal_object_access_activity_filter`

[ESCU - Windows AD AdminSDHolder ACL Modified - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the modification of the Access Control List for the AdminSDHolder object within a Windows domain. Specifically, the detection triggers on the addition of a new rule to the existing ACL. AdminSDHolder is an object located in the System Partition in Active Directory and is used as a security template for objects that are members of certain privileged groups. Objects in these groups are enumerated and any objects with security descriptors that dont match the AdminSDHolder ACL are flagged for updating. The Security Descriptor propagator (SDProp) process runs every 60 minutes on the PDC Emulator and re-stamps the object Access Control List (ACL) with the security permissions set on the AdminSDHolder. An adversary who has obtained privileged access to a Windows Domain may modify the AdminSDHolder ACL to establish persistence and allow an unprivileged user to take control of a domain.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the modification of the Access Control List for the AdminSDHolder object within a Windows domain. Specifically, the detection triggers on the addition of a new rule to the existing ACL. AdminSDHolder is an object located in the System Partition in Active Directory and is used as a security template for objects that are members of certain privileged groups. Objects in these groups are enumerated and any objects with security descriptors that dont match the AdminSDHolder ACL are flagged for updating. The Security Descriptor propagator (SDProp) process runs every 60 minutes on the PDC Emulator and re-stamps the object Access Control List (ACL) with the security permissions set on the AdminSDHolder. An adversary who has obtained privileged access to a Windows Domain may modify the AdminSDHolder ACL to establish persistence and allow an unprivileged user to take control of a domain.
action.escu.how_to_implement = To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for the AdminSDHolder object in order to log modifications.
action.escu.known_false_positives = Adding new users or groups to the AdminSDHolder ACL is not usual. Filter as needed
action.escu.creation_date = 2022-11-15
action.escu.modification_date = 2022-11-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD AdminSDHolder ACL Modified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = The AdminSDHolder domain object has been modified on $Computer$ by $SubjectUserName$
action.risk.param._risk = [{"risk_object_field": "SubjectUserName", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD AdminSDHolder ACL Modified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the modification of the Access Control List for the AdminSDHolder object within a Windows domain. Specifically, the detection triggers on the addition of a new rule to the existing ACL. AdminSDHolder is an object located in the System Partition in Active Directory and is used as a security template for objects that are members of certain privileged groups. Objects in these groups are enumerated and any objects with security descriptors that dont match the AdminSDHolder ACL are flagged for updating. The Security Descriptor propagator (SDProp) process runs every 60 minutes on the PDC Emulator and re-stamps the object Access Control List (ACL) with the security permissions set on the AdminSDHolder. An adversary who has obtained privileged access to a Windows Domain may modify the AdminSDHolder ACL to establish persistence and allow an unprivileged user to take control of a domain.
action.notable.param.rule_title = Windows AD AdminSDHolder ACL Modified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=nTSecurityDescriptor OperationType="%%14674" ObjectDN="CN=AdminSDHolder,CN=System*" | rex field=AttributeValue max_match=10000 "A;;CCDCLCSWRPWPDTLOCRSDRCWDWO;;;(?P<added_user_sid>S-1-[0-59]-\d{2}-\d{8,10}-\d{8,10}-\d{8,10}-[1-9]\d{3})\)" | stats values(added_user_sid) by _time, Computer, SubjectUserName, ObjectDN | `windows_ad_adminsdholder_acl_modified_filter`

[ESCU - Windows AD Cross Domain SID History Addition - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic looks for changes to the sIDHistory AD attribute of user or computer objects within different domains. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries for inter-domain privilege escalation and persistence.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134.005", "T1134"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic looks for changes to the sIDHistory AD attribute of user or computer objects within different domains. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries for inter-domain privilege escalation and persistence.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit User Account Management` and  `Audit Computer Account Management` within `Account Management` all need to be enabled.
action.escu.known_false_positives = Domain mergers and migrations may generate large volumes of false positives for this analytic.
action.escu.creation_date = 2022-11-17
action.escu.modification_date = 2022-11-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Cross Domain SID History Addition - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = Active Directory SID History Attribute was added to $user$ by $src_user$
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Cross Domain SID History Addition - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1134.005", "T1134"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic looks for changes to the sIDHistory AD attribute of user or computer objects within different domains. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries for inter-domain privilege escalation and persistence.
action.notable.param.rule_title = Windows AD Cross Domain SID History Addition
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistoryMatch>.*)(\-|\\\)" | rex field=TargetSid "^(?P<TargetSidmatch>.*)(\-|\\\)" | where SidHistoryMatch!=TargetSidmatch AND SidHistoryMatch!=TargetDomainName | rename TargetSid as userSid | table _time action status host user userSid SidHistory Logon_ID src_user | `windows_ad_cross_domain_sid_history_addition_filter`

[ESCU - Windows AD Domain Controller Audit Policy Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the disabling of audit policies on a domain controller. The detection is made by identifying changes made to audit policies and checks for the removal of success or failure auditing, which are common indicators of policy tampering. The detection is important because it indicates that an attacker has gained access to the domain controller and is attempting to evade detection and cover up malicious activity. The impact of such an attack can be severe, including data theft, privilege escalation, and compromise of the entire network. False positives might occur since legitimate changes to audit policies might also trigger the analytic. Upon triage, review the audit policy change event and investigate the source of the change. Additionally, you must capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the attack source."
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic detects the disabling of audit policies on a domain controller. The detection is made by identifying changes made to audit policies and checks for the removal of success or failure auditing, which are common indicators of policy tampering. The detection is important because it indicates that an attacker has gained access to the domain controller and is attempting to evade detection and cover up malicious activity. The impact of such an attack can be severe, including data theft, privilege escalation, and compromise of the entire network. False positives might occur since legitimate changes to audit policies might also trigger the analytic. Upon triage, review the audit policy change event and investigate the source of the change. Additionally, you must capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the attack source."
action.escu.how_to_implement = Ensure you are ingesting EventCode `4719` from your domain controllers, the category domain_controller exists in assets and identities, and that assets and identities is enabled. If A&I is not configured, you will need to manually filter the results within the base search.
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2023-01-26
action.escu.modification_date = 2023-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Domain Controller Audit Policy Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = GPO $SubCategory$ of $Category$ was disabled on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Domain Controller Audit Policy Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 100, "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the disabling of audit policies on a domain controller. The detection is made by identifying changes made to audit policies and checks for the removal of success or failure auditing, which are common indicators of policy tampering. The detection is important because it indicates that an attacker has gained access to the domain controller and is attempting to evade detection and cover up malicious activity. The impact of such an attack can be severe, including data theft, privilege escalation, and compromise of the entire network. False positives might occur since legitimate changes to audit policies might also trigger the analytic. Upon triage, review the audit policy change event and investigate the source of the change. Additionally, you must capture and inspect any relevant on-disk artifacts and review concurrent processes to identify the attack source."
action.notable.param.rule_title = Windows AD Domain Controller Audit Policy Disabled
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448, %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure removed")) dest_category="domain_controller"| replace "%%8448" with "Success removed", "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure removed" in AuditPolicyChanges | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes), SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) | stats min(_time) as _time values(host) as dest by AuditPolicyChanges SubcategoryGuid | lookup advanced_audit_policy_guids GUID as SubcategoryGuid OUTPUT Category SubCategory | `windows_ad_domain_controller_audit_policy_disabled_filter`

[ESCU - Windows AD Domain Controller Promotion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a genuine DC promotion event. Identifying when a computer assigns itself the necessary SPNs to function as a domain controller. Note these events are triggered on the existing domain controllers, not the newly joined domain controller. This detection will serve to identify rogue DCs added to the network. There are 2x detections within this analytic story which identify DCShadow attacks, if you do not currently possess the logging for these detections, remove the where clause within this detection to identify DCShadow activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies a genuine DC promotion event. Identifying when a computer assigns itself the necessary SPNs to function as a domain controller. Note these events are triggered on the existing domain controllers, not the newly joined domain controller. This detection will serve to identify rogue DCs added to the network. There are 2x detections within this analytic story which identify DCShadow attacks, if you do not currently possess the logging for these detections, remove the where clause within this detection to identify DCShadow activity.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcode `4742`. The Advanced Security Audit policy setting `Audit Computer Account Management` within `Account Management` needs to be enabled.
action.escu.known_false_positives = None.
action.escu.creation_date = 2023-01-26
action.escu.modification_date = 2023-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Domain Controller Promotion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = AD Domain Controller Promotion Event Detected for $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Domain Controller Promotion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a genuine DC promotion event. Identifying when a computer assigns itself the necessary SPNs to function as a domain controller. Note these events are triggered on the existing domain controllers, not the newly joined domain controller. This detection will serve to identify rogue DCs added to the network. There are 2x detections within this analytic story which identify DCShadow attacks, if you do not currently possess the logging for these detections, remove the where clause within this detection to identify DCShadow activity.
action.notable.param.rule_title = Windows AD Domain Controller Promotion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4742 ServicePrincipalNames IN ("*E3514235-4B06-11D1-AB04-00C04FC2DCD2/*","*GC/*")| stats min(_time) as _time latest(ServicePrincipalNames) as ServicePrincipalNames,values(signature) as signature, values(src_user) as src_user, values(user) as user by Logon_ID, dvc| where src_user=user| rename Logon_ID as TargetLogonId, user as dest | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$" | fields - dest, dvc, signature]| stats min(_time) as _time, values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip values(ServicePrincipalNames) as ServicePrincipalNames values(signature) as signature values(dest) as dest values(dvc) as dvc by TargetLogonId | eval dest=trim(dest,"$") | `windows_ad_domain_controller_promotion_filter`

[ESCU - Windows AD Domain Replication ACL Addition - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the addition of the permissions necessary to perform a DCSync attack. In order to replicate AD objects, the initiating user or computer must have the following permissions on the domain. - DS-Replication-Get-Changes - DS-Replication-Get-Changes-All Certain Sync operations may require the additional permission of DS-Replication-Get-Changes-In-Filtered-Set. By default, adding DCSync permissions via the Powerview Add-ObjectACL operation adds all 3. This alert identifies where this trifecta has been met, and also where just the base level requirements have been met.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic detects the addition of the permissions necessary to perform a DCSync attack. In order to replicate AD objects, the initiating user or computer must have the following permissions on the domain. - DS-Replication-Get-Changes - DS-Replication-Get-Changes-All Certain Sync operations may require the additional permission of DS-Replication-Get-Changes-In-Filtered-Set. By default, adding DCSync permissions via the Powerview Add-ObjectACL operation adds all 3. This alert identifies where this trifecta has been met, and also where just the base level requirements have been met.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting the eventcode 5136. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` to  `Write All Properties` applied to the domain root and all descendant objects. Once the necessary logging has been enabled, enumerate the domain policy to verify if existing accounts with access need to be whitelisted, or revoked. Assets and Identities is also leveraged to automatically translate the objectSid into username. Ensure your identities lookup is configured with the sAMAccountName and objectSid of all AD user and computer objects.
action.escu.known_false_positives = When there is a change to nTSecurityDescriptor, Windows logs the entire ACL with the newly added components. If existing accounts are present with this permission, they will raise an alert each time the nTSecurityDescriptor is updated unless whitelisted.
action.escu.creation_date = 2022-11-18
action.escu.modification_date = 2022-11-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Domain Replication ACL Addition - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = $src_user$ has granted $user$ permission to replicate AD objects
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Domain Replication ACL Addition - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 100, "mitre_attack": ["T1484"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the addition of the permissions necessary to perform a DCSync attack. In order to replicate AD objects, the initiating user or computer must have the following permissions on the domain. - DS-Replication-Get-Changes - DS-Replication-Get-Changes-All Certain Sync operations may require the additional permission of DS-Replication-Get-Changes-In-Filtered-Set. By default, adding DCSync permissions via the Powerview Add-ObjectACL operation adds all 3. This alert identifies where this trifecta has been met, and also where just the base level requirements have been met.
action.notable.param.rule_title = Windows AD Domain Replication ACL Addition
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security`  | rex field=AttributeValue max_match=10000 \"OA;;CR;89e95b76-444d-4c62-991a-0facbeda640c;;(?P<DSRGetChangesFiltered_user_sid>S-1-[0-59]-\d{2}-\d{8,10}-\d{8,10}-\d{8,10}-[1-9]\d{3})\)\"| table _time dest src_user DSRGetChanges_user_sid DSRGetChangesAll_user_sid DSRGetChangesFiltered_user_sid| mvexpand DSRGetChanges_user_sid| eval minDCSyncPermissions=if(DSRGetChanges_user_sid=DSRGetChangesAll_user_sid,\"true\",\"false\"), fullSet=if(DSRGetChanges_user_sid=DSRGetChangesAll_user_sid AND DSRGetChanges_user_sid=DSRGetChangesFiltered_user_sid,\"true\",\"false\")| where minDCSyncPermissions=\"true\" | lookup identity_lookup_expanded  objectSid as DSRGetChanges_user_sid OUTPUT sAMAccountName as user | rename DSRGetChanges_user_sid as userSid | stats min(_time) as _time values(user) as user by dest src_user userSid minDCSyncPermissions fullSet| `windows_ad_domain_replication_acl_addition_filter`

[ESCU - Windows AD DSRM Account Changes - Rule]
action.escu = 0
action.escu.enabled = 1
description = Aside from being used to promote genuine domain controllers, the DSRM (Directory Services Restore Mode) account can be used to persist within a Domain. A DC can be configured to allow the DSRM account to logon & be used in the same way as a local administrator account. This detection is looking for alterations to the behaviour of the account via registry.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Aside from being used to promote genuine domain controllers, the DSRM (Directory Services Restore Mode) account can be used to persist within a Domain. A DC can be configured to allow the DSRM account to logon & be used in the same way as a local administrator account. This detection is looking for alterations to the behaviour of the account via registry.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Disaster recovery events.
action.escu.creation_date = 2022-09-08
action.escu.modification_date = 2022-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD DSRM Account Changes - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks", "Windows Registry Abuse", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = DSRM Account Changes Initiated on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD DSRM Account Changes - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks", "Windows Registry Abuse", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Aside from being used to promote genuine domain controllers, the DSRM (Directory Services Restore Mode) account can be used to persist within a Domain. A DC can be configured to allow the DSRM account to logon & be used in the same way as a local administrator account. This detection is looking for alterations to the behaviour of the account via registry.
action.notable.param.rule_title = Windows AD DSRM Account Changes
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" Registry.registry_value_data IN ("*1","*2") by Registry.action Registry.registry_path Registry.registry_value_data Registry.registry_value_type Registry.process_guid | `drop_dm_object_name(Registry)` | join type=outer process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`] | table _time action dest user parent_process_name parent_process process_name process process_guid registry_path registry_value_data registry_value_type | `windows_ad_dsrm_account_changes_filter`

[ESCU - Windows AD DSRM Password Reset - Rule]
action.escu = 0
action.escu.enabled = 1
description = Aside from being used to promote genuine domain controllers, the DSRM (Directory Services Restore Mode) account can be used to persist within a Domain. A DC can be configured to allow the DSRM account to logon & be used in the same way as a local administrator account. This detection is looking for any password reset attempts against that account.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = Aside from being used to promote genuine domain controllers, the DSRM (Directory Services Restore Mode) account can be used to persist within a Domain. A DC can be configured to allow the DSRM account to logon & be used in the same way as a local administrator account. This detection is looking for any password reset attempts against that account.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcode `4794` and  have the Advanced Security Audit policy `Audit User Account Management` within `Account Management` enabled.
action.escu.known_false_positives = Resetting the DSRM password for legitamate reasons, i.e. forgot the password. Disaster recovery. Deploying AD backdoor deliberately.
action.escu.creation_date = 2022-09-08
action.escu.modification_date = 2022-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD DSRM Password Reset - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = DSRM Account Password was reset on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD DSRM Password Reset - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Aside from being used to promote genuine domain controllers, the DSRM (Directory Services Restore Mode) account can be used to persist within a Domain. A DC can be configured to allow the DSRM account to logon & be used in the same way as a local administrator account. This detection is looking for any password reset attempts against that account.
action.notable.param.rule_title = Windows AD DSRM Password Reset
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as _time from datamodel=Change where All_Changes.result_id="4794" AND All_Changes.result="An attempt was made to set the Directory Services Restore Mode administrator password" by All_Changes.action, All_Changes.dest, All_Changes.src, All_Changes.user | `drop_dm_object_name(All_Changes)` | `windows_ad_dsrm_password_reset_filter`

[ESCU - Windows AD Privileged Account SID History Addition - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection identifies when the SID of a privileged user is added to the SID History attribute of another user. Useful for tracking SID history abuse across multiple domains. This detection leverages the Asset and Identities framework. See the implementation section for further details on configuration.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134.005", "T1134"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This detection identifies when the SID of a privileged user is added to the SID History attribute of another user. Useful for tracking SID history abuse across multiple domains. This detection leverages the Asset and Identities framework. See the implementation section for further details on configuration.
action.escu.how_to_implement = Ensure you have objectSid and the Down Level Logon Name `DOMAIN\sAMACountName` added to the identity field of your Asset and Identities lookup, along with the category of privileged for the applicable users. Ensure you are ingesting eventcodes 4742 and 4738. Two advanced audit policies `Audit User Account Management` and `Audit Computer Account Management` under `Account Management` are required to generate these event codes.
action.escu.known_false_positives = Migration of privileged accounts.
action.escu.creation_date = 2022-09-12
action.escu.modification_date = 2022-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Privileged Account SID History Addition - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A Privileged User Account SID History Attribute was added to $user$ by $src_user$
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Privileged Account SID History Addition - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1134.005", "T1134"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection identifies when the SID of a privileged user is added to the SID History attribute of another user. Useful for tracking SID history abuse across multiple domains. This detection leverages the Asset and Identities framework. See the implementation section for further details on configuration.
action.notable.param.rule_title = Windows AD Privileged Account SID History Addition
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistory>.*?)(}$|$)" | eval category="privileged" | lookup identity_lookup_expanded category, identity as SidHistory OUTPUT identity_tag as match | where isnotnull(match) | rename TargetSid as userSid | table _time action status host user userSid SidHistory Logon_ID src_user | `windows_ad_privileged_account_sid_history_addition_filter`

[ESCU - Windows AD Privileged Object Access Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = Windows Active Directory contains numerous objects that grant elevated access to the domain they reside in. These objects should be rarely accessed by normal users or processes. Access attempts to one or more of these objects may be evidence of attacker enumeration of Active Directory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Windows Active Directory contains numerous objects that grant elevated access to the domain they reside in. These objects should be rarely accessed by normal users or processes. Access attempts to one or more of these objects may be evidence of attacker enumeration of Active Directory.
action.escu.how_to_implement = Enable Audit Directory Service Access via GPO and collect event code 4662. The required SACLs need to be created for the relevant objects. Be aware Splunk filters this event by default on the Windows TA.
action.escu.known_false_positives = Service accounts or applications that routinely query Active Directory for information.
action.escu.creation_date = 2023-06-01
action.escu.modification_date = 2023-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Privileged Object Access Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = The account $user$ accessed $object_count$ privileged AD object(s).
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"threat_object_field": "object_name", "threat_object_type": "other"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Privileged Object Access Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Windows Active Directory contains numerous objects that grant elevated access to the domain they reside in. These objects should be rarely accessed by normal users or processes. Access attempts to one or more of these objects may be evidence of attacker enumeration of Active Directory.
action.notable.param.rule_title = Windows AD Privileged Object Access Activity
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4662 ObjectName IN ( "CN=Account Operators,*", "CN=Administrators,*", "CN=Backup Operators,*", "CN=Cert Publishers,*", "CN=Certificate Service DCOM Access,*", "CN=Domain Admins,*", "CN=Domain Controllers,*", "CN=Enterprise Admins,*", "CN=Enterprise Read-only Domain Controllers,*", "CN=Group Policy Creator Owners,*", "CN=Incoming Forest Trust Builders,*", "CN=Microsoft Exchange Servers,*", "CN=Network Configuration Operators,*", "CN=Power Users,*", "CN=Print Operators,*", "CN=Read-only Domain Controllers,*", "CN=Replicators,*", "CN=Schema Admins,*", "CN=Server Operators,*", "CN=Exchange Trusted Subsystem,*", "CN=Exchange Windows Permission,*", "CN=Organization Management,*") | rex field=ObjectName "CN\=(?<object_name>[^,]+)" | stats values(Computer) as dest, values(object_name) as object_name, dc(ObjectName) as object_count, min(_time) as firstTime, max(_time) as lastTime, count by SubjectUserName | rename SubjectUserName as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ad_privileged_object_access_activity_filter`

[ESCU - Windows AD Replication Request Initiated by User Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = This alert was written to detect activity associated with the DCSync attack. When a domain controller receives a replication request, the user account permissions are validated, however no checks are performed to validate the request was initiated by a Domain Controller. Once an attacker gains control of an account with the necessary privileges, they can request password hashes for any or all users within the domain. This alert detects when a user account creates a handle to domainDNS with the necessary replication permissions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.006", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication", "Change"]
action.escu.eli5 = This alert was written to detect activity associated with the DCSync attack. When a domain controller receives a replication request, the user account permissions are validated, however no checks are performed to validate the request was initiated by a Domain Controller. Once an attacker gains control of an account with the necessary privileges, they can request password hashes for any or all users within the domain. This alert detects when a user account creates a handle to domainDNS with the necessary replication permissions.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services Access` within `DS Access` needs to be enabled, as well as the following SACLs applied to the domain root and all descendant objects. The principals `everybody`,  `Domain Computers`, and  `Domain Controllers` auditing the permissions `Replicating Directory Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes In Filtered Set`
action.escu.known_false_positives = Azure AD Connect syncing operations.
action.escu.creation_date = 2022-09-08
action.escu.modification_date = 2022-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Replication Request Initiated by User Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks", "Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = Windows Active Directory Replication Request Initiated by User Account $user$ at $src_ip$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Replication Request Initiated by User Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks", "Credential Dumping"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1003.006", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This alert was written to detect activity associated with the DCSync attack. When a domain controller receives a replication request, the user account permissions are validated, however no checks are performed to validate the request was initiated by a Domain Controller. Once an attacker gains control of an account with the necessary privileges, they can request password hashes for any or all users within the domain. This alert detects when a user account creates a handle to domainDNS with the necessary replication permissions.
action.notable.param.rule_title = Windows AD Replication Request Initiated by User Account
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}", "domainDNS") AND Properties IN ("*Replicating Directory Changes All*", "*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*", "*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") AND AccessMask="0x100" AND NOT (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") | stats min(_time) as _time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status | rename SubjectDomainName as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId, _time as attack_time | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | table attack_time, AuthenticationPackageName, LogonProcessName, LogonType, TargetUserSid, Target_Domain, user, Computer, TargetLogonId, status, src_ip, src_category, ObjectName, ObjectServer, ObjectType, OperationType | stats min(attack_time) as _time values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip by TargetLogonId | `windows_ad_replication_request_initiated_by_user_account_filter`

[ESCU - Windows AD Replication Request Initiated from Unsanctioned Location - Rule]
action.escu = 0
action.escu.enabled = 1
description = This alert was written to detect activity associated with the DCSync attack performed by computer accounts. When a domain controller receives a replication request, the account permissions are validated, however no checks are performed to validate the request was initiated by a Domain Controller. Once an attacker gains control of an account with the necessary privileges, they can request password hashes for any or all users within the domain. This alert detects when a computer account account creates a handle to domainDNS with the necessary replication permissions. These requests are then filtered to exclude where the events originate from a known domain controller IP address.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.006", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication", "Change"]
action.escu.eli5 = This alert was written to detect activity associated with the DCSync attack performed by computer accounts. When a domain controller receives a replication request, the account permissions are validated, however no checks are performed to validate the request was initiated by a Domain Controller. Once an attacker gains control of an account with the necessary privileges, they can request password hashes for any or all users within the domain. This alert detects when a computer account account creates a handle to domainDNS with the necessary replication permissions. These requests are then filtered to exclude where the events originate from a known domain controller IP address.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services Access` within `DS Access` needs to be enabled, as well as the following SACLs applied to the domain root and all descendant objects. The principals `everybody`,  `Domain Computers`, and  `Domain Controllers` auditing the permissions `Replicating Directory Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes In Filtered Set` Assets and Identities will also need to be configured, with the category of domain_controller added for domain controllers.
action.escu.known_false_positives = Genuine DC promotion may trigger this alert.
action.escu.creation_date = 2022-11-17
action.escu.modification_date = 2022-11-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Replication Request Initiated from Unsanctioned Location - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks", "Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = Windows Active Directory Replication Request Initiated from Unsanctioned Location $src_ip$ by $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Replication Request Initiated from Unsanctioned Location - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks", "Credential Dumping"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1003.006", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This alert was written to detect activity associated with the DCSync attack performed by computer accounts. When a domain controller receives a replication request, the account permissions are validated, however no checks are performed to validate the request was initiated by a Domain Controller. Once an attacker gains control of an account with the necessary privileges, they can request password hashes for any or all users within the domain. This alert detects when a computer account account creates a handle to domainDNS with the necessary replication permissions. These requests are then filtered to exclude where the events originate from a known domain controller IP address.
action.notable.param.rule_title = Windows AD Replication Request Initiated from Unsanctioned Location
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}", "domainDNS") AND Properties IN ("*Replicating Directory Changes All*", "*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*", "*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") AND AccessMask="0x100" AND (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") | stats min(_time) as attack_time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status | rename SubjectDomainName as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | table attack_time, AuthenticationPackageName, LogonProcessName, LogonType, TargetUserSid, Target_Domain, user, Computer, TargetLogonId, status, src_ip, src_category, ObjectName, ObjectServer, ObjectType, OperationType | stats min(attack_time) as _time, values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip by TargetLogonId | search NOT src_category="domain_controller" | `windows_ad_replication_request_initiated_from_unsanctioned_location_filter`

[ESCU - Windows AD Same Domain SID History Addition - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic looks for changes to the sIDHistory AD attribute of user or computer objects which exist within the same domain. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries to stealthily grant access to a backdoor account within the same domain. This analytic was written to pick up on activity via Mimikatz sid::patch. Please note there are additional avenues to abuse SID history such as DCShadow & Golden / Diamond tickets which won't be detected using these event codes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134.005", "T1134"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic looks for changes to the sIDHistory AD attribute of user or computer objects which exist within the same domain. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries to stealthily grant access to a backdoor account within the same domain. This analytic was written to pick up on activity via Mimikatz sid::patch. Please note there are additional avenues to abuse SID history such as DCShadow & Golden / Diamond tickets which won't be detected using these event codes.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit User Account Management` and  `Audit Computer Account Management` within `Account Management` all need to be enabled. SID resolution is not required..
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2022-09-09
action.escu.modification_date = 2022-09-09
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Same Domain SID History Addition - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = Active Directory SID History Attribute was added to $user$ by $src_user$
action.risk.param._risk = [{"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Same Domain SID History Addition - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1134.005", "T1134"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic looks for changes to the sIDHistory AD attribute of user or computer objects which exist within the same domain. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries to stealthily grant access to a backdoor account within the same domain. This analytic was written to pick up on activity via Mimikatz sid::patch. Please note there are additional avenues to abuse SID history such as DCShadow & Golden / Diamond tickets which won't be detected using these event codes.
action.notable.param.rule_title = Windows AD Same Domain SID History Addition
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistoryMatch>.*)(\-|\\\)" | rex field=TargetSid "^(?P<TargetSidmatch>.*)(\-|\\\)" | where SidHistoryMatch=TargetSidmatch OR SidHistoryMatch=TargetDomainName | rename TargetSid as userSid, TargetDomainName as userDomainName | table _time action status host user userSid userDomainName SidHistory Logon_ID src_user | `windows_ad_same_domain_sid_history_addition_filter`

[ESCU - Windows AD ServicePrincipalName Added To Domain Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the addition of a Service Principal Name to a domain account. While this event may be part of a legitimate action part of certain administrative operations, it may also be evidence of a persistence attack. Domain accounts with Servce Principal Names are vulnerable to a technique called Kerberoasting that enables attackers to potentially obtain the cleartext password of the account by performing offline cracking. An adversary who has obtained privileged access to a domain environment may add an SPN to a privileged account to then leverage the Kerberoasting technique and attempt to obtain its clertext password.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the addition of a Service Principal Name to a domain account. While this event may be part of a legitimate action part of certain administrative operations, it may also be evidence of a persistence attack. Domain accounts with Servce Principal Names are vulnerable to a technique called Kerberoasting that enables attackers to potentially obtain the cleartext password of the account by performing offline cracking. An adversary who has obtained privileged access to a domain environment may add an SPN to a privileged account to then leverage the Kerberoasting technique and attempt to obtain its clertext password.
action.escu.how_to_implement = To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications.
action.escu.known_false_positives = A Service Principal Name should only be added to an account when an application requires it. While infrequent, this detection may trigger on legitimate actions. Filter as needed.
action.escu.creation_date = 2022-11-17
action.escu.modification_date = 2022-11-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD ServicePrincipalName Added To Domain Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/service_principal_name_added/windows-security.log"]
action.risk = 1
action.risk.param._risk_message = A Servince Principal Name for $ObjectDN$ was set by $SubjectUserName$
action.risk.param._risk = [{"risk_object_field": "SubjectUserName", "risk_object_type": "user", "risk_score": 30}, {"risk_object_field": "ObjectDN", "risk_object_type": "user", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD ServicePrincipalName Added To Domain Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/service_principal_name_added/windows-security.log"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the addition of a Service Principal Name to a domain account. While this event may be part of a legitimate action part of certain administrative operations, it may also be evidence of a persistence attack. Domain accounts with Servce Principal Names are vulnerable to a technique called Kerberoasting that enables attackers to potentially obtain the cleartext password of the account by performing offline cracking. An adversary who has obtained privileged access to a domain environment may add an SPN to a privileged account to then leverage the Kerberoasting technique and attempt to obtain its clertext password.
action.notable.param.rule_title = Windows AD ServicePrincipalName Added To Domain Account
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName OperationType="%%14674" | stats values(ObjectDN) by _time, Computer, SubjectUserName, AttributeValue | `windows_ad_serviceprincipalname_added_to_domain_account_filter`

[ESCU - Windows AD Short Lived Domain Account ServicePrincipalName - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the addition of a Service Principal Name to a domain account that is quickly deleted within 5 minutes or less. While this event may be part of a legitimate action part of certain administrative operations, it may also be evidence of a persistence attack. Domain accounts with Service Principal Names are vulnerable to a technique called Kerberoasting that enables attackers to potentially obtain the cleartext password of the account by performing offline cracking. An adversary who has obtained privileged access to a domain environment may add an SPN to a privileged account to then leverage the Kerberoasting technique and attempt to obtain its clertext password. To clean things up, the adversary may delete the SPN which will trigger this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the addition of a Service Principal Name to a domain account that is quickly deleted within 5 minutes or less. While this event may be part of a legitimate action part of certain administrative operations, it may also be evidence of a persistence attack. Domain accounts with Service Principal Names are vulnerable to a technique called Kerberoasting that enables attackers to potentially obtain the cleartext password of the account by performing offline cracking. An adversary who has obtained privileged access to a domain environment may add an SPN to a privileged account to then leverage the Kerberoasting technique and attempt to obtain its clertext password. To clean things up, the adversary may delete the SPN which will trigger this detection.
action.escu.how_to_implement = To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications.
action.escu.known_false_positives = A Service Principal Name should only be added to an account when an application requires it. Adding an SPN and quickly deleting it is less common but may be part of legitimate action. Filter as needed.
action.escu.creation_date = 2022-11-18
action.escu.modification_date = 2022-11-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Short Lived Domain Account ServicePrincipalName - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A Servince Principal Name for $user$ was set and shortly deleted
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Short Lived Domain Account ServicePrincipalName - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the addition of a Service Principal Name to a domain account that is quickly deleted within 5 minutes or less. While this event may be part of a legitimate action part of certain administrative operations, it may also be evidence of a persistence attack. Domain accounts with Service Principal Names are vulnerable to a technique called Kerberoasting that enables attackers to potentially obtain the cleartext password of the account by performing offline cracking. An adversary who has obtained privileged access to a domain environment may add an SPN to a privileged account to then leverage the Kerberoasting technique and attempt to obtain its clertext password. To clean things up, the adversary may delete the SPN which will trigger this detection.
action.notable.param.rule_title = Windows AD Short Lived Domain Account ServicePrincipalName
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName | transaction ObjectDN AttributeValue startswith=(EventCode=5136 OperationType="%%14674") endswith=(EventCode=5136 OperationType="%%14675") | eval short_lived=case((duration<300),"TRUE") | search short_lived = TRUE | rename ObjectDN as user | `windows_ad_short_lived_domain_account_serviceprincipalname_filter`

[ESCU - Windows AD Short Lived Domain Controller SPN Attribute - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when either a global catalog SPN or a DRS RPC SPN are temporarily added to an Active Directory computer object, both of which can be evidence of a DCShadow attack. DCShadow allows an attacker who has obtained privileged access to register a rogue Domain Controller (DC). Once registered, the rogue DC may be able to inject and replicate changes into the AD infrastructure for any domain object, including credentials and keys. This technique was initially released in 2018 by security researchers Benjamin Delpy and Vincent Le Toux. No event logs are written for changes to AD attributes, allowing for stealthy backdoors to be implanted in the domain, or metadata such as timestamps overwritten to cover tracks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when either a global catalog SPN or a DRS RPC SPN are temporarily added to an Active Directory computer object, both of which can be evidence of a DCShadow attack. DCShadow allows an attacker who has obtained privileged access to register a rogue Domain Controller (DC). Once registered, the rogue DC may be able to inject and replicate changes into the AD infrastructure for any domain object, including credentials and keys. This technique was initially released in 2018 by security researchers Benjamin Delpy and Vincent Le Toux. No event logs are written for changes to AD attributes, allowing for stealthy backdoors to be implanted in the domain, or metadata such as timestamps overwritten to cover tracks.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` to `Write All Properties` applied to the domain root and all descendant objects.
action.escu.known_false_positives = None.
action.escu.creation_date = 2022-09-02
action.escu.modification_date = 2022-09-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Short Lived Domain Controller SPN Attribute - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = Short Lived Domain Controller SPN AD Attribute Triggered by $user$ from $src_ip$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Short Lived Domain Controller SPN Attribute - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when either a global catalog SPN or a DRS RPC SPN are temporarily added to an Active Directory computer object, both of which can be evidence of a DCShadow attack. DCShadow allows an attacker who has obtained privileged access to register a rogue Domain Controller (DC). Once registered, the rogue DC may be able to inject and replicate changes into the AD infrastructure for any domain object, including credentials and keys. This technique was initially released in 2018 by security researchers Benjamin Delpy and Vincent Le Toux. No event logs are written for changes to AD attributes, allowing for stealthy backdoors to be implanted in the domain, or metadata such as timestamps overwritten to cover tracks.
action.notable.param.rule_title = Windows AD Short Lived Domain Controller SPN Attribute
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName (AttributeValue="GC/*" OR AttributeValue="E3514235-4B06-11D1-AB04-00C04FC2DCD2/*") | stats min(_time) as _time range(_time) as duration values(OperationType) as OperationType values(src_nt_domain) as src_nt_domain values(src_user) as src_user values(Computer) as Computer, values(ObjectDN) as ObjectDN by Logon_ID | eval short_lived=case((duration<30),"TRUE") | where short_lived="TRUE" AND mvcount(OperationType)>1 | replace "%%14674" with "Value Added", "%%14675" with "Value Deleted" in OperationType | rename Logon_ID as TargetLogonId | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | stats min(_time) as _time, values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip values(ObjectDN) as ObjectDN values(OperationType) as OperationType by TargetLogonId | `windows_ad_short_lived_domain_controller_spn_attribute_filter`

[ESCU - Windows AD Short Lived Server Object - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a change in an Active Directory environment that could represent evidence of the DCShadow attack. DCShadow allows an attacker who has obtained privileged access to register a rogue Domain Controller (DC). Once registered, the rogue DC may be able to inject and replicate changes in the AD infrastructure for any domain object, including credentials and keys. This technique was initially released in 2018 by security researchers Benjamin Delpy and Vincent Le Toux. Specifically, the detection will trigger when a possible rogue Domain Controller computer object is created and quickly deleted within 30 seconds or less in an Active Directory domain. This behavior was identfied by simulating the DCShadow attack with Mimikatz.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a change in an Active Directory environment that could represent evidence of the DCShadow attack. DCShadow allows an attacker who has obtained privileged access to register a rogue Domain Controller (DC). Once registered, the rogue DC may be able to inject and replicate changes in the AD infrastructure for any domain object, including credentials and keys. This technique was initially released in 2018 by security researchers Benjamin Delpy and Vincent Le Toux. Specifically, the detection will trigger when a possible rogue Domain Controller computer object is created and quickly deleted within 30 seconds or less in an Active Directory domain. This behavior was identfied by simulating the DCShadow attack with Mimikatz.
action.escu.how_to_implement = To successfully implement this search, you ned to be ingesting Event codes `5137` and `5141`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. For these event codes to be generated, specific SACLs are required.
action.escu.known_false_positives = Creating and deleting a server object within 30 seconds or less is unusual but not impossible in a production environment. Filter as needed.
action.escu.creation_date = 2022-10-17
action.escu.modification_date = 2022-10-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Short Lived Server Object - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = Potential DCShadow Attack Detected on $Computer$
action.risk.param._risk = [{"risk_object_field": "SubjectUserName", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Short Lived Server Object - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a change in an Active Directory environment that could represent evidence of the DCShadow attack. DCShadow allows an attacker who has obtained privileged access to register a rogue Domain Controller (DC). Once registered, the rogue DC may be able to inject and replicate changes in the AD infrastructure for any domain object, including credentials and keys. This technique was initially released in 2018 by security researchers Benjamin Delpy and Vincent Le Toux. Specifically, the detection will trigger when a possible rogue Domain Controller computer object is created and quickly deleted within 30 seconds or less in an Active Directory domain. This behavior was identfied by simulating the DCShadow attack with Mimikatz.
action.notable.param.rule_title = Windows AD Short Lived Server Object
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5137 OR EventCode=5141 ObjectDN="*CN=Servers,CN=Default-First-Site-Name,CN=Sites,CN=Configuration*" | transaction ObjectDN startswith=(EventCode=5137) endswith=(EventCode=5141) | eval short_lived=case((duration<30),"TRUE") | search short_lived = TRUE | stats values(ObjectDN) values(signature) values(EventCode) by _time, Computer, SubjectUserName | `windows_ad_short_lived_server_object_filter`

[ESCU - Windows AD SID History Attribute Modified - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages event code `5136` to identify a modification of the SID History AD attribute. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries to stealthily grant access to a backdoor account within the same domain.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1134", "T1134.005"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages event code `5136` to identify a modification of the SID History AD attribute. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries to stealthily grant access to a backdoor account within the same domain.
action.escu.how_to_implement = To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications.
action.escu.known_false_positives = Domain mergers and migrations may generate large volumes of false positives for this analytic.
action.escu.creation_date = 2022-11-16
action.escu.modification_date = 2022-11-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD SID History Attribute Modified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = SID History AD attribute modified by $SubjectUserName$ for $ObjectDN$
action.risk.param._risk = [{"risk_object_field": "SubjectUserName", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD SID History Attribute Modified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1134", "T1134.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages event code `5136` to identify a modification of the SID History AD attribute. The SID history AD attribute allows users to inherit permissions from a separate AD account without group changes. Initially developed for access continuity when migrating user accounts to different domains, this attribute can also be abused by adversaries to stealthily grant access to a backdoor account within the same domain.
action.notable.param.rule_title = Windows AD SID History Attribute Modified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=sIDHistory OperationType="%%14674" | stats values(ObjectDN) by _time, Computer, SubjectUserName, AttributeValue | `windows_ad_sid_history_attribute_modified_filter`

[ESCU - Windows AdFind Exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. AdFind.exe is a powerful tool that is commonly used for querying and retrieving information from Active Directory (AD). While it is primarily designed for AD administration and management, it has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. AdFind.exe is a powerful tool that is commonly used for querying and retrieving information from Active Directory (AD). While it is primarily designed for AD administration and management, it has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = ADfind is a command-line tool for AD administration and management that is seen to be leveraged by various adversaries. Filter out legitimate administrator usage using the filter macro.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AdFind Exe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Domain Trust Discovery", "IcedID", "NOBELIUM Group", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = Windows AdFind Exe
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AdFind Exe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Domain Trust Discovery", "IcedID", "NOBELIUM Group", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. AdFind.exe is a powerful tool that is commonly used for querying and retrieving information from Active Directory (AD). While it is primarily designed for AD administration and management, it has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST.
action.notable.param.rule_title = Windows AdFind Exe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* -f *" OR Processes.process="* -b *") AND (Processes.process=*objectcategory* OR Processes.process="* -gcb *" OR Processes.process="* -sc *") by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter`

[ESCU - Windows Admin Permission Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to identify suspicious file creation in the root drive (C:\). This tactic was observed in NjRAT as a means to ascertain whether its malware instance running on the compromised host possesses administrative privileges. The methodology involves an attempt to create a 'win.dat' file in the C:\ directory. If this file is successfully created, it serves as an indicator that the process indeed holds administrative privileges. This anomaly detection mechanism serves as a valuable pivot point for detecting NjRAT and other malware strains employing similar techniques to assess the privileges of their running malware instances, without using token privilege API calls or PowerShell commandlets.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to identify suspicious file creation in the root drive (C:\). This tactic was observed in NjRAT as a means to ascertain whether its malware instance running on the compromised host possesses administrative privileges. The methodology involves an attempt to create a 'win.dat' file in the C:\ directory. If this file is successfully created, it serves as an indicator that the process indeed holds administrative privileges. This anomaly detection mechanism serves as a valuable pivot point for detecting NjRAT and other malware strains employing similar techniques to assess the privileges of their running malware instances, without using token privilege API calls or PowerShell commandlets.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = False positives may occur if there are legitimate accounts with the privilege to drop files in the root of the C drive. It's recommended to verify the legitimacy of such actions and the accounts involved.
action.escu.creation_date = 2023-09-19
action.escu.modification_date = 2023-09-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Admin Permission Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = A file was created in root drive C:/ on host - $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Admin Permission Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe", "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif", "*.lnk", "*.dat") by Filesystem.dest Filesystem.file_create_time Filesystem.process_id Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.user | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | where LIKE(root_drive, "C:") AND dropped_file_path_split_count = 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admin_permission_discovery_filter`

[ESCU - Windows Administrative Shares Accessed On Multiple Hosts - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event IDs 5140 or 5145 to identify a source computer accessing windows administrative shares (C$, Admin$ and IPC$ ) across a large number remote endpoints. Specifically, the logic will trigger when a source endpoint accesses administrative shares across 30 or more target computers within a 5 minute timespan. This behavior could represent an adversary who is enumerating network shares across an Active Directory environment in the search for sensitive files, a common technique leveraged by red teamers and threat actors. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1135"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event IDs 5140 or 5145 to identify a source computer accessing windows administrative shares (C$, Admin$ and IPC$ ) across a large number remote endpoints. Specifically, the logic will trigger when a source endpoint accesses administrative shares across 30 or more target computers within a 5 minute timespan. This behavior could represent an adversary who is enumerating network shares across an Active Directory environment in the search for sensitive files, a common technique leveraged by red teamers and threat actors. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting file share events. The Advanced Security Audit policy setting `Audit Detailed File Share` or `Audit File Share` within `Object Access` need to be enabled.
action.escu.known_false_positives = An single endpoint accessing windows administrative shares across a large number of endpoints is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems.
action.escu.creation_date = 2023-03-23
action.escu.modification_date = 2023-03-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Administrative Shares Accessed On Multiple Hosts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = $IpAddress$ accessed the IPC share on more than 30 endpoints in a timespan of 5 minutes.
action.risk.param._risk = [{"risk_object_field": "host_targets", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Administrative Shares Accessed On Multiple Hosts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1135"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event IDs 5140 or 5145 to identify a source computer accessing windows administrative shares (C$, Admin$ and IPC$ ) across a large number remote endpoints. Specifically, the logic will trigger when a source endpoint accesses administrative shares across 30 or more target computers within a 5 minute timespan. This behavior could represent an adversary who is enumerating network shares across an Active Directory environment in the search for sensitive files, a common technique leveraged by red teamers and threat actors. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.notable.param.rule_title = Windows Administrative Shares Accessed On Multiple Hosts
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5140 OR EventCode=5145 (ShareName="\\\\*\\ADMIN$" OR ShareName="\\\\*\\IPC$" OR ShareName="\\\\*\\C$") | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets values(ShareName) as shares by _time, IpAddress, SubjectUserName, EventCode | where unique_targets > 30 | `windows_administrative_shares_accessed_on_multiple_hosts_filter`

[ESCU - Windows Admon Default Group Policy Object Modified - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Splunks Admon to identify the modification of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the modification of the default GPOs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Splunks Admon to identify the modification of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the modification of the default GPOs.
action.escu.how_to_implement = To successfully implement this search, you need to be monitoring Active Directory logs using Admon. Details can be found here https://docs.splunk.com/Documentation/SplunkCloud/8.1.2101/Data/MonitorActiveDirectory
action.escu.known_false_positives = The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed.
action.escu.creation_date = 2023-03-29
action.escu.modification_date = 2023-03-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Admon Default Group Policy Object Modified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A default domain group policy was updated on $dcName$
action.risk.param._risk = [{"risk_object_field": "dcName", "risk_object_type": "system", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Admon Default Group Policy Object Modified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 100, "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Splunks Admon to identify the modification of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the modification of the default GPOs.
action.notable.param.rule_title = Windows Admon Default Group Policy Object Modified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" (displayName="Default Domain Policy" OR displayName="Default Domain Controllers Policy") | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) by dcName, displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admon_default_group_policy_object_modified_filter`

[ESCU - Windows Admon Group Policy Object Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Splunks Admon to identify the creation of a new Group Policy Object. With GPOs, system administrators can manage and configure applications, software operations, and user settings throughout an entire organization. GPOs can be abused and leveraged by adversaries to escalate privileges or deploy malware across an Active Directory network. As an example, the Lockbit ransomware malware will create new group policies on the domain controller that are then pushed out to every device on the network. Security teams should monitor the creation of new Group Policy Objects.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Splunks Admon to identify the creation of a new Group Policy Object. With GPOs, system administrators can manage and configure applications, software operations, and user settings throughout an entire organization. GPOs can be abused and leveraged by adversaries to escalate privileges or deploy malware across an Active Directory network. As an example, the Lockbit ransomware malware will create new group policies on the domain controller that are then pushed out to every device on the network. Security teams should monitor the creation of new Group Policy Objects.
action.escu.how_to_implement = To successfully implement this search, you need to be monitoring Active Directory logs using Admon. Details can be found here https://docs.splunk.com/Documentation/SplunkCloud/8.1.2101/Data/MonitorActiveDirectory
action.escu.known_false_positives = Group Policy Objects are created as part of regular administrative operations, filter as needed.
action.escu.creation_date = 2023-04-06
action.escu.modification_date = 2023-04-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Admon Group Policy Object Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A new group policy objected was created on $dcName$
action.risk.param._risk = [{"risk_object_field": "dcName", "risk_object_type": "system", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Admon Group Policy Object Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 100, "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Splunks Admon to identify the creation of a new Group Policy Object. With GPOs, system administrators can manage and configure applications, software operations, and user settings throughout an entire organization. GPOs can be abused and leveraged by adversaries to escalate privileges or deploy malware across an Active Directory network. As an example, the Lockbit ransomware malware will create new group policies on the domain controller that are then pushed out to every device on the network. Security teams should monitor the creation of new Group Policy Objects.
action.notable.param.rule_title = Windows Admon Group Policy Object Created
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" versionNumber=0 displayName!="New Group Policy Object" | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) by dcName, displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admon_group_policy_object_created_filter`

[ESCU - Windows Apache Benchmark Binary - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a default behavior of a MetaSploit payload. MetaSploit uses Apache Benchmark to generate payloads. The payloads contain standard artifacts including "Apache Benchmark" and the original file name is always ab.exe. During triage, review the process and it's path. It is possible network connections spawned from it. Review parallel processes for further behaviors.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a default behavior of a MetaSploit payload. MetaSploit uses Apache Benchmark to generate payloads. The payloads contain standard artifacts including "Apache Benchmark" and the original file name is always ab.exe. During triage, review the process and it's path. It is possible network connections spawned from it. Review parallel processes for further behaviors.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as there is a small subset of binaries that contain the original file name of ab.exe. Filter as needed.
action.escu.creation_date = 2022-11-21
action.escu.modification_date = 2022-11-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Apache Benchmark Binary - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["MetaSploit"]
action.risk = 1
action.risk.param._risk_message = A known MetaSploit default payload has been identified on $dest$ ran by $user$, $parent_process_name$ spawning $process_name$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 100}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Apache Benchmark Binary - Rule
action.correlationsearch.annotations = {"analytic_story": ["MetaSploit"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=ab.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_apache_benchmark_binary_filter`

[ESCU - Windows App Layer Protocol Qakbot NamedPipe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious process creating or connecting to a possible Qakbot named pipe . This technique was seen in Qakbot malware that creates named pipe after injecting its code in legitimate process to communicate on other process that also has an injected code to steal information on the compromised host. This Anomaly detection can be a good pivot for possible Qakbot infection. This detection looks for possible random generated named pipe (in GUID form) created by known process being abused by Qakbot.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1071"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious process creating or connecting to a possible Qakbot named pipe . This technique was seen in Qakbot malware that creates named pipe after injecting its code in legitimate process to communicate on other process that also has an injected code to steal information on the compromised host. This Anomaly detection can be a good pivot for possible Qakbot infection. This detection looks for possible random generated named pipe (in GUID form) created by known process being abused by Qakbot.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, pipename, processguid and named pipe event type from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-10
action.escu.modification_date = 2022-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows App Layer Protocol Qakbot NamedPipe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = $Image$ is creating or connecting to a named pipe $PipeName$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows App Layer Protocol Qakbot NamedPipe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe") Image IN ("*\\calc.exe", "*\\notepad.exe", "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe") | regex PipeName="^\\\{[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{8}" | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessGuid ProcessId PipeName SecurityID EventCode dest UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_app_layer_protocol_qakbot_namedpipe_filter`

[ESCU - Windows App Layer Protocol Wermgr Connect To NamedPipe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious wermgr.exe process creating or connecting to a named pipe. Wermgr.exe is part of Windows OS Problem reporting application responsible for reporting problems, fault or error happen on the Windows OS. This file is being abused by several Threat actors and malware such as Trickbot and Qakbot to execute their malicious code. This anomaly detection can be a good pivot on possible wermgr.exe processes having injected malicious code that might be related to qakbot infection that communicates via named pipe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1071"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious wermgr.exe process creating or connecting to a named pipe. Wermgr.exe is part of Windows OS Problem reporting application responsible for reporting problems, fault or error happen on the Windows OS. This file is being abused by several Threat actors and malware such as Trickbot and Qakbot to execute their malicious code. This anomaly detection can be a good pivot on possible wermgr.exe processes having injected malicious code that might be related to qakbot infection that communicates via named pipe.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, pipename, processguid and named pipe event type from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-28
action.escu.modification_date = 2022-10-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows App Layer Protocol Wermgr Connect To NamedPipe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = wermgr.exe process is creating or connecting to a named pipe $PipeName$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows App Layer Protocol Wermgr Connect To NamedPipe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode IN (17, 18) Image= "*\\wermgr.exe" EventType IN ( "CreatePipe", "ConnectPipe") | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessGuid ProcessId PipeName SecurityID EventCode dest UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_app_layer_protocol_wermgr_connect_to_namedpipe_filter`

[ESCU - Windows Application Layer Protocol RMS Radmin Tool Namedpipe - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of default or publicly known named pipes used by RMX remote admin tool. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. RMX Tool uses named pipes in many way as part of its communication for its server and client component. This tool was abuse by several adversaries and malware like Azorult to collect data to the targeted host. This TTP is a good indicator that this tool was install in production premise and need to check if the user has a valid reason why it need to install this legitimate application.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1071"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the use of default or publicly known named pipes used by RMX remote admin tool. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. RMX Tool uses named pipes in many way as part of its communication for its server and client component. This tool was abuse by several adversaries and malware like Azorult to collect data to the targeted host. This TTP is a good indicator that this tool was install in production premise and need to check if the user has a valid reason why it need to install this legitimate application.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = False positives may be present. Filter based on pipe name or process.
action.escu.creation_date = 2022-06-24
action.escu.modification_date = 2022-06-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Application Layer Protocol RMS Radmin Tool Namedpipe - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = possible RMS admin tool named pipe was created in endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Application Layer Protocol RMS Radmin Tool Namedpipe - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1071"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of default or publicly known named pipes used by RMX remote admin tool. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. RMX Tool uses named pipes in many way as part of its communication for its server and client component. This tool was abuse by several adversaries and malware like Azorult to collect data to the targeted host. This TTP is a good indicator that this tool was install in production premise and need to check if the user has a valid reason why it need to install this legitimate application.
action.notable.param.rule_title = Windows Application Layer Protocol RMS Radmin Tool Namedpipe
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe") PipeName IN ("\\RManFUSServerNotify32", "\\RManFUSCallbackNotify32", "\\RMSPrint*") | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessId PipeName dest UserID | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_application_layer_protocol_rms_radmin_tool_namedpipe_filter`

[ESCU - Windows Autostart Execution LSASS Driver Registry Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the abuse of two undocumented registry keys that allow for a DLL to load into lsass.exe to potentially capture credentials. Upon successful modification of \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt or \CurrentControlSet\Services\NTDS\LsaDbExtPt, a DLL either remote or local will be set as the value and load up into lsass.exe. Based on POC code a text file may be written to disk with credentials.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.008"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the abuse of two undocumented registry keys that allow for a DLL to load into lsass.exe to potentially capture credentials. Upon successful modification of \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt or \CurrentControlSet\Services\NTDS\LsaDbExtPt, a DLL either remote or local will be set as the value and load up into lsass.exe. Based on POC code a text file may be written to disk with credentials.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be present on recent Windows Operating Systems. Filtering may be required based on process_name. In addition, look for non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by adding Endpoint.processes process_name to query to identify the process making the modification.
action.escu.creation_date = 2022-08-22
action.escu.modification_date = 2022-08-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Autostart Execution LSASS Driver Registry Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = The registry values for DirectoryServiceExtPt or LsaDbExtPt were modified on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Autostart Execution LSASS Driver Registry Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1547.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the abuse of two undocumented registry keys that allow for a DLL to load into lsass.exe to potentially capture credentials. Upon successful modification of \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt or \CurrentControlSet\Services\NTDS\LsaDbExtPt, a DLL either remote or local will be set as the value and load up into lsass.exe. Based on POC code a text file may be written to disk with credentials.
action.notable.param.rule_title = Windows Autostart Execution LSASS Driver Registry Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\CurrentControlSet\\Services\\NTDS\\DirectoryServiceExtPt","*\\CurrentControlSet\\Services\\NTDS\\LsaDbExtPt") by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_autostart_execution_lsass_driver_registry_modification_filter`

[ESCU - Windows Binary Proxy Execution Mavinject DLL Injection - Rule]
action.escu = 0
action.escu.enabled = 1
description = Adversaries may abuse mavinject.exe to inject malicious DLLs into running processes (i.e. Dynamic-link Library Injection), allowing for arbitrary code execution (ex. C:\Windows\system32\mavinject.exe PID /INJECTRUNNING PATH_DLL). In addition to Dynamic-link Library Injection, Mavinject.exe can also be abused to perform import descriptor injection via its /HMODULE command-line parameter (ex. mavinject.exe PID /HMODULE=BASE_ADDRESS PATH_DLL ORDINAL_NUMBER). This command would inject an import table entry consisting of the specified DLL into the module at the given base address. During triage, review file modifcations and parallel processes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.013", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Adversaries may abuse mavinject.exe to inject malicious DLLs into running processes (i.e. Dynamic-link Library Injection), allowing for arbitrary code execution (ex. C:\Windows\system32\mavinject.exe PID /INJECTRUNNING PATH_DLL). In addition to Dynamic-link Library Injection, Mavinject.exe can also be abused to perform import descriptor injection via its /HMODULE command-line parameter (ex. mavinject.exe PID /HMODULE=BASE_ADDRESS PATH_DLL ORDINAL_NUMBER). This command would inject an import table entry consisting of the specified DLL into the module at the given base address. During triage, review file modifcations and parallel processes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter on DLL name or parent process.
action.escu.creation_date = 2022-07-07
action.escu.modification_date = 2022-07-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Binary Proxy Execution Mavinject DLL Injection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting load a DLL.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Binary Proxy Execution Mavinject DLL Injection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1218.013", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Adversaries may abuse mavinject.exe to inject malicious DLLs into running processes (i.e. Dynamic-link Library Injection), allowing for arbitrary code execution (ex. C:\Windows\system32\mavinject.exe PID /INJECTRUNNING PATH_DLL). In addition to Dynamic-link Library Injection, Mavinject.exe can also be abused to perform import descriptor injection via its /HMODULE command-line parameter (ex. mavinject.exe PID /HMODULE=BASE_ADDRESS PATH_DLL ORDINAL_NUMBER). This command would inject an import table entry consisting of the specified DLL into the module at the given base address. During triage, review file modifcations and parallel processes.
action.notable.param.rule_title = Windows Binary Proxy Execution Mavinject DLL Injection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mavinject.exe Processes.process IN ("*injectrunning*", "*hmodule=0x*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_binary_proxy_execution_mavinject_dll_injection_filter`

[ESCU - Windows Boot or Logon Autostart Execution In Startup Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious files dropped or created in the Windows %startup% folder. This technique is a common way to gain persistence on a targeted host. Threat actor, adversaries and red teamer abuse this folder path to automatically execute their malicious sample upon boot or restart of the infected host. This TTP detection is a good indicator that a suspicious process wants to gain persistence on the targeted host. We suggest to verify the process name by using the process guid field, the file created and also the user and the computer name for further investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.001", "T1547"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify suspicious files dropped or created in the Windows %startup% folder. This technique is a common way to gain persistence on a targeted host. Threat actor, adversaries and red teamer abuse this folder path to automatically execute their malicious sample upon boot or restart of the infected host. This TTP detection is a good indicator that a suspicious process wants to gain persistence on the targeted host. We suggest to verify the process name by using the process guid field, the file created and also the user and the computer name for further investigation.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = Administrators may allow creation of script or exe in this path.
action.escu.creation_date = 2023-01-12
action.escu.modification_date = 2023-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Boot or Logon Autostart Execution In Startup Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Chaos Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = a process dropped a file in %startup% folder in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Boot or Logon Autostart Execution In Startup Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Chaos Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1547.001", "T1547"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\*" by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.user Filesystem.file_path Filesystem.process_guid Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_boot_or_logon_autostart_execution_in_startup_folder_filter`

[ESCU - Windows BootLoader Inventory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting query utilizes a PowerShell Scripted input that captures the bootloader paths for each Windows endpoint it is deployed to. The template inputs.conf is located in the references link. By default, it only captures the path, but may be modified to capture everything that BCDedit provides. It can be verbose, but may be worth it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1542.001", "T1542"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting query utilizes a PowerShell Scripted input that captures the bootloader paths for each Windows endpoint it is deployed to. The template inputs.conf is located in the references link. By default, it only captures the path, but may be modified to capture everything that BCDedit provides. It can be verbose, but may be worth it.
action.escu.how_to_implement = To implement this analytic, a new stanza will need to be added to a inputs.conf and deployed to all or some Windows endpoints. https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 provides the stanza. If modifying the sourcetype, be sure to update the Macro for this analytic. Recommend running it daily, or weekly, depending on threat model.
action.escu.known_false_positives = No false positives here, only bootloaders. Filter as needed or create a lookup as a baseline.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows BootLoader Inventory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["BlackLotus Campaign", "Windows BootKits"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows BootLoader Inventory - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackLotus Campaign", "Windows BootKits"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1542.001", "T1542"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `bootloader_inventory` | stats count min(_time) as firstTime max(_time) as lastTime values(_raw) by host | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_bootloader_inventory_filter`

[ESCU - Windows Bypass UAC via Pkgmgr Tool - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a potentially suspicious execution of the 'pkgmgr' process involving the use of an XML input file for package management. The 'pkgmgr' process, though deprecated in modern Windows systems, was historically used for managing packages. The presence of an XML input file raises concerns about the nature of the executed command and its potential impact on the system. Due to the deprecated status of 'pkgmgr' and the involvement of an XML file, this activity warrants careful investigation. XML files are commonly used for configuration and data exchange, making it crucial to ascertain the intentions and legitimacy of the command. To ensure system security, it is recommended to use up-to-date package management utilities, such as DISM or PowerShell's PackageManagement module, and exercise caution when executing commands involving potentially sensitive operations or files.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a potentially suspicious execution of the 'pkgmgr' process involving the use of an XML input file for package management. The 'pkgmgr' process, though deprecated in modern Windows systems, was historically used for managing packages. The presence of an XML input file raises concerns about the nature of the executed command and its potential impact on the system. Due to the deprecated status of 'pkgmgr' and the involvement of an XML file, this activity warrants careful investigation. XML files are commonly used for configuration and data exchange, making it crucial to ascertain the intentions and legitimacy of the command. To ensure system security, it is recommended to use up-to-date package management utilities, such as DISM or PowerShell's PackageManagement module, and exercise caution when executing commands involving potentially sensitive operations or files.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present on recent Windows Operating Systems. Filtering may be required based on process_name. In addition, look for non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by adding Endpoint.processes process_name to query to identify the process making the modification.
action.escu.creation_date = 2023-07-26
action.escu.modification_date = 2023-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Bypass UAC via Pkgmgr Tool - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = A pkgmgr.exe executed with package manager xml input file on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 9}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Bypass UAC via Pkgmgr Tool - Rule
action.correlationsearch.annotations = {"analytic_story": ["Warzone RAT"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1548.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process_name = pkgmgr.exe Processes.process = "*.xml*" NOT(Processes.parent_process_path IN("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "*:\\Program Files*")) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process_path Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_bypass_uac_via_pkgmgr_tool_filter`

[ESCU - Windows Cached Domain Credentials Reg Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line related to the discovery of cache domain credential logon count in the registry. This Technique was being abused by several post exploitation tool like Winpeas where it query CachedLogonsCount registry value in Winlogon registry. This value can be good information about the login caching setting on the Windows OS target host. A value of 0 means login caching is disable and values > 50 caches only 50 login attempts. By default all versions of Windows 10 save cached logins except Windows Server 2008.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.005", "T1003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line related to the discovery of cache domain credential logon count in the registry. This Technique was being abused by several post exploitation tool like Winpeas where it query CachedLogonsCount registry value in Winlogon registry. This value can be good information about the login caching setting on the Windows OS target host. A value of 0 means login caching is disable and values > 50 caches only 50 login attempts. By default all versions of Windows 10 save cached logins except Windows Server 2008.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Cached Domain Credentials Reg Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = a process with commandline $process$ tries to retrieve cache domain credential logon count in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Cached Domain Credentials Reg Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1003.005", "T1003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process = "* query *" AND Processes.process = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Processes.process = "*CACHEDLOGONSCOUNT*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cached_domain_credentials_reg_query_filter`

[ESCU - Windows Change Default File Association For No File Ext - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect suspicious process commandline to change or set the default file association of a file without file extension with notepad.exe. This technique was seen in some APT and ransomware Prestige where it set/modify the default process to run file association, like .txt to notepad.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.001", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect suspicious process commandline to change or set the default file association of a file without file extension with notepad.exe. This technique was seen in some APT and ransomware Prestige where it set/modify the default process to run file association, like .txt to notepad.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Change Default File Association For No File Ext - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = process with commandline $process$ set or change the file association of a file with no file extension in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Change Default File Association For No File Ext - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1546.001", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is developed to detect suspicious process commandline to change or set the default file association of a file without file extension with notepad.exe. This technique was seen in some APT and ransomware Prestige where it set/modify the default process to run file association, like .txt to notepad.exe.
action.notable.param.rule_title = Windows Change Default File Association For No File Ext
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_reg` AND Processes.process="* add *" AND Processes.process="* HKCR\\*" AND Processes.process="*\\shell\\open\\command*" AND  Processes.process= *Notepad.exe* by  Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | rex field=process "Notepad\.exe (?<file_name_association>.*$)" | rex field=file_name_association "\.(?<extension>[^\.]*$)" | where isnull(extension) and isnotnull(file_name_association) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_change_default_file_association_for_no_file_ext_filter`

[ESCU - Windows ClipBoard Data via Get-ClipBoard - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a powershell script command to retrieve clipboard data. This technique was seen in several post exploitation tools like WINPEAS to steal sensitive information that was saved in clipboard. Using the Get-Clipboard powershell commandlet, adversaries can be able collect data stored in clipboard that might be a copied user name, password or other sensitive information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1115"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a powershell script command to retrieve clipboard data. This technique was seen in several post exploitation tools like WINPEAS to steal sensitive information that was saved in clipboard. Using the Get-Clipboard powershell commandlet, adversaries can be able collect data stored in clipboard that might be a copied user name, password or other sensitive information.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible there will be false positives, filter as needed.
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows ClipBoard Data via Get-ClipBoard - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = Powershell script $ScriptBlockText$ execute Get-Clipboard commandlet on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows ClipBoard Data via Get-ClipBoard - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1115"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Get-Clipboard*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_clipboard_data_via_get_clipboard_filter`

[ESCU - Windows COM Hijacking InprocServer32 Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of reg.exe performing an add to the InProcServer32, which may be related to COM hijacking. Adversaries can use the COM system to insert malicious code that can be executed in place of legitimate software through hijacking the COM references and relationships as a means for persistence. Hijacking a COM object requires a change in the Registry to replace a reference to a legitimate system component which may cause that component to not work when executed. When that system component is executed through normal system operation the adversary's code will be executed instead.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.015", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of reg.exe performing an add to the InProcServer32, which may be related to COM hijacking. Adversaries can use the COM system to insert malicious code that can be executed in place of legitimate software through hijacking the COM references and relationships as a means for persistence. Hijacking a COM object requires a change in the Registry to replace a reference to a legitimate system component which may cause that component to not work when executed. When that system component is executed through normal system operation the adversary's code will be executed instead.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and some filtering may be required.
action.escu.creation_date = 2022-09-26
action.escu.modification_date = 2022-09-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows COM Hijacking InprocServer32 Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to modify InProcServer32 within the registry.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows COM Hijacking InprocServer32 Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1546.015", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of reg.exe performing an add to the InProcServer32, which may be related to COM hijacking. Adversaries can use the COM system to insert malicious code that can be executed in place of legitimate software through hijacking the COM references and relationships as a means for persistence. Hijacking a COM object requires a change in the Registry to replace a reference to a legitimate system component which may cause that component to not work when executed. When that system component is executed through normal system operation the adversary's code will be executed instead.
action.notable.param.rule_title = Windows COM Hijacking InprocServer32 Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` Processes.process=*inprocserver32* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_com_hijacking_inprocserver32_modification_filter`

[ESCU - Windows Command and Scripting Interpreter Hunting Path Traversal - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies path traversal command-line execution and should be used to tune and driver other more higher fidelity analytics. This technique was seen in malicious document that execute malicious code using msdt.exe and path traversal technique that serve as defense evasion. This Hunting query is a good pivot to look for possible suspicious process and command-line that runs execute path traversal technique to run malicious code. This may help you to find possible downloaded malware or other lolbin execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies path traversal command-line execution and should be used to tune and driver other more higher fidelity analytics. This technique was seen in malicious document that execute malicious code using msdt.exe and path traversal technique that serve as defense evasion. This Hunting query is a good pivot to look for possible suspicious process and command-line that runs execute path traversal technique to run malicious code. This may help you to find possible downloaded malware or other lolbin execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = false positive may vary depends on the score you want to check. The bigger number of path traversal string count the better.
action.escu.creation_date = 2022-06-01
action.escu.modification_date = 2022-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Command and Scripting Interpreter Hunting Path Traversal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Command and Scripting Interpreter Hunting Path Traversal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes  by Processes.original_file_name Processes.process_id Processes.parent_process_id Processes.process_hash Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval count_of_pattern1 = (mvcount(split(process,"/.."))-1) | eval count_of_pattern2 = (mvcount(split(process,"\.."))-1) | eval count_of_pattern3 = (mvcount(split(process,"\\.."))-1) | eval count_of_pattern4 = (mvcount(split(process,"//.."))-1) | search count_of_pattern1 > 1 OR count_of_pattern2 > 1 OR count_of_pattern3 > 1 OR count_of_pattern4 > 1 | `windows_command_and_scripting_interpreter_hunting_path_traversal_filter`

[ESCU - Windows Command and Scripting Interpreter Path Traversal Exec - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies path traversal command-line execution. This technique was seen in malicious document that execute malicious code using msdt.exe and path traversal technique that serve as defense evasion. This TTP is a good pivot to look for more suspicious process and command-line that runs before and after this execution. This may help you to find possible downloaded malware or other lolbin execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies path traversal command-line execution. This technique was seen in malicious document that execute malicious code using msdt.exe and path traversal technique that serve as defense evasion. This TTP is a good pivot to look for more suspicious process and command-line that runs before and after this execution. This may help you to find possible downloaded malware or other lolbin execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Not known at this moment.
action.escu.creation_date = 2022-06-01
action.escu.modification_date = 2022-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Command and Scripting Interpreter Path Traversal Exec - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"]
action.risk = 1
action.risk.param._risk_message = A parent process $parent_process_name$ has spawned a child $process_name$ with path traversal commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Command and Scripting Interpreter Path Traversal Exec - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies path traversal command-line execution. This technique was seen in malicious document that execute malicious code using msdt.exe and path traversal technique that serve as defense evasion. This TTP is a good pivot to look for more suspicious process and command-line that runs before and after this execution. This may help you to find possible downloaded malware or other lolbin execution.
action.notable.param.rule_title = Windows Command and Scripting Interpreter Path Traversal Exec
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where  Processes.process="*\/..\/..\/..\/*" OR Processes.process="*\\..\\..\\..\\*" OR Processes.process="*\/\/..\/\/..\/\/..\/\/*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_and_scripting_interpreter_path_traversal_exec_filter`

[ESCU - Windows Command Shell DCRat ForkBomb Payload - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies DCRat "forkbomb" payload feature. This technique was seen in dark crystal RAT backdoor capabilities where it will execute several cmd child process executing "notepad.exe & pause". The following analytic detects the multiple cmd.exe and child process notepad.exe  execution using batch script in the targeted host within 30s timeframe. this TTP can be a good pivot to check DCRat infection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.003", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies DCRat "forkbomb" payload feature. This technique was seen in dark crystal RAT backdoor capabilities where it will execute several cmd child process executing "notepad.exe & pause". The following analytic detects the multiple cmd.exe and child process notepad.exe  execution using batch script in the targeted host within 30s timeframe. this TTP can be a good pivot to check DCRat infection.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-07-28
action.escu.modification_date = 2022-07-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Command Shell DCRat ForkBomb Payload - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT"]
action.risk = 1
action.risk.param._risk_message = Multiple cmd.exe processes with child process of notepad.exe executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Command Shell DCRat ForkBomb Payload - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1059.003", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies DCRat "forkbomb" payload feature. This technique was seen in dark crystal RAT backdoor capabilities where it will execute several cmd child process executing "notepad.exe & pause". The following analytic detects the multiple cmd.exe and child process notepad.exe  execution using batch script in the targeted host within 30s timeframe. this TTP can be a good pivot to check DCRat infection.
action.notable.param.rule_title = Windows Command Shell DCRat ForkBomb Payload
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.parent_process_id) as parent_process_id values(Processes.process_id) as process_id dc(Processes.parent_process_id) as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name= "cmd.exe" (Processes.process_name = "notepad.exe" OR Processes.original_file_name= "notepad.exe") Processes.parent_process = "*.bat*" by  Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.parent_process Processes.dest Processes.user  _time span=30s | where parent_process_id_count>= 10 AND process_id_count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_shell_dcrat_forkbomb_payload_filter`

[ESCU - Windows Command Shell Fetch Env Variables - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious process command line fetching the environment variables with a non-shell parent process. This technique was seen in qakbot malware where it fetches the environment variable in the target or compromised host. This TTP detection is a good pivot of possible malicious behavior since the command line is executed by a common non-shell process like cmd.exe , powershell.exe and many more. This can also be a good sign that the parent process has a malicious code injected to it to execute this command.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious process command line fetching the environment variables with a non-shell parent process. This technique was seen in qakbot malware where it fetches the environment variable in the target or compromised host. This TTP detection is a good pivot of possible malicious behavior since the command line is executed by a common non-shell process like cmd.exe , powershell.exe and many more. This can also be a good sign that the parent process has a malicious code injected to it to execute this command.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = shell process that are not included in this search may cause False positive. Filter is needed.
action.escu.creation_date = 2022-10-27
action.escu.modification_date = 2022-10-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Command Shell Fetch Env Variables - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = non-shell parent process has a child process $process_name$ with a commandline $process$ to fetch env variables in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Command Shell Fetch Env Variables - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious process command line fetching the environment variables with a non-shell parent process. This technique was seen in qakbot malware where it fetches the environment variable in the target or compromised host. This TTP detection is a good pivot of possible malicious behavior since the command line is executed by a common non-shell process like cmd.exe , powershell.exe and many more. This can also be a good sign that the parent process has a malicious code injected to it to execute this command.
action.notable.param.rule_title = Windows Command Shell Fetch Env Variables
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*cmd /c set" OR Processes.process = "*cmd.exe /c set" AND NOT (Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell*" OR Processes.parent_process_name="pwsh.exe" OR Processes.parent_process_name = "explorer.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_shell_fetch_env_variables_filter`

[ESCU - Windows Common Abused Cmd Shell Risk Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following correlation identifies instances where four or more distinct detection analytics are associated with malicious command line behavior that is known to be exploited by multiple threat actors, adversaries, or red teamers on a specific host. By leveraging the Command Line Interface (CLI), attackers can execute malicious commands, gain access to sensitive data, install backdoors, and engage in various nefarious activities. The impact of such compromise can be severe, as attackers may gain unauthorized control over the compromised system, enabling them to exfiltrate valuable information, escalate privileges, or launch further attacks within the network. If this detection is triggered, there is a high level of confidence in the occurrence of suspicious command line activities on the host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222", "T1049", "T1033", "T1529", "T1016", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following correlation identifies instances where four or more distinct detection analytics are associated with malicious command line behavior that is known to be exploited by multiple threat actors, adversaries, or red teamers on a specific host. By leveraging the Command Line Interface (CLI), attackers can execute malicious commands, gain access to sensitive data, install backdoors, and engage in various nefarious activities. The impact of such compromise can be severe, as attackers may gain unauthorized control over the compromised system, enabling them to exfiltrate valuable information, escalate privileges, or launch further attacks within the network. If this detection is triggered, there is a high level of confidence in the occurrence of suspicious command line activities on the host.
action.escu.how_to_implement = Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
action.escu.known_false_positives = False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
action.escu.creation_date = 2023-06-14
action.escu.modification_date = 2023-06-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Common Abused Cmd Shell Risk Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Netsh Abuse", "Disabling Security Tools", "Windows Post-Exploitation", "Qakbot", "Azorult", "Sandworm Tools", "Volt Typhoon", "FIN7", "DarkCrystal RAT", "Windows Defense Evasion Tactics"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Windows Common Abused Cmd Shell Risk Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Netsh Abuse", "Disabling Security Tools", "Windows Post-Exploitation", "Qakbot", "Azorult", "Sandworm Tools", "Volt Typhoon", "FIN7", "DarkCrystal RAT", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1222", "T1049", "T1033", "T1529", "T1016", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following correlation identifies instances where four or more distinct detection analytics are associated with malicious command line behavior that is known to be exploited by multiple threat actors, adversaries, or red teamers on a specific host. By leveraging the Command Line Interface (CLI), attackers can execute malicious commands, gain access to sensitive data, install backdoors, and engage in various nefarious activities. The impact of such compromise can be severe, as attackers may gain unauthorized control over the compromised system, enabling them to exfiltrate valuable information, escalate privileges, or launch further attacks within the network. If this detection is triggered, there is a high level of confidence in the occurrence of suspicious command line activities on the host.
action.notable.param.rule_title = RBA: Windows Common Abused Cmd Shell Risk Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*Cmdline Tool Not Executed In CMD Shell*", "*Windows System Network Config Discovery Display DNS*", "*Local Account Discovery With Wmic*", "*Net Localgroup Discovery*", "*Create local admin accounts using net exe*", "*Local Account Discovery with Net*", "*Icacls Deny Command*", "*ICACLS Grant Command*", "*Windows Proxy Via Netsh*", "*Processes launching netsh*", "*Disabling Firewall with Netsh*", "*Windows System Network Connections Discovery Netsh*", "*Network Connection Discovery With Arp*", "*Windows System Discovery Using ldap Nslookup*", "*Windows System Shutdown CommandLine*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `windows_common_abused_cmd_shell_risk_behavior_filter`

[ESCU - Windows Computer Account Created by Computer Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifes a Computer Account creating a new Computer Account with specific a Service Principle Name - "RestrictedKrbHost". The RestrictedKrbHost service class allows client applications to use Kerberos authentication when they do not have the identity of the service but have the server name.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifes a Computer Account creating a new Computer Account with specific a Service Principle Name - "RestrictedKrbHost". The RestrictedKrbHost service class allows client applications to use Kerberos authentication when they do not have the identity of the service but have the server name.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = It is possible third party applications may have a computer account that adds computer accounts, filtering may be required.
action.escu.creation_date = 2022-04-27
action.escu.modification_date = 2022-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Computer Account Created by Computer Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"]
action.risk = 1
action.risk.param._risk_message = A Computer Account created a Computer Account on $dest$, possibly indicative of Kerberos relay attack.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Computer Account Created by Computer Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"], "cis20": ["CIS 10"], "confidence": 60, "impact": 50, "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifes a Computer Account creating a new Computer Account with specific a Service Principle Name - "RestrictedKrbHost". The RestrictedKrbHost service class allows client applications to use Kerberos authentication when they do not have the identity of the service but have the server name.
action.notable.param.rule_title = Windows Computer Account Created by Computer Account
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4741 user_type=computer Subject_Account_Domain!="NT AUTHORITY"  Message=*RestrictedKrbHost* | stats  count min(_time) as firstTime max(_time) as lastTime by dest, subject, action ,src_user, user, Account_Name, Subject_Account_Name,Subject_Account_Domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_computer_account_created_by_computer_account_filter`

[ESCU - Windows Computer Account Requesting Kerberos Ticket - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a ComputerAccount requesting a Kerberos Ticket. typically, a user account requests a Kerberos ticket. This behavior was identified with KrbUpRelay, but additional Kerberos attacks have exhibited similar behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a ComputerAccount requesting a Kerberos Ticket. typically, a user account requests a Kerberos ticket. This behavior was identified with KrbUpRelay, but additional Kerberos attacks have exhibited similar behavior.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4768 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = It is possible false positives will be present based on third party applications. Filtering may be needed.
action.escu.creation_date = 2022-04-27
action.escu.modification_date = 2022-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Computer Account Requesting Kerberos Ticket - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"]
action.risk = 1
action.risk.param._risk_message = A Computer Account requested a Kerberos ticket on $dest$, possibly indicative of Kerberos relay attack.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Computer Account Requesting Kerberos Ticket - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a ComputerAccount requesting a Kerberos Ticket. typically, a user account requests a Kerberos ticket. This behavior was identified with KrbUpRelay, but additional Kerberos attacks have exhibited similar behavior.
action.notable.param.rule_title = Windows Computer Account Requesting Kerberos Ticket
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security`  EventCode=4768 Account_Name="*$"  src_ip!="::1" | stats  count min(_time) as firstTime max(_time) as lastTime by dest, subject, action, Supplied_Realm_Name, user, Account_Name, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_computer_account_requesting_kerberos_ticket_filter`

[ESCU - Windows Computer Account With SPN - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies two SPNs, HOST and RestrictedKrbHost, added using the KrbRelayUp behavior. This particular behavior has been found in other Kerberos based attacks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic identifies two SPNs, HOST and RestrictedKrbHost, added using the KrbRelayUp behavior. This particular behavior has been found in other Kerberos based attacks.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = It is possible third party applications may add these SPNs to Computer Accounts, filtering may be needed.
action.escu.creation_date = 2022-04-28
action.escu.modification_date = 2022-04-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Computer Account With SPN - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"]
action.risk = 1
action.risk.param._risk_message = A Computer Account was created with SPNs related to Kerberos on $dest$, possibly indicative of Kerberos relay attack.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Computer Account With SPN - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies two SPNs, HOST and RestrictedKrbHost, added using the KrbRelayUp behavior. This particular behavior has been found in other Kerberos based attacks.
action.notable.param.rule_title = Windows Computer Account With SPN
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4741 MSADChangedAttributes IN ("*HOST/*","*RestrictedKrbHost/*") AND New_UAC_Value=0x80 | eval Effecting_Account=mvindex(Security_ID,1) | eval New_Computer_Account_Name=mvindex(Security_ID,0) | stats count min(_time) as firstTime max(_time) as lastTime values(EventCode),values(Account_Domain),values(Security_ID), values(Effecting_Account), values(New_Computer_Account_Name),values(SAM_Account_Name),values(DNS_Host_Name),values(MSADChangedAttributes) by dest Logon_ID subject | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_computer_account_with_spn_filter`

[ESCU - Windows Create Local Account - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a new local user account added to a computer. Note that, this should be restricted to critical assets.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = The following analytic identifies a new local user account added to a computer. Note that, this should be restricted to critical assets.
action.escu.how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/
action.escu.known_false_positives = It is possible that an administrator created the account. Verifying activity with an administrator is advised. This analytic is set to anomaly to allow for risk to be added. Filter and tune as needed. Restrict to critical infrastructure to reduce any volume.
action.escu.creation_date = 2022-10-05
action.escu.modification_date = 2022-10-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Create Local Account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.risk = 1
action.risk.param._risk_message = The following $user$ was added to $dest$ as a local account.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 18}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Create Local Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 10"], "confidence": 90, "impact": 20, "mitre_attack": ["T1136.001", "T1136"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 by All_Changes.user All_Changes.dest All_Changes.result All_Changes.action | `drop_dm_object_name("All_Changes")` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_create_local_account_filter`

[ESCU - Windows Credential Dumping LSASS Memory Createdump - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of CreateDump.exe being used to perform a process dump. This particular binary is not native to Windows, but is found to be brought in my many different third party applications including PowerShell 7.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of CreateDump.exe being used to perform a process dump. This particular binary is not native to Windows, but is found to be brought in my many different third party applications including PowerShell 7.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present if an application is dumping processes, filter as needed. Recommend reviewing createdump.exe usage across the fleet to better understand all usage and by what.
action.escu.creation_date = 2023-01-23
action.escu.modification_date = 2023-01-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Credential Dumping LSASS Memory Createdump - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to dump a process.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 70}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Credential Dumping LSASS Memory Createdump - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 70, "impact": 100, "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of CreateDump.exe being used to perform a process dump. This particular binary is not native to Windows, but is found to be brought in my many different third party applications including PowerShell 7.
action.notable.param.rule_title = Windows Credential Dumping LSASS Memory Createdump
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=createdump.exe OR Processes.original_file_name="FX_VER_INTERNALNAME_STR" Processes.process="*-u *" AND Processes.process="*-f *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_dumping_lsass_memory_createdump_filter`

[ESCU - Windows Credentials from Password Stores Chrome Extension Access - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic focuses on identifying non-chrome processes that attempt to access the Chrome extensions file. This file contains crucial settings and information related to the browser's extensions installed on the computer. Adversaries and malware authors have been known to exploit this file to extract sensitive information from the Chrome browser on targeted hosts. Detecting such anomalous behavior provides valuable insights for analyzing suspicious processes beyond the commonly observed chrome.exe and explorer.exe executables. By monitoring for access to the Chrome extensions file by non-chrome processes, we can enhance our ability to detect potential threats and protect sensitive information stored within the browser.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic focuses on identifying non-chrome processes that attempt to access the Chrome extensions file. This file contains crucial settings and information related to the browser's extensions installed on the computer. Adversaries and malware authors have been known to exploit this file to extract sensitive information from the Chrome browser on targeted hosts. Detecting such anomalous behavior provides valuable insights for analyzing suspicious processes beyond the commonly observed chrome.exe and explorer.exe executables. By monitoring for access to the Chrome extensions file by non-chrome processes, we can enhance our ability to detect potential threats and protect sensitive information stored within the browser.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = Uninstall chrome browser extension application may access this file and folder path to removed chrome installation in the target host. Filter is needed.
action.escu.creation_date = 2023-04-26
action.escu.modification_date = 2023-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Credentials from Password Stores Chrome Extension Access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["RedLine Stealer", "Amadey"]
action.risk = 1
action.risk.param._risk_message = A non-chrome process $process_name$ accessing chrome browser extension folder files on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Credentials from Password Stores Chrome Extension Access - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer", "Amadey"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Local Extension Settings\\*" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", "*\\chrome.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_extension_access_filter`

[ESCU - Windows Credentials from Password Stores Chrome LocalState Access - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to detect non-chrome processes accessing the Chrome user data file called "local state." This file contains important settings and information related to the browser's operations on the computer. Threat actors, adversaries, and malware authors have been known to exploit this file in attempts to extract the encrypted master key used for decrypting passwords saved in the Chrome browser. Detecting access to the "local state" file by non-chrome processes serves as a valuable pivot for analyzing suspicious processes beyond the commonly observed chrome.exe and explorer.exe executables. By monitoring for this anomaly, we can improve our ability to identify potential threats and safeguard sensitive information stored within the browser.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is designed to detect non-chrome processes accessing the Chrome user data file called "local state." This file contains important settings and information related to the browser's operations on the computer. Threat actors, adversaries, and malware authors have been known to exploit this file in attempts to extract the encrypted master key used for decrypting passwords saved in the Chrome browser. Detecting access to the "local state" file by non-chrome processes serves as a valuable pivot for analyzing suspicious processes beyond the commonly observed chrome.exe and explorer.exe executables. By monitoring for this anomaly, we can improve our ability to identify potential threats and safeguard sensitive information stored within the browser.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = Uninstall chrome application may access this file and folder path to removed chrome installation in target host. Filter is needed.
action.escu.creation_date = 2023-04-26
action.escu.modification_date = 2023-04-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Credentials from Password Stores Chrome LocalState Access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["RedLine Stealer", "Amadey", "Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = A non-chrome process $process_name$ accessing "Chrome\\User Data\\Local State" file on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Credentials from Password Stores Chrome LocalState Access - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer", "Amadey", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Local State" NOT (process_name IN ("*\\chrome.exe","*:\\Windows\\explorer.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_localstate_access_filter`

[ESCU - Windows Credentials from Password Stores Chrome Login Data Access - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to identify non-chrome processes accessing the Chrome user data file called "login data." This SQLite database file contains important information related to the browser's operations on the computer. Threat actors, adversaries, and malware authors have been known to exploit this file in attempts to extract and decrypt passwords saved in the Chrome browser. Detecting access to the "login data" file by non-chrome processes serves as a valuable pivot for analyzing suspicious processes beyond the commonly observed chrome.exe and explorer.exe executables. By monitoring for this anomaly, we can enhance our ability to detect potential threats and protect sensitive information stored within the browser.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is designed to identify non-chrome processes accessing the Chrome user data file called "login data." This SQLite database file contains important information related to the browser's operations on the computer. Threat actors, adversaries, and malware authors have been known to exploit this file in attempts to extract and decrypt passwords saved in the Chrome browser. Detecting access to the "login data" file by non-chrome processes serves as a valuable pivot for analyzing suspicious processes beyond the commonly observed chrome.exe and explorer.exe executables. By monitoring for this anomaly, we can enhance our ability to detect potential threats and protect sensitive information stored within the browser.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = Uninstall application may access this registry to remove the entry of the target application. filter is needed.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Credentials from Password Stores Chrome Login Data Access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["RedLine Stealer", "Amadey", "Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = A non-chrome process $process_name$ accessing Chrome "Login Data" file on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Credentials from Password Stores Chrome Login Data Access - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer", "Amadey", "Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Login Data" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", "*:\\Windows\\System32\\dllhost.exe", "*\\chrome.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_login_data_access_filter`

[ESCU - Windows Credentials from Password Stores Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of Windows OS cmdkey.exe tool. This tool is being abused or used by several post exploitation tool such as winpeas that being used by ransomware prestige to list stored user names, passwords or credentials in the targeted Windows OS host. This information can be used by the attacker to gain privilege escalation and persistence in the targeted hosts for further attacks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1555"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of Windows OS cmdkey.exe tool. This tool is being abused or used by several post exploitation tool such as winpeas that being used by ransomware prestige to list stored user names, passwords or credentials in the targeted Windows OS host. This information can be used by the attacker to gain privilege escalation and persistence in the targeted hosts for further attacks.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network administrator can use this tool for auditing process.
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Credentials from Password Stores Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = a process $process_name$ was executed in $dest$ to display stored username and credentials.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Credentials from Password Stores Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1555"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/list*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_query_filter`

[ESCU - Windows Credentials in Registry Reg Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line related to the discovery of possible password or credentials in the registry. This technique is being abused by adversaries or post exploitation tools like winpeas to steal credentials in the registry in the targeted host. Registry can contain several sensitive information like username and credentials that can be used for privilege escalation, persistence or even in lateral movement. This Anomaly detection can be a good pivot to detect a suspicious process querying a registry related to password or private keys.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.002", "T1552"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line related to the discovery of possible password or credentials in the registry. This technique is being abused by adversaries or post exploitation tools like winpeas to steal credentials in the registry in the targeted host. Registry can contain several sensitive information like username and credentials that can be used for privilege escalation, persistence or even in lateral movement. This Anomaly detection can be a good pivot to detect a suspicious process querying a registry related to password or private keys.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Credentials in Registry Reg Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = reg query commandline $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Credentials in Registry Reg Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1552.002", "T1552"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process IN ("*\\Software\\ORL\\WinVNC3\\Password*", "*\\SOFTWARE\\RealVNC\\WinVNC4 /v password*", "*\\CurrentControlSet\\Services\\SNMP*", "*\\Software\\TightVNC\\Server*", "*\\Software\\SimonTatham\\PuTTY\\Sessions*", "*\\Software\\OpenSSH\\Agent\\Keys*", "*password*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_in_registry_reg_query_filter`

[ESCU - Windows Curl Download to Suspicious Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Windows Curl.exe downloading a file to a suspicious location. \
-O or --output is used when a file is to be downloaded and placed in a specified location. \
During triage, review parallel processes for further behavior. In addition, identify if the download was successful. If a file was downloaded, capture and analyze.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Windows Curl.exe downloading a file to a suspicious location. \
-O or --output is used when a file is to be downloaded and placed in a specified location. \
During triage, review parallel processes for further behavior. In addition, identify if the download was successful. If a file was downloaded, capture and analyze.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible Administrators or super users will use Curl for legitimate purposes. Filter as needed.
action.escu.creation_date = 2021-10-19
action.escu.modification_date = 2021-10-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Curl Download to Suspicious Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Ingress Tool Transfer", "Forest Blizzard"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ to download a file to a suspicious directory.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Curl Download to Suspicious Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Ingress Tool Transfer", "Forest Blizzard"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of Windows Curl.exe downloading a file to a suspicious location. \
-O or --output is used when a file is to be downloaded and placed in a specified location. \
During triage, review parallel processes for further behavior. In addition, identify if the download was successful. If a file was downloaded, capture and analyze.
action.notable.param.rule_title = Windows Curl Download to Suspicious Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_curl` Processes.process IN ("*-O *","*--output*") Processes.process IN ("*\\appdata\\*","*\\programdata\\*","*\\public\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_curl_download_to_suspicious_path_filter`

[ESCU - Windows Curl Upload to Remote Destination - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Windows Curl.exe uploading a file to a remote destination. \
`-T` or `--upload-file` is used when a file is to be uploaded to a remotge destination. \
`-d` or `--data` POST is the HTTP method that was invented to send data to a receiving web application, and it is, for example, how most common HTML forms on the web work. \
HTTP multipart formposts are done with `-F`, but this appears to not be compatible with the Windows version of Curl. Will update if identified adversary tradecraft. \
Adversaries may use one of the three methods based on the remote destination and what they are attempting to upload (zip vs txt). During triage, review parallel processes for further behavior. In addition, identify if the upload was successful in network logs. If a file was uploaded, isolate the endpoint and review.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Windows Curl.exe uploading a file to a remote destination. \
`-T` or `--upload-file` is used when a file is to be uploaded to a remotge destination. \
`-d` or `--data` POST is the HTTP method that was invented to send data to a receiving web application, and it is, for example, how most common HTML forms on the web work. \
HTTP multipart formposts are done with `-F`, but this appears to not be compatible with the Windows version of Curl. Will update if identified adversary tradecraft. \
Adversaries may use one of the three methods based on the remote destination and what they are attempting to upload (zip vs txt). During triage, review parallel processes for further behavior. In addition, identify if the upload was successful in network logs. If a file was uploaded, isolate the endpoint and review.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be limited to source control applications and may be required to be filtered out.
action.escu.creation_date = 2021-11-10
action.escu.modification_date = 2021-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Curl Upload to Remote Destination - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ingress Tool Transfer"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ uploading a file to a remote destination.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Curl Upload to Remote Destination - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of Windows Curl.exe uploading a file to a remote destination. \
`-T` or `--upload-file` is used when a file is to be uploaded to a remotge destination. \
`-d` or `--data` POST is the HTTP method that was invented to send data to a receiving web application, and it is, for example, how most common HTML forms on the web work. \
HTTP multipart formposts are done with `-F`, but this appears to not be compatible with the Windows version of Curl. Will update if identified adversary tradecraft. \
Adversaries may use one of the three methods based on the remote destination and what they are attempting to upload (zip vs txt). During triage, review parallel processes for further behavior. In addition, identify if the upload was successful in network logs. If a file was uploaded, isolate the endpoint and review.
action.notable.param.rule_title = Windows Curl Upload to Remote Destination
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_curl` Processes.process IN ("*-T *","*--upload-file *", "*-d *", "*--data *", "*-F *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_curl_upload_to_remote_destination_filter`

[ESCU - Windows Data Destruction Recursive Exec Files Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious process recursively deleting files in a compromised host. This behavior was seen in several destructive malware, wiper like caddy wiper, doublezero, and now swiftslicer that delete or overwrite files with random generated strings to make it unrecoverable. This analytic can also catch a possible recursive file write on multiple files using Sysmon Event 23. Sysmon treats the modified file as a deleted one as soon as it was overwritten. This analytic can be a good indicator that a possible destructive malware is running in a host machine or a big software application is being uninstalled.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious process recursively deleting files in a compromised host. This behavior was seen in several destructive malware, wiper like caddy wiper, doublezero, and now swiftslicer that delete or overwrite files with random generated strings to make it unrecoverable. This analytic can also catch a possible recursive file write on multiple files using Sysmon Event 23. Sysmon treats the modified file as a deleted one as soon as it was overwritten. This analytic can be a good indicator that a possible destructive malware is running in a host machine or a big software application is being uninstalled.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = uninstall a big software application may trigger this detection. Filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Data Destruction Recursive Exec Files Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Swift Slicer", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = a big number of executable files being deleted in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "deleted_files", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Data Destruction Recursive Exec Files Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Swift Slicer", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious process recursively deleting files in a compromised host. This behavior was seen in several destructive malware, wiper like caddy wiper, doublezero, and now swiftslicer that delete or overwrite files with random generated strings to make it unrecoverable. This analytic can also catch a possible recursive file write on multiple files using Sysmon Event 23. Sysmon treats the modified file as a deleted one as soon as it was overwritten. This analytic can be a good indicator that a possible destructive malware is running in a host machine or a big software application is being uninstalled.
action.notable.param.rule_title = Windows Data Destruction Recursive Exec Files Deletion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=23  TargetFilename IN ("*.exe", "*.sys", "*.dll") | bin _time span=2m | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by dest user EventCode Image ProcessID | where count >=500 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_data_destruction_recursive_exec_files_deletion_filter`

[ESCU - Windows Defacement Modify Transcodedwallpaper File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a modification to the Transcodedwallpaper file in the wallpaper theme directory to change the wallpaper of the host machine. This technique was seen in adversaries attempting to deface or change the desktop wallpaper of the targeted host. During our testing, the common process that affects or changes the wallpaper if a user changes it via desktop personalized setting is explorer.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1491"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a modification to the Transcodedwallpaper file in the wallpaper theme directory to change the wallpaper of the host machine. This technique was seen in adversaries attempting to deface or change the desktop wallpaper of the targeted host. During our testing, the common process that affects or changes the wallpaper if a user changes it via desktop personalized setting is explorer.exe.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = 3rd part software application can change the wallpaper. Filter is needed.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Defacement Modify Transcodedwallpaper File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Brute Ratel C4"]
action.risk = 1
action.risk.param._risk_message = modification or creation of transcodedwallpaper file by $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Defacement Modify Transcodedwallpaper File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1491"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_path !="*\\Windows\\Explorer.EXE" by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid  Processes.original_file_name | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\AppData\\Roaming\\Microsoft\\Windows\\Themes\\TranscodedWallpaper" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields file_name file_path process_name process_path process dest file_create_time _time proc_guid] | `windows_defacement_modify_transcodedwallpaper_file_filter`

[ESCU - Windows Default Group Policy Object Modified - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event ID 5136 to identify the modification of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the modification of the default GPOs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event ID 5136 to identify the modification of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the modification of the default GPOs.
action.escu.how_to_implement = To successfully implement this search, the Advanced Security Audit policy setting `Audit Directory Service Changes` within `DS Access` needs to be enabled. Furthermore, the appropriate system access control lists (SACL) need to be created as the used events are not logged by default. A good guide to accomplish this can be found here https://jgspiers.com/audit-group-policy-changes/.
action.escu.known_false_positives = The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed.
action.escu.creation_date = 2023-03-28
action.escu.modification_date = 2023-03-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Default Group Policy Object Modified - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A default group policy object was modified on $Computer$ by $SubjectUserSid$
action.risk.param._risk = [{"risk_object_field": "SubjectUserSid", "risk_object_type": "user", "risk_score": 50}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Default Group Policy Object Modified - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 100, "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event ID 5136 to identify the modification of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the modification of the default GPOs.
action.notable.param.rule_title = Windows Default Group Policy Object Modified
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=versionNumber (ObjectDN="CN={31B2F340-016D-11D2-945F-00C04FB984F9},CN=POLICIES,CN=SYSTEM,DC=*" OR ObjectDN="CN={6AC1786C-016F-11D2-945F-00C04fB984F9},CN=POLICIES,CN=SYSTEM,DC=*") | stats min(_time) as firstTime max(_time) as lastTime by ObjectDN SubjectUserSid AttributeValue Computer DSName | rename AttributeValue as versionNumber | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_filter`

[ESCU - Windows Default Group Policy Object Modified with GPME - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages the Endpoint datamodel to identify the potential edition of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the edition of the default GPOs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic leverages the Endpoint datamodel to identify the potential edition of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the edition of the default GPOs.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed.
action.escu.creation_date = 2023-04-24
action.escu.modification_date = 2023-04-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Default Group Policy Object Modified with GPME - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A default group policy object was opened with Group Policy Manage Editor on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 50}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 50}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Default Group Policy Object Modified with GPME - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 100, "mitre_attack": ["T1484", "T1484.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages the Endpoint datamodel to identify the potential edition of a default Group Policy Object. A fresh installation of an Active Directory network will typically contain two default group policy objects `Default Domain Controllers Policy` and `Default Domain Policy`. The default domain controllers policy is used to enforce and set policies to all the domain controllers within the domain environment. The default domain policy is linked to all users and computers by default. An adversary who has obtained privileged access to an Active Directory network may modify the default group policy objects to obtain further access, deploy persistence or execute malware across a large number of hosts. Security teams should monitor the edition of the default GPOs.
action.notable.param.rule_title = Windows Default Group Policy Object Modified with GPME
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mmc.exe (Processes.process =*gpme.msc*) AND (Processes.process = "*31B2F340-016D-11D2-945F-00C04FB984F9*" OR Processes.process = "*6AC1786C-016F-11D2-945F-00C04fB984F9*"  ) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_with_gpme_filter`

[ESCU - Windows Defender Exclusion Registry Entry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect a suspicious process that modify a registry related to windows defender exclusion feature. This registry is abused by adversaries, malware author and red teams to bypassed Windows Defender Anti-Virus product by excluding folder path, file path, process, extensions and etc. from its real time or schedule scan to execute their malicious code. This is a good indicator for a defense evasion and to look further for events after this behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will detect a suspicious process that modify a registry related to windows defender exclusion feature. This registry is abused by adversaries, malware author and red teams to bypassed Windows Defender Anti-Virus product by excluding folder path, file path, process, extensions and etc. from its real time or schedule scan to execute their malicious code. This is a good indicator for a defense evasion and to look further for events after this behavior.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to use this windows features.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Defender Exclusion Registry Entry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos", "Windows Defense Evasion Tactics", "Azorult", "Qakbot", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = Exclusion registry $registry_path$ modified or added on $dest$ for Windows Defender
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Defender Exclusion Registry Entry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos", "Windows Defense Evasion Tactics", "Azorult", "Qakbot", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will detect a suspicious process that modify a registry related to windows defender exclusion feature. This registry is abused by adversaries, malware author and red teams to bypassed Windows Defender Anti-Virus product by excluding folder path, file path, process, extensions and etc. from its real time or schedule scan to execute their malicious code. This is a good indicator for a defense evasion and to look further for events after this behavior.
action.notable.param.rule_title = Windows Defender Exclusion Registry Entry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_defender_exclusion_registry_entry_filter`

[ESCU - Windows Delete or Modify System Firewall - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies potentially malicious 'netsh' processes that manipulate firewall configurations. This behavior has been observed in the NJRAT malware, which deletes its added firewall rules as part of its cleanup process. Leveraging this anomaly detection can be a valuable approach for detecting malware, such as NJRAT, that makes alterations to firewall configurations as a component of its malicious activities.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1562.004"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies potentially malicious 'netsh' processes that manipulate firewall configurations. This behavior has been observed in the NJRAT malware, which deletes its added firewall rules as part of its cleanup process. Leveraging this anomaly detection can be a valuable approach for detecting malware, such as NJRAT, that makes alterations to firewall configurations as a component of its malicious activities.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator may modify or delete firewall configuration.
action.escu.creation_date = 2023-09-08
action.escu.modification_date = 2023-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Delete or Modify System Firewall - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = A $process_name$ deleted a firewall configuration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Delete or Modify System Firewall - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1562", "T1562.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process = "* firewall *" Processes.process = "* delete *" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_delete_or_modify_system_firewall_filter`

[ESCU - Windows Deleted Registry By A Non Critical Process File Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect deletion of registry with suspicious process file path. This technique was seen in Double Zero wiper malware where it will delete all the subkey in HKLM, HKCU and HKU registry hive as part of its destructive payload to the targeted hosts. This anomaly detections can catch possible malware or advesaries deleting registry as part of defense evasion or even payload impact but can also catch for third party application updates or installation. In this scenario false positive filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect deletion of registry with suspicious process file path. This technique was seen in Double Zero wiper malware where it will delete all the subkey in HKLM, HKCU and HKU registry hive as part of its destructive payload to the targeted hosts. This anomaly detections can catch possible malware or advesaries deleting registry as part of defense evasion or even payload impact but can also catch for third party application updates or installation. In this scenario false positive filter is needed.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This detection can catch for third party application updates or installation. In this scenario false positive filter is needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Deleted Registry By A Non Critical Process File Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Double Zero Destructor"]
action.risk = 1
action.risk.param._risk_message = registry was deleted by a suspicious $process_name$ with proces path $process_path in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Deleted Registry By A Non Critical Process File Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Double Zero Destructor"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry WHERE Registry.action=deleted BY _time span=1h Registry.dest Registry.registry_path Registry.registry_value_name Registry.registry_key_name Registry.process_guid Registry.registry_value_data Registry.action | `drop_dm_object_name(Registry)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT (Processes.process_path IN ("*\\windows\\*", "*\\program files*")) by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.user Processes.parent_process_name Processes.parent_process Processes.process_path Processes.process_guid | `drop_dm_object_name(Processes)`] | fields _time parent_process_name parent_process process_name process_path process process_guid registry_path registry_value_name registry_value_data registry_key_name action dest user | `windows_deleted_registry_by_a_non_critical_process_file_path_filter`

[ESCU - Windows Disable Change Password Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to disable change password feature of the windows host. This registry modification may disables the Change Password button on the Windows Security dialog box (which appears when you press Ctrl+Alt+Del). As a result, users cannot change their Windows password on demand. This technique was seen in some malware family like ransomware to prevent the user to change the password after ownning the network or a system during attack. This windows feature may implemented by administrator to prevent normal user to change the password of a critical host or server, In this type of scenario filter is needed to minimized false positive.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Change", "Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to disable change password feature of the windows host. This registry modification may disables the Change Password button on the Windows Security dialog box (which appears when you press Ctrl+Alt+Del). As a result, users cannot change their Windows password on demand. This technique was seen in some malware family like ransomware to prevent the user to change the password after ownning the network or a system during attack. This windows feature may implemented by administrator to prevent normal user to change the password of a critical host or server, In this type of scenario filter is needed to minimized false positive.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = This windows feature may implemented by administrator to prevent normal user to change the password of a critical host or server, In this type of scenario filter is needed to minimized false positive.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Change Password Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Registry modification in "DisableChangePassword" on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Change Password Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_change_password_through_registry_filter`

[ESCU - Windows Disable Lock Workstation Feature Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to disable Lock Computer windows features. This registry modification prevent the user from locking its screen or computer that are being abused by several malware for example ransomware. This technique was used by threat actor to make its payload more impactful to the compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to disable Lock Computer windows features. This registry modification prevent the user from locking its screen or computer that are being abused by several malware for example ransomware. This technique was used by threat actor to make its payload more impactful to the compromised host.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Lock Workstation Feature Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Registry modification in "DisableLockWorkstation" on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Lock Workstation Feature Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_lock_workstation_feature_through_registry_filter`

[ESCU - Windows Disable LogOff Button Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to disable logoff feature in windows host. This registry when enable will prevent users to log off of the system by using any method, including programs run from the command line, such as scripts. It also disables or removes all menu items and buttons that log the user off of the system. This technique was seen abused by ransomware malware to make the compromised host un-useful and hard to remove other registry modification made on the machine that needs restart to take effect. This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to disable logoff feature in windows host. This registry when enable will prevent users to log off of the system by using any method, including programs run from the command line, such as scripts. It also disables or removes all menu items and buttons that log the user off of the system. This technique was seen abused by ransomware malware to make the compromised host un-useful and hard to remove other registry modification made on the machine that needs restart to take effect. This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable LogOff Button Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Registry modification in "NoLogOff" on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable LogOff Button Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" Registry.registry_value_name IN ("NoLogOff", "StartMenuLogOff") Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_logoff_button_through_registry_filter`

[ESCU - Windows Disable Memory Crash Dump - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process that is attempting to disable the ability on Windows to generate a memory crash dump. This was recently identified being utilized by HermeticWiper. To disable crash dumps, the value must be set to 0. This feature is typically modified to perform a memory crash dump when a computer stops unexpectedly because of a Stop error (also known as a blue screen, system crash, or bug check).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process that is attempting to disable the ability on Windows to generate a memory crash dump. This was recently identified being utilized by HermeticWiper. To disable crash dumps, the value must be set to 0. This feature is typically modified to perform a memory crash dump when a computer stops unexpectedly because of a Stop error (also known as a blue screen, system crash, or bug check).
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` and `Registry` node.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Memory Crash Dump - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Data Destruction", "Windows Registry Abuse", "Hermetic Wiper"]
action.risk = 1
action.risk.param._risk_message = A process was identified attempting to disable memory crash dumps on $dest$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Memory Crash Dump - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Data Destruction", "Windows Registry Abuse", "Hermetic Wiper"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a process that is attempting to disable the ability on Windows to generate a memory crash dump. This was recently identified being utilized by HermeticWiper. To disable crash dumps, the value must be set to 0. This feature is typically modified to perform a memory crash dump when a computer stops unexpectedly because of a Stop error (also known as a blue screen, system crash, or bug check).
action.notable.param.rule_title = Windows Disable Memory Crash Dump
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\CurrentControlSet\\Control\\CrashControl\\CrashDumpEnabled") AND Registry.registry_value_data="0x00000000" by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.registry_key_name  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_memory_crash_dump_filter`

[ESCU - Windows Disable Notification Center - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following search identifies a modification of registry to disable the windows notification center feature in a windows host machine. This registry modification removes notification and action center from the notification area on the task bar. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following search identifies a modification of registry to disable the windows notification center feature in a windows host machine. This registry modification removes notification and action center from the notification area on the task bar. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = admin or user may choose to disable this windows features.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Notification Center - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = The Windows notification center was disabled on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Notification Center - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name= "DisableNotificationCenter" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_notification_center_filter`

[ESCU - Windows Disable or Modify Tools Via Taskkill - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to identify potentially malicious processes that terminate other processes using taskkill.exe. This technique has been observed in various malware instances, employed by adversaries and red teamers alike, to forcibly terminate other processes whether they be security products or other legitimate applications as part of their malicious activities. Detecting this anomaly serves as a valuable alert mechanism to identify suspicious processes or malware attempting to evade detection and disrupt system stability.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1562.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is designed to identify potentially malicious processes that terminate other processes using taskkill.exe. This technique has been observed in various malware instances, employed by adversaries and red teamers alike, to forcibly terminate other processes whether they be security products or other legitimate applications as part of their malicious activities. Detecting this anomaly serves as a valuable alert mechanism to identify suspicious processes or malware attempting to evade detection and disrupt system stability.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Network administrator can use this application to kill process during audit or investigation.
action.escu.creation_date = 2023-09-13
action.escu.modification_date = 2023-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable or Modify Tools Via Taskkill - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = A taskkill process to terminate process is executed on host- $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"threat_object_field": "parent_process_name", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable or Modify Tools Via Taskkill - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1562", "T1562.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe" Processes.process IN ("* /f*", "* /t*") Processes.process IN ("* /im*", "* /pid*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_or_modify_tools_via_taskkill_filter`

[ESCU - Windows Disable Shutdown Button Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to disable shutdown button on the logon user. This technique was seen in several malware especially in ransomware family like killdisk malware variant to make the compromised host un-useful and hard to remove other registry modification made on the machine that needs restart to take effect. This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to disable shutdown button on the logon user. This technique was seen in several malware especially in ransomware family like killdisk malware variant to make the compromised host un-useful and hard to remove other registry modification made on the machine that needs restart to take effect. This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Shutdown Button Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Registry modification in "shutdownwithoutlogon" on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Shutdown Button Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\shutdownwithoutlogon" Registry.registry_value_data = "0x00000000") OR (Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoClose" Registry.registry_value_data = "0x00000001")) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_shutdown_button_through_registry_filter`

[ESCU - Windows Disable Windows Event Logging Disable HTTP Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies AppCmd.exe being utilized to disable HTTP logging on IIS. Adversaries may perform this action to disable logging and delete the logs so remove any trace or events on disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.002", "T1562", "T1505", "T1505.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies AppCmd.exe being utilized to disable HTTP logging on IIS. Adversaries may perform this action to disable logging and delete the logs so remove any trace or events on disk.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present only if scripts or Administrators are disabling logging. Filter as needed by parent process or other.
action.escu.creation_date = 2022-12-21
action.escu.modification_date = 2022-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Windows Event Logging Disable HTTP Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IIS Components", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable IIS HTTP Logging.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Windows Event Logging Disable HTTP Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562.002", "T1562", "T1505", "T1505.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies AppCmd.exe being utilized to disable HTTP logging on IIS. Adversaries may perform this action to disable logging and delete the logs so remove any trace or events on disk.
action.notable.param.rule_title = Windows Disable Windows Event Logging Disable HTTP Logging
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name  IN ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process IN ("*set config*", "*httplogging*","*dontlog:true*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_event_logging_disable_http_logging_filter`

[ESCU - Windows Disable Windows Group Policy Features Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to disable windows features. These techniques are seen in several ransomware malware to impair the compromised host to make it hard for analyst to mitigate or response from the attack. Disabling these known features make the analysis and forensic response more hard. Disabling these feature is not so common but can still be implemented by the administrator for security purposes. In this scenario filters for users that are allowed doing this is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to disable windows features. These techniques are seen in several ransomware malware to impair the compromised host to make it hard for analyst to mitigate or response from the attack. Disabling these known features make the analysis and forensic response more hard. Disabling these feature is not so common but can still be implemented by the administrator for security purposes. In this scenario filters for users that are allowed doing this is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = Disabling these features for legitimate purposes is not a common use case but can still be implemented by the administrators. Filter as needed.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Disable Windows Group Policy Features Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Registry modification to disable windows group policy features on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Disable Windows Group Policy Features Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\*" Registry.registry_value_name IN ("NoDesktop", "NoFind", "NoControlPanel", "NoFileMenu", "NoSetTaskbar", "NoTrayContextMenu", "TaskbarLockAll", "NoThemesTab","NoPropertiesMyDocuments","NoVisualStyleChoice","NoColorChoice","NoPropertiesMyDocuments") Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_group_policy_features_through_registry_filter`

[ESCU - Windows DisableAntiSpyware Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
action.escu.known_false_positives = It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DisableAntiSpyware Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult", "Windows Registry Abuse", "CISA AA22-264A", "RedLine Stealer", "Windows Defense Evasion Tactics", "Ryuk Ransomware"]
action.risk = 1
action.risk.param._risk_message = Windows DisableAntiSpyware registry key set to 'disabled' on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DisableAntiSpyware Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult", "Windows Registry Abuse", "CISA AA22-264A", "RedLine Stealer", "Windows Defense Evasion Tactics", "Ryuk Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
action.notable.param.rule_title = Windows DisableAntiSpyware Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_value_name="DisableAntiSpyware" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`

[ESCU - Windows DiskCryptor Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies DiskCryptor  process name of dcrypt.exe or internal name dcinst.exe. This utility has been utilized by adversaries to encrypt disks manually during an operation. In addition, during install, a dcrypt.sys driver is installed and requires a reboot in order to take effect. There are no command-line arguments used.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies DiskCryptor  process name of dcrypt.exe or internal name dcinst.exe. This utility has been utilized by adversaries to encrypt disks manually during an operation. In addition, during install, a dcrypt.sys driver is installed and requires a reboot in order to take effect. There are no command-line arguments used.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible false positives may be present based on the internal name dcinst.exe, filter as needed. It may be worthy to alert on the service name.
action.escu.creation_date = 2021-11-15
action.escu.modification_date = 2021-11-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DiskCryptor Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DiskCryptor Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1486"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dcrypt.exe" OR Processes.original_file_name=dcinst.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_diskcryptor_usage_filter`

[ESCU - Windows Diskshadow Proxy Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = DiskShadow.exe is a Microsoft Signed binary present on Windows Server. It has a scripting mode intended for complex scripted backup operations. This feature also allows for execution of arbitrary unsigned code. This analytic looks for the usage of the scripting mode flags in executions of DiskShadow. During triage, compare to known backup behavior in your environment and then review the scripts called by diskshadow.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = DiskShadow.exe is a Microsoft Signed binary present on Windows Server. It has a scripting mode intended for complex scripted backup operations. This feature also allows for execution of arbitrary unsigned code. This analytic looks for the usage of the scripting mode flags in executions of DiskShadow. During triage, compare to known backup behavior in your environment and then review the scripts called by diskshadow.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators using the DiskShadow tool in their infrastructure as a main backup tool with scripts will cause false positives that can be filtered with `windows_diskshadow_proxy_execution_filter`
action.escu.creation_date = 2022-02-15
action.escu.modification_date = 2022-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Diskshadow Proxy Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = Possible Signed Binary Proxy Execution on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Diskshadow Proxy Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = DiskShadow.exe is a Microsoft Signed binary present on Windows Server. It has a scripting mode intended for complex scripted backup operations. This feature also allows for execution of arbitrary unsigned code. This analytic looks for the usage of the scripting mode flags in executions of DiskShadow. During triage, compare to known backup behavior in your environment and then review the scripts called by diskshadow.
action.notable.param.rule_title = Windows Diskshadow Proxy Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_diskshadow` (Processes.process=*-s* OR Processes.process=*/s*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_diskshadow_proxy_execution_filter`

[ESCU - Windows DISM Remove Defender - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of the Windows Disk Image Utility, `dism.exe`, to remove Windows Defender. Adversaries may use `dism.exe` to disable Defender before completing their objective.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of the Windows Disk Image Utility, `dism.exe`, to remove Windows Defender. Adversaries may use `dism.exe` to disable Defender before completing their objective.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legitimate administrative tools leverage `dism.exe` to manipulate packages and features of the operating system. Filter as needed.
action.escu.creation_date = 2021-11-17
action.escu.modification_date = 2021-11-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DISM Remove Defender - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable Windows Defender.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DISM Remove Defender - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of the Windows Disk Image Utility, `dism.exe`, to remove Windows Defender. Adversaries may use `dism.exe` to disable Defender before completing their objective.
action.notable.param.rule_title = Windows DISM Remove Defender
action.notable.param.security_domain = access
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe (Processes.process="*/online*" AND Processes.process="*/disable-feature*" AND Processes.process="*Windows-Defender*" AND Processes.process="*/remove*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dism_remove_defender_filter`

[ESCU - Windows DLL Search Order Hijacking Hunt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic is an experimental query built against a accidental feature using the latest Sysmon TA 3.0 (https://splunkbase.splunk.com/app/5709/) which maps the module load (ImageLoaded) to process_name. This analytic will deprecate once this is fixed. This hunting analytic identifies known libraries in Windows that may be used in a DLL search order hijack or DLL Sideloading setting. This may require recompiling the DLL, moving the DLL or moving the vulnerable process. The query looks for any running out of system32 or syswow64. Some libraries natively run out of other application paths and will need to be added to the exclusion as needed. The lookup is comprised of Microsoft native libraries identified within the Hijacklibs.net project.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.001", "T1574"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic is an experimental query built against a accidental feature using the latest Sysmon TA 3.0 (https://splunkbase.splunk.com/app/5709/) which maps the module load (ImageLoaded) to process_name. This analytic will deprecate once this is fixed. This hunting analytic identifies known libraries in Windows that may be used in a DLL search order hijack or DLL Sideloading setting. This may require recompiling the DLL, moving the DLL or moving the vulnerable process. The query looks for any running out of system32 or syswow64. Some libraries natively run out of other application paths and will need to be added to the exclusion as needed. The lookup is comprised of Microsoft native libraries identified within the Hijacklibs.net project.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present based on paths. Filter or add other paths to the exclusion as needed.
action.escu.creation_date = 2022-12-31
action.escu.modification_date = 2022-12-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DLL Search Order Hijacking Hunt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land", "Windows Defense Evasion Tactics"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DLL Search Order Hijacking Hunt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 10, "impact": 10, "mitre_attack": ["T1574.001", "T1574"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown NOT (Processes.process_path IN ("*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*"))  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process_path | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup hijacklibs library AS process_name OUTPUT islibrary | search islibrary = True | rename parent_process_name as "Process Name" process_name AS ImageLoaded process_path AS Module_Path | `windows_dll_search_order_hijacking_hunt_filter`

[ESCU - Windows DLL Search Order Hijacking Hunt with Sysmon - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic is an experimental query built against a accidental feature using the latest Sysmon TA 3.0 (https://splunkbase.splunk.com/app/5709/) which maps the module load (ImageLoaded) to process_name. This analytic will deprecate once this is fixed. This hunting analytic identifies known libraries in Windows that may be used in a DLL search order hijack or DLL Sideloading setting. This may require recompiling the DLL, moving the DLL or moving the vulnerable process. The query looks for any running out of system32 or syswow64. Some libraries natively run out of other application paths and will need to be added to the exclusion as needed. The lookup is comprised of Microsoft native libraries identified within the Hijacklibs.net project.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.001", "T1574"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic is an experimental query built against a accidental feature using the latest Sysmon TA 3.0 (https://splunkbase.splunk.com/app/5709/) which maps the module load (ImageLoaded) to process_name. This analytic will deprecate once this is fixed. This hunting analytic identifies known libraries in Windows that may be used in a DLL search order hijack or DLL Sideloading setting. This may require recompiling the DLL, moving the DLL or moving the vulnerable process. The query looks for any running out of system32 or syswow64. Some libraries natively run out of other application paths and will need to be added to the exclusion as needed. The lookup is comprised of Microsoft native libraries identified within the Hijacklibs.net project.
action.escu.how_to_implement = The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
action.escu.known_false_positives = False positives will be present based on paths. Filter or add other paths to the exclusion as needed.
action.escu.creation_date = 2022-12-31
action.escu.modification_date = 2022-12-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DLL Search Order Hijacking Hunt with Sysmon - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Living Off The Land", "Windows Defense Evasion Tactics", "Qakbot"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DLL Search Order Hijacking Hunt with Sysmon - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land", "Windows Defense Evasion Tactics", "Qakbot"], "cis20": ["CIS 10"], "confidence": 10, "impact": 10, "mitre_attack": ["T1574.001", "T1574"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 NOT (process_path IN ("*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*")) | lookup hijacklibs library AS process_name OUTPUT islibrary | search islibrary = True |rename process_name AS ImageLoaded process_path AS Module_Path | stats values(parent_process_name) count by _time dest ImageLoaded Module_Path | `windows_dll_search_order_hijacking_hunt_with_sysmon_filter`

[ESCU - Windows DLL Search Order Hijacking with iscsicpl - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a recently disclosed search ordler DLL hijack in iscsicpl.exe. The malicious DLL must be in a new path and iscsicpl.exe, upon load, will execute the payload. The analytic is restricted to Windows shells. Two proof of concepts were identified and utilized to determine the behavior. The command-line is an option to go after, but most likely identifying a child process off iscsicpl.exe will be more effective. Monitoring for suspicious DLL loads is also an option.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a recently disclosed search ordler DLL hijack in iscsicpl.exe. The malicious DLL must be in a new path and iscsicpl.exe, upon load, will execute the payload. The analytic is restricted to Windows shells. Two proof of concepts were identified and utilized to determine the behavior. The command-line is an option to go after, but most likely identifying a child process off iscsicpl.exe will be more effective. Monitoring for suspicious DLL loads is also an option.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filtering may be required. Remove the Windows Shells macro to determine if other utilities are using iscsicpl.exe.
action.escu.creation_date = 2022-07-29
action.escu.modification_date = 2022-07-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DLL Search Order Hijacking with iscsicpl - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to elevate access.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DLL Search Order Hijacking with iscsicpl - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1574.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a recently disclosed search ordler DLL hijack in iscsicpl.exe. The malicious DLL must be in a new path and iscsicpl.exe, upon load, will execute the payload. The analytic is restricted to Windows shells. Two proof of concepts were identified and utilized to determine the behavior. The command-line is an option to go after, but most likely identifying a child process off iscsicpl.exe will be more effective. Monitoring for suspicious DLL loads is also an option.
action.notable.param.rule_title = Windows DLL Search Order Hijacking with iscsicpl
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=iscsicpl.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_dll_search_order_hijacking_with_iscsicpl_filter`

[ESCU - Windows DLL Side-Loading In Calc - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious DLL modules loaded by calc.exe that are not in windows %systemroot%\system32 or %systemroot%\sysWoW64 folder. This technique is well used by Qakbot malware to execute its malicious DLL file via dll side loading technique in calc process execution. This TTP detection is a good indicator that a suspicious dll was loaded in a public or non-common installation folder of Windows Operating System that needs further investigation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious DLL modules loaded by calc.exe that are not in windows %systemroot%\system32 or %systemroot%\sysWoW64 folder. This technique is well used by Qakbot malware to execute its malicious DLL file via dll side loading technique in calc process execution. This TTP detection is a good indicator that a suspicious dll was loaded in a public or non-common installation folder of Windows Operating System that needs further investigation.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on processes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-24
action.escu.modification_date = 2022-10-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DLL Side-Loading In Calc - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = a dll modules is loaded by calc.exe in $ImageLoaded$ that are not in common windows OS installation folder in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DLL Side-Loading In Calc - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious DLL modules loaded by calc.exe that are not in windows %systemroot%\system32 or %systemroot%\sysWoW64 folder. This technique is well used by Qakbot malware to execute its malicious DLL file via dll side loading technique in calc process execution. This TTP detection is a good indicator that a suspicious dll was loaded in a public or non-common installation folder of Windows Operating System that needs further investigation.
action.notable.param.rule_title = Windows DLL Side-Loading In Calc
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 Image = "*\calc.exe" AND NOT (Image IN ("*:\\windows\\system32\\*", "*:\\windows\\sysWow64\\*")) AND NOT(ImageLoaded IN("*:\\windows\\system32\\*", "*:\\windows\\sysWow64\\*", "*:\\windows\\WinSXS\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName Product process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_side_loading_in_calc_filter`

[ESCU - Windows DLL Side-Loading Process Child Of Calc - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the suspicious child process of calc.exe due to dll side loading technique to execute another executable. This technique was seen in qakbot malware that uses dll side loading technique to calc applications to load its malicious dll code. The malicious dll that abuses dll side loading technique will load the actual qakbot loader dll using regsvr32.exe application. This TTP is a good indicator of qakbot since the calc.exe will not load other child processes aside from win32calc.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the suspicious child process of calc.exe due to dll side loading technique to execute another executable. This technique was seen in qakbot malware that uses dll side loading technique to calc applications to load its malicious dll code. The malicious dll that abuses dll side loading technique will load the actual qakbot loader dll using regsvr32.exe application. This TTP is a good indicator of qakbot since the calc.exe will not load other child processes aside from win32calc.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-20
action.escu.modification_date = 2022-10-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DLL Side-Loading Process Child Of Calc - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = calc.exe has a child process $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DLL Side-Loading Process Child Of Calc - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "calc.exe")  AND Processes.process_name != "win32calc.exe" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_side_loading_process_child_of_calc_filter`

[ESCU - Windows DNS Gather Network Info - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line used to enumerate DNS records. Adversaries, threat actors, or red teamers may employ this technique to gather information about a victim's DNS, which can be utilized during targeting. This method was also observed as part of a tool used by the Sandworm APT group in a geopolitical cyber warfare attack. By using the dnscmd.exe Windows application, an attacker can enumerate DNS records for specific domains within the targeted network, potentially aiding in further attacks. This anomaly detection can serve as a valuable starting point for identifying users and hostnames that may be compromised or targeted by adversaries seeking to collect data information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1590.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line used to enumerate DNS records. Adversaries, threat actors, or red teamers may employ this technique to gather information about a victim's DNS, which can be utilized during targeting. This method was also observed as part of a tool used by the Sandworm APT group in a geopolitical cyber warfare attack. By using the dnscmd.exe Windows application, an attacker can enumerate DNS records for specific domains within the targeted network, potentially aiding in further attacks. This anomaly detection can serve as a valuable starting point for identifying users and hostnames that may be compromised or targeted by adversaries seeking to collect data information.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network administrator can execute this command to enumerate DNS record. Filter or add other paths to the exclusion as needed.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DNS Gather Network Info - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Sandworm Tools", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = A process commandline $process$ to enumerate dns record in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DNS Gather Network Info - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sandworm Tools", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1590.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dnscmd.exe" Processes.process = "* /enumrecords *" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dns_gather_network_info_filter`

[ESCU - Windows DnsAdmins New Member Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event ID 4732 to identify the addition of a new member to the DnsAdmins group within Active Directory. . Members of the DnsAdmin group can manage the DNS service which most of the times runs on the Domain Controller. By abusing legitimate DNS management functionality, a member of the DnsAdmins group can escalate privileges by executing malicious code on a Domain Controller as SYSTEM. Security teams should monitor the modification of the DnsAdmins group and validate the changes are legitimate.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event ID 4732 to identify the addition of a new member to the DnsAdmins group within Active Directory. . Members of the DnsAdmin group can manage the DNS service which most of the times runs on the Domain Controller. By abusing legitimate DNS management functionality, a member of the DnsAdmins group can escalate privileges by executing malicious code on a Domain Controller as SYSTEM. Security teams should monitor the modification of the DnsAdmins group and validate the changes are legitimate.
action.escu.how_to_implement = To successfully implement this search, Domain Controller events need to be ingested. The Advanced Security Audit policy setting `Audit Security Group Management` within `Account Management` needs to be enabled.
action.escu.known_false_positives = New members can be added to the DnsAdmins group as part of legitimate administrative tasks. Filter as needed.
action.escu.creation_date = 2023-04-06
action.escu.modification_date = 2023-04-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DnsAdmins New Member Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = A new member was added to the DnsAdmins group by $SubjectUserName$
action.risk.param._risk = [{"risk_object_field": "SubjectUserName", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DnsAdmins New Member Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1098"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event ID 4732 to identify the addition of a new member to the DnsAdmins group within Active Directory. . Members of the DnsAdmin group can manage the DNS service which most of the times runs on the Domain Controller. By abusing legitimate DNS management functionality, a member of the DnsAdmins group can escalate privileges by executing malicious code on a Domain Controller as SYSTEM. Security teams should monitor the modification of the DnsAdmins group and validate the changes are legitimate.
action.notable.param.rule_title = Windows DnsAdmins New Member Added
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4732 TargetUserName=DnsAdmins | stats min(_time) as firstTime max(_time) as lastTime values(SubjectUserName) values(Computer) by MemberSid, TargetUserName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dnsadmins_new_member_added_filter`

[ESCU - Windows Domain Admin Impersonation Indicator - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies potential Kerberos ticket forging attacks, specifically the Diamond Ticket attack. This is detected when a user logs into a host and the GroupMembership field in event 4627 indicates a privileged group (e.g., Domain Admins), but the user does not actually belong to that group in the directory service. The detection leverages Windows Security Event Log 4627, which logs account logon events. The analytic cross-references the GroupMembership field from the event against a pre-populated lookup of actual group memberships. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table. Any discrepancies between the events GroupMembership and the lookup indicate potential ticket forging. Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers to impersonate any user and potentially gain unauthorized access to resources. By forging a ticket that indicates membership in a privileged group, an attacker can bypass security controls and gain elevated privileges. Detecting such discrepancies in group memberships during logon events can be a strong indicator of this attack in progress, making it crucial for security teams to monitor and investigate. If validated as a true positive, this indicates that an attacker has successfully forged a Kerberos ticket and may have gained unauthorized access to critical resources, potentially with elevated privileges.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies potential Kerberos ticket forging attacks, specifically the Diamond Ticket attack. This is detected when a user logs into a host and the GroupMembership field in event 4627 indicates a privileged group (e.g., Domain Admins), but the user does not actually belong to that group in the directory service. The detection leverages Windows Security Event Log 4627, which logs account logon events. The analytic cross-references the GroupMembership field from the event against a pre-populated lookup of actual group memberships. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table. Any discrepancies between the events GroupMembership and the lookup indicate potential ticket forging. Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers to impersonate any user and potentially gain unauthorized access to resources. By forging a ticket that indicates membership in a privileged group, an attacker can bypass security controls and gain elevated privileges. Detecting such discrepancies in group memberships during logon events can be a strong indicator of this attack in progress, making it crucial for security teams to monitor and investigate. If validated as a true positive, this indicates that an attacker has successfully forged a Kerberos ticket and may have gained unauthorized access to critical resources, potentially with elevated privileges.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Authentication events across all endpoints and ingest Event Id 4627. Specifically, the Audit Group Membership subcategory within the Logon Logooff category needs to be enabled. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table.
action.escu.known_false_positives = False positives may trigger the detections certain scenarios like directory service delays or out of date lookups. Filter as needed.
action.escu.creation_date = 2023-10-06
action.escu.modification_date = 2023-10-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Domain Admin Impersonation Indicator - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = $TargetUserName$ may be impersonating a Domain Administrator through a forged Kerberos ticket.
action.risk.param._risk = [{"risk_object_field": "TargetUserName", "risk_object_type": "user", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Domain Admin Impersonation Indicator - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies potential Kerberos ticket forging attacks, specifically the Diamond Ticket attack. This is detected when a user logs into a host and the GroupMembership field in event 4627 indicates a privileged group (e.g., Domain Admins), but the user does not actually belong to that group in the directory service. The detection leverages Windows Security Event Log 4627, which logs account logon events. The analytic cross-references the GroupMembership field from the event against a pre-populated lookup of actual group memberships. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table. Any discrepancies between the events GroupMembership and the lookup indicate potential ticket forging. Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers to impersonate any user and potentially gain unauthorized access to resources. By forging a ticket that indicates membership in a privileged group, an attacker can bypass security controls and gain elevated privileges. Detecting such discrepancies in group memberships during logon events can be a strong indicator of this attack in progress, making it crucial for security teams to monitor and investigate. If validated as a true positive, this indicates that an attacker has successfully forged a Kerberos ticket and may have gained unauthorized access to critical resources, potentially with elevated privileges.
action.notable.param.rule_title = Windows Domain Admin Impersonation Indicator
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security`  EventCode=4627 LogonType=3 NOT TargetUserName IN ("*$", "SYSTEM", "DWM-*","LOCAL SERVICE","NETWORK SERVICE", "ANONYMOUS LOGON", "UMFD-*") | where match(GroupMembership, "Domain Admins") | lookup domain_admins username as TargetUserName OUTPUT username | fillnull value=NotDA username | search username = "NotDA" | stats  count by _time, TargetUserName, GroupMembership, host | `windows_domain_admin_impersonation_indicator_filter`

[ESCU - Windows DotNet Binary in Non Standard Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies native .net binaries within the Windows operating system that may be abused by adversaries by moving it to a new directory. The analytic identifies the .net binary by using a lookup and compares the process name and original file name (internal name). The analytic utilizes a lookup with the is_net_windows_file macro to identify the binary process name and original file name. if one or the other matches an alert will be generated. Adversaries abuse these binaries as they are native to windows and native DotNet. Note that not all SDK (post install of Windows) are captured in the lookup.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1036.003", "T1218", "T1218.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies native .net binaries within the Windows operating system that may be abused by adversaries by moving it to a new directory. The analytic identifies the .net binary by using a lookup and compares the process name and original file name (internal name). The analytic utilizes a lookup with the is_net_windows_file macro to identify the binary process name and original file name. if one or the other matches an alert will be generated. Adversaries abuse these binaries as they are native to windows and native DotNet. Note that not all SDK (post install of Windows) are captured in the lookup.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering may be required. Certain utilities will run from non-standard paths based on the third-party application in use.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows DotNet Binary in Non Standard Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Masquerading - Rename System Utilities", "Ransomware", "Unusual Processes", "Signed Binary Proxy Execution InstallUtil", "Data Destruction", "WhisperGate"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ from a non-standard path was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows DotNet Binary in Non Standard Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Masquerading - Rename System Utilities", "Ransomware", "Unusual Processes", "Signed Binary Proxy Execution InstallUtil", "Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1036", "T1036.003", "T1218", "T1218.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies native .net binaries within the Windows operating system that may be abused by adversaries by moving it to a new directory. The analytic identifies the .net binary by using a lookup and compares the process name and original file name (internal name). The analytic utilizes a lookup with the is_net_windows_file macro to identify the binary process name and original file name. if one or the other matches an alert will be generated. Adversaries abuse these binaries as they are native to windows and native DotNet. Note that not all SDK (post install of Windows) are captured in the lookup.
action.notable.param.rule_title = Windows DotNet Binary in Non Standard Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where NOT (Processes.process_path IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*", "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_path Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `is_net_windows_file` | `windows_dotnet_binary_in_non_standard_path_filter`

[ESCU - Windows Driver Inventory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting / inventory query assists defenders in identifying Drivers being loaded across the fleet. This query relies upon a PowerShell script input to be deployed to critical systems and beyond. If capturing all via the input, this will provide retrospection into drivers persisting. Note, that this is not perfect across a large fleet. Modify the query as you need to view the data differently.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting / inventory query assists defenders in identifying Drivers being loaded across the fleet. This query relies upon a PowerShell script input to be deployed to critical systems and beyond. If capturing all via the input, this will provide retrospection into drivers persisting. Note, that this is not perfect across a large fleet. Modify the query as you need to view the data differently.
action.escu.how_to_implement = To capture the drivers by host, utilize the referenced Gist to create the inputs, props and transforms. Otherwise, this hunt query will not work.
action.escu.known_false_positives = Filter and modify the analytic as you'd like. Filter based on path. Remove the system32\drivers and look for non-standard paths.
action.escu.creation_date = 2023-02-03
action.escu.modification_date = 2023-02-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Driver Inventory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Drivers"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Driver Inventory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers"], "cis20": ["CIS 10"], "confidence": 10, "impact": 50, "mitre_attack": ["T1068"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `driverinventory` | stats values(Path) min(_time) as firstTime max(_time) as lastTime count by host DriverType | rename host as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_driver_inventory_filter`

[ESCU - Windows Driver Load Non-Standard Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses Windows EventCode 7045 to identify new Kernel Mode Drivers being loaded in Windows from a non-standard path. Note that, adversaries may move malicious or vulnerable drivers into these paths and load up. The idea is that this analytic provides visibility into drivers loading in non-standard file paths.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1014", "T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic uses Windows EventCode 7045 to identify new Kernel Mode Drivers being loaded in Windows from a non-standard path. Note that, adversaries may move malicious or vulnerable drivers into these paths and load up. The idea is that this analytic provides visibility into drivers loading in non-standard file paths.
action.escu.how_to_implement = To implement this analytic, the Windows EventCode 7045 will need to be logged. The Windows TA for Splunk is also recommended.
action.escu.known_false_positives = False positives may be present based on legitimate third party applications needing to install drivers. Filter, or allow list known good drivers consistently being installed in these paths.
action.escu.creation_date = 2023-02-24
action.escu.modification_date = 2023-02-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Driver Load Non-Standard Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Drivers", "CISA AA22-320A", "AgentTesla", "BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = A kernel mode driver was loaded from a non-standard path on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Driver Load Non-Standard Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers", "CISA AA22-320A", "AgentTesla", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1014", "T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic uses Windows EventCode 7045 to identify new Kernel Mode Drivers being loaded in Windows from a non-standard path. Note that, adversaries may move malicious or vulnerable drivers into these paths and load up. The idea is that this analytic provides visibility into drivers loading in non-standard file paths.
action.notable.param.rule_title = Windows Driver Load Non-Standard Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045 ServiceType="kernel mode driver" NOT (ImagePath IN ("*\\Windows\\*", "*\\Program File*", "*\\systemroot\\*","%SystemRoot%*", "system32\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_driver_load_non_standard_path_filter`

[ESCU - Windows Drivers Loaded by Signature - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic assists with viewing all drivers being loaded by using Sysmon EventCode 6 (Driver Load). Sysmon provides some simple fields to assist with identifying suspicious drivers. Use this analytic to look at prevalence of driver (count), path of driver, signature status and hash. Review these fields with scrutiny until the ability to prove the driver is legitimate and has a purpose in the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1014", "T1068"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic assists with viewing all drivers being loaded by using Sysmon EventCode 6 (Driver Load). Sysmon provides some simple fields to assist with identifying suspicious drivers. Use this analytic to look at prevalence of driver (count), path of driver, signature status and hash. Review these fields with scrutiny until the ability to prove the driver is legitimate and has a purpose in the environment.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have the latest version of the Sysmon TA. Most EDR products provide the ability to review driver loads, or module loads, and using a query as such help with hunting for malicious drivers.
action.escu.known_false_positives = This analytic is meant to assist with identifying drivers loaded in the environment and not to be setup for notables off the bat.
action.escu.creation_date = 2022-03-30
action.escu.modification_date = 2022-03-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Drivers Loaded by Signature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Drivers", "CISA AA22-320A", "AgentTesla", "BlackByte Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Drivers Loaded by Signature - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers", "CISA AA22-320A", "AgentTesla", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1014", "T1068"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=6 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) count by dest Signed Signature service_signature_verified service_signature_exists Hashes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_drivers_loaded_by_signature_filter`

[ESCU - Windows Enable Win32 ScheduledJob via Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic searches for a registry modification that enables the use of the at.exe or wmi Win32_ScheduledJob command to add scheduled tasks on a Windows endpoint. Specifically, it looks for the creation of a new DWORD value named "EnableAt" in the following registry path: "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration". If this value is set to 1, it enables the at.exe and wmi Win32_ScheduledJob commands to schedule tasks on the system. Detecting this registry modification is important because it may indicate that an attacker has enabled the ability to add scheduled tasks to the system, which can be used to execute malicious code at specific times or intervals.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic searches for a registry modification that enables the use of the at.exe or wmi Win32_ScheduledJob command to add scheduled tasks on a Windows endpoint. Specifically, it looks for the creation of a new DWORD value named "EnableAt" in the following registry path: "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration". If this value is set to 1, it enables the at.exe and wmi Win32_ScheduledJob commands to schedule tasks on the system. Detecting this registry modification is important because it may indicate that an attacker has enabled the ability to add scheduled tasks to the system, which can be used to execute malicious code at specific times or intervals.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = In some cases, an automated script or system may enable this setting continuously, leading to false positives. To avoid such situations, it is recommended to monitor the frequency and context of the registry modification and modify or filter the detection rules as needed. This can help to reduce the number of false positives and ensure that only genuine threats are identified. Additionally, it is important to investigate any detected instances of this modification and analyze them in the broader context of the system and network to determine if further action is necessary.
action.escu.creation_date = 2023-03-27
action.escu.modification_date = 2023-03-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Enable Win32 ScheduledJob via Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A process has modified the schedule task registry value - EnableAt - on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Enable Win32 ScheduledJob via Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.005"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\CurrentVersion\\Schedule\\Configuration*" Registry.registry_value_name=EnableAt  by Registry.dest, Registry.user, Registry.registry_value_name, Registry.registry_value_type | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_enable_win32_scheduledjob_via_registry_filter`

[ESCU - Windows Event For Service Disabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious system event of services that was modified from start to disabled. This technique is seen where the adversary attempts to disable security app services, other malware services to evade the defense systems on the compromised host
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will identify suspicious system event of services that was modified from start to disabled. This technique is seen where the adversary attempts to disable security app services, other malware services to evade the defense systems on the compromised host
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = Windows service update may cause this event. In that scenario, filtering is needed.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Event For Service Disabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "RedLine Stealer"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Event For Service Disabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7040  Message = "*service was changed from demand start to disabled." | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Message User Sid service service_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_for_service_disabled_filter`

[ESCU - Windows Event Log Cleared - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Windows Security Event ID 1102 or System log event 104 to identify when a Windows event log is cleared. Note that this analytic will require tuning or restricted to specific endpoints based on criticality. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Windows Security Event ID 1102 or System log event 104 to identify when a Windows event log is cleared. Note that this analytic will require tuning or restricted to specific endpoints based on criticality. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
action.escu.known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. Filter as needed.
action.escu.creation_date = 2020-07-06
action.escu.modification_date = 2020-07-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Event Log Cleared - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware", "Clop Ransomware", "CISA AA22-264A"]
action.risk = 1
action.risk.param._risk_message = Windows event logs cleared on $dest$ via EventCode $EventCode$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Event Log Cleared - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware", "Clop Ransomware", "CISA AA22-264A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Windows Security Event ID 1102 or System log event 104 to identify when a Windows event log is cleared. Note that this analytic will require tuning or restricted to specific endpoints based on criticality. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.
action.notable.param.rule_title = Windows Event Log Cleared
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = (`wineventlog_security` EventCode=1102) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by dest Message EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter`

[ESCU - Windows Event Triggered Image File Execution Options Injection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies EventCode 3000 in Application channel indicating a process exit. This behavior is based on process names being added to the Image File Execution Options under HKLM \SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\ and \SOFTWARE\Microsoft\Windows NT\CurrentVersion\SilentProcessExit. Once these are set for a process, an eventcode 3000 will generate. The example used is from Thinkst Canary where a CanaryToken is setup to monitor for a commonly abused living off the land binary (ex. Klist.exe) and generate an event when it occurs. This can be seen as settings traps to monitor for suspicious behavior. Monitor and tune this hunting analytic and setup traps across your organization and begin monitoring.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.012"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic identifies EventCode 3000 in Application channel indicating a process exit. This behavior is based on process names being added to the Image File Execution Options under HKLM \SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\ and \SOFTWARE\Microsoft\Windows NT\CurrentVersion\SilentProcessExit. Once these are set for a process, an eventcode 3000 will generate. The example used is from Thinkst Canary where a CanaryToken is setup to monitor for a commonly abused living off the land binary (ex. Klist.exe) and generate an event when it occurs. This can be seen as settings traps to monitor for suspicious behavior. Monitor and tune this hunting analytic and setup traps across your organization and begin monitoring.
action.escu.how_to_implement = This analytic requires capturing the Windows Event Log Application channel in XML.
action.escu.known_false_positives = False positives may be present and tuning will be required before turning into a TTP or notable.
action.escu.creation_date = 2022-09-08
action.escu.modification_date = 2022-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Event Triggered Image File Execution Options Injection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Persistence Techniques"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Event Triggered Image File Execution Options Injection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1546.012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_application` EventCode=3000 | rename param1 AS "Process" param2 AS "Exit_Code" | stats count min(_time) as firstTime max(_time) as lastTime by Process Exit_Code dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_triggered_image_file_execution_options_injection_filter`

[ESCU - Windows Excessive Disabled Services Event - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious excessive number of system events of services that was modified from start to disabled. This technique is seen where the adversary attempts to disable security app services, other malware services oer serve as an destructive impact to complete the objective on the compromised system. One good example for this scenario is Olympic destroyer where it disable all active services in the compromised host as part of its destructive impact and defense evasion.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic will identify suspicious excessive number of system events of services that was modified from start to disabled. This technique is seen where the adversary attempts to disable security app services, other malware services oer serve as an destructive impact to complete the objective on the compromised system. One good example for this scenario is Olympic destroyer where it disable all active services in the compromised host as part of its destructive impact and defense evasion.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2022-02-23
action.escu.modification_date = 2022-02-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Excessive Disabled Services Event - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Service was disabled in $Computer$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Excessive Disabled Services Event - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify suspicious excessive number of system events of services that was modified from start to disabled. This technique is seen where the adversary attempts to disable security app services, other malware services oer serve as an destructive impact to complete the objective on the compromised system. One good example for this scenario is Olympic destroyer where it disable all active services in the compromised host as part of its destructive impact and defense evasion.
action.notable.param.rule_title = Windows Excessive Disabled Services Event
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7040 Message = "*service was changed from demand start to disabled." | stats count values(Message) as MessageList dc(Message) as MessageCount min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode  User Sid | where MessageCount >=10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_excessive_disabled_services_event_filter`

[ESCU - Windows Executable in Loaded Modules - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies potentially malicious 'ImageLoaded' events, particularly when they involve executable files. This behavior was observed in NjRAT instances, where, during each instance of loading a module from its C2 server onto the compromised host, Sysmon recorded the path of the actual Image or Process as an 'ImageLoaded' event, rather than the typical tracking of dynamically loaded DLL modules in memory. This event holds significance because it tracks processes that load modules and libraries, which are typically in the .dll format rather than .exe. Leveraging this 'Time-To-Perform' (TTP) detection method can prove invaluable for the identification of NjRAT malware or other malicious software instances that introduce executable files as modules within a targeted host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1129"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies potentially malicious 'ImageLoaded' events, particularly when they involve executable files. This behavior was observed in NjRAT instances, where, during each instance of loading a module from its C2 server onto the compromised host, Sysmon recorded the path of the actual Image or Process as an 'ImageLoaded' event, rather than the typical tracking of dynamically loaded DLL modules in memory. This event holds significance because it tracks processes that load modules and libraries, which are typically in the .dll format rather than .exe. Leveraging this 'Time-To-Perform' (TTP) detection method can prove invaluable for the identification of NjRAT malware or other malicious software instances that introduce executable files as modules within a targeted host.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown.
action.escu.creation_date = 2023-09-12
action.escu.modification_date = 2023-09-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Executable in Loaded Modules - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = An executable $Imageloaded$ loaded by $Image$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Executable in Loaded Modules - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1129"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies potentially malicious 'ImageLoaded' events, particularly when they involve executable files. This behavior was observed in NjRAT instances, where, during each instance of loading a module from its C2 server onto the compromised host, Sysmon recorded the path of the actual Image or Process as an 'ImageLoaded' event, rather than the typical tracking of dynamically loaded DLL modules in memory. This event holds significance because it tracks processes that load modules and libraries, which are typically in the .dll format rather than .exe. Leveraging this 'Time-To-Perform' (TTP) detection method can prove invaluable for the identification of NjRAT malware or other malicious software instances that introduce executable files as modules within a targeted host.
action.notable.param.rule_title = Windows Executable in Loaded Modules
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 ImageLoaded= *.exe | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed SignatureStatus OriginalFileName process_name Computer EventCode ProcessId Hashes IMPHASH | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_executable_in_loaded_modules_filter`

[ESCU - Windows Execute Arbitrary Commands with MSDT - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a recently disclosed arbitraty command execution using Windows msdt.exe - a Diagnostics Troubleshooting Wizard. The sample identified will use the ms-msdt:/ protocol handler to load msdt.exe to retrieve a remote payload. During triage, review file modifications for html. Identify parallel process execution that may be related, including an Office Product.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a recently disclosed arbitraty command execution using Windows msdt.exe - a Diagnostics Troubleshooting Wizard. The sample identified will use the ms-msdt:/ protocol handler to load msdt.exe to retrieve a remote payload. During triage, review file modifications for html. Identify parallel process execution that may be related, including an Office Product.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed. Added .xml to potentially capture any answer file usage. Remove as needed.
action.escu.creation_date = 2022-06-29
action.escu.modification_date = 2022-06-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Execute Arbitrary Commands with MSDT - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"]
action.risk = 1
action.risk.param._risk_message = A parent process $parent_process_name$ has spawned a child process $process_name$ on host $dest$ possibly indicative of indirect command execution.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 100}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Execute Arbitrary Commands with MSDT - Rule
action.correlationsearch.annotations = {"analytic_story": ["Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2022-30190"], "impact": 100, "mitre_attack": ["T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a recently disclosed arbitraty command execution using Windows msdt.exe - a Diagnostics Troubleshooting Wizard. The sample identified will use the ms-msdt:/ protocol handler to load msdt.exe to retrieve a remote payload. During triage, review file modifications for html. Identify parallel process execution that may be related, including an Office Product.
action.notable.param.rule_title = Windows Execute Arbitrary Commands with MSDT
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msdt.exe Processes.process IN ("*msdt*","*ms-msdt:*","*ms-msdt:/id*","*ms-msdt:-id*","*/id*") AND (Processes.process="*IT_BrowseForFile=*" OR Processes.process="*IT_RebrowseForFile=*" OR Processes.process="*.xml*") AND Processes.process="*PCWDiagnostic*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_execute_arbitrary_commands_with_msdt_filter`

[ESCU - Windows Exfiltration Over C2 Via Invoke RestMethod - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the potential exfiltration of data using PowerShell's Invoke-RestMethod. This technique was observed in the Winter-Vivern malware, which uploads desktop screenshots and files from compromised or targeted hosts. Detecting this TTP can serve as a valuable indicator that a process is attempting to upload files to an external or internal URI link. We recommend examining the process, the files it is trying to upload, and the URL link or C2 destination where the data is being uploaded.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1041"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the potential exfiltration of data using PowerShell's Invoke-RestMethod. This technique was observed in the Winter-Vivern malware, which uploads desktop screenshots and files from compromised or targeted hosts. Detecting this TTP can serve as a valuable indicator that a process is attempting to upload files to an external or internal URI link. We recommend examining the process, the files it is trying to upload, and the URL link or C2 destination where the data is being uploaded.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited. Filter as needed.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Exfiltration Over C2 Via Invoke RestMethod - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Winter Vivern"]
action.risk = 1
action.risk.param._risk_message = A PowerShell script on $Computer$ is attempting to transfer files to a remote URL.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Exfiltration Over C2 Via Invoke RestMethod - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1041"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the potential exfiltration of data using PowerShell's Invoke-RestMethod. This technique was observed in the Winter-Vivern malware, which uploads desktop screenshots and files from compromised or targeted hosts. Detecting this TTP can serve as a valuable indicator that a process is attempting to upload files to an external or internal URI link. We recommend examining the process, the files it is trying to upload, and the URL link or C2 destination where the data is being uploaded.
action.notable.param.rule_title = Windows Exfiltration Over C2 Via Invoke RestMethod
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Invoke-RestMethod *" AND ScriptBlockText = "* -Uri *" AND ScriptBlockText = "* -Method *" AND ScriptBlockText = "* Post *" AND ScriptBlockText = "* -InFile *" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_invoke_restmethod_filter`

[ESCU - Windows Exfiltration Over C2 Via Powershell UploadString - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies potential data exfiltration using the PowerShell net.webclient command. This technique was observed in the Winter-Vivern malware, which uploads desktop screenshots and files from compromised or targeted hosts. Detecting this TTP can serve as a valuable indicator that a process is attempting to upload files to an external or internal URI link. We recommend examining the process, the files it is trying to upload, and the URL link or C2 destination where the data is being uploaded.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1041"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies potential data exfiltration using the PowerShell net.webclient command. This technique was observed in the Winter-Vivern malware, which uploads desktop screenshots and files from compromised or targeted hosts. Detecting this TTP can serve as a valuable indicator that a process is attempting to upload files to an external or internal URI link. We recommend examining the process, the files it is trying to upload, and the URL link or C2 destination where the data is being uploaded.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited. Filter as needed.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Exfiltration Over C2 Via Powershell UploadString - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Winter Vivern"]
action.risk = 1
action.risk.param._risk_message = A PowerShell script on $Computer$ is attempting to transfer files to a remote URL.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Exfiltration Over C2 Via Powershell UploadString - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1041"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies potential data exfiltration using the PowerShell net.webclient command. This technique was observed in the Winter-Vivern malware, which uploads desktop screenshots and files from compromised or targeted hosts. Detecting this TTP can serve as a valuable indicator that a process is attempting to upload files to an external or internal URI link. We recommend examining the process, the files it is trying to upload, and the URL link or C2 destination where the data is being uploaded.
action.notable.param.rule_title = Windows Exfiltration Over C2 Via Powershell UploadString
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Net.webclient*" AND ScriptBlockText = "*.UploadString*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_powershell_uploadstring_filter`

[ESCU - Windows Export Certificate - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when a certificate is exported from the Windows Certificate Store. This analytic utilizes the Certificates Lifecycle log channel event ID 1007. EventID 1007 is focused on the Export of a certificate from the local certificate store. In addition, review the ProcessName field as it will help to determine automation/Admin or adversary extracting the certificate. Depending on the organization, the certificate may be used for authentication to the VPN or private resources.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.004", "T1552", "T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when a certificate is exported from the Windows Certificate Store. This analytic utilizes the Certificates Lifecycle log channel event ID 1007. EventID 1007 is focused on the Export of a certificate from the local certificate store. In addition, review the ProcessName field as it will help to determine automation/Admin or adversary extracting the certificate. Depending on the organization, the certificate may be used for authentication to the VPN or private resources.
action.escu.how_to_implement = To implement this analytic, you must collect Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational or Microsoft-Windows-CertificateServicesClient-Lifecycle-User/Operational.
action.escu.known_false_positives = False positives may be generated based on an automated process or service that exports certificates on the regular. Review is required before setting to alert. Monitor for abnormal processes performing an export.
action.escu.creation_date = 2023-02-11
action.escu.modification_date = 2023-02-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Export Certificate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = An certificate was exported on $dest$ from the Windows Certificate Store.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Export Certificate - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1552.004", "T1552", "T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `certificateservices_lifecycle` EventCode=1007 | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SubjectName, UserData_Xml | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_export_certificate_filter`

[ESCU - Windows File Share Discovery With Powerview - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of the Invoke-ShareFinder PowerShell commandlet part of PowerView. This module obtains the list of all active domain computers and lists the active shares on each computer. Network file shares in Active Directory environments may contain sensitive information like backups, scripts, credentials, etc. Adversaries who have obtained a foothold in an AD network may leverage PowerView to identify secrets and leverage them for Privilege Escalation or Lateral Movement.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1135"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the use of the Invoke-ShareFinder PowerShell commandlet part of PowerView. This module obtains the list of all active domain computers and lists the active shares on each computer. Network file shares in Active Directory environments may contain sensitive information like backups, scripts, credentials, etc. Adversaries who have obtained a foothold in an AD network may leverage PowerView to identify secrets and leverage them for Privilege Escalation or Lateral Movement.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
action.escu.known_false_positives = Security teams may leverage PowerView proactively to identify and remediate sensitive file shares. Filter as needed.
action.escu.creation_date = 2023-03-20
action.escu.modification_date = 2023-03-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows File Share Discovery With Powerview - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Invoke-ShareFinder commandlet was executed on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows File Share Discovery With Powerview - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1135"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of the Invoke-ShareFinder PowerShell commandlet part of PowerView. This module obtains the list of all active domain computers and lists the active shares on each computer. Network file shares in Active Directory environments may contain sensitive information like backups, scripts, credentials, etc. Adversaries who have obtained a foothold in an AD network may leverage PowerView to identify secrets and leverage them for Privilege Escalation or Lateral Movement.
action.notable.param.rule_title = Windows File Share Discovery With Powerview
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104  (ScriptBlockText=Invoke-ShareFinder*) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_share_discovery_with_powerview_filter`

[ESCU - Windows File Transfer Protocol In Non-Common Process Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a possible windows application having a FTP connection in a non common installation path in windows operating system.This network protocol is being used by adversaries, threat actors and malware like AgentTesla as a Command And Control communication to transfer its collected stolen information like the desktop screenshots, browser information and system information of a targeted or compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1071.003", "T1071"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a possible windows application having a FTP connection in a non common installation path in windows operating system.This network protocol is being used by adversaries, threat actors and malware like AgentTesla as a Command And Control communication to transfer its collected stolen information like the desktop screenshots, browser information and system information of a targeted or compromised host.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 3 connection events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = third party application may use this network protocol as part of its feature. Filter is needed.
action.escu.creation_date = 2022-09-16
action.escu.modification_date = 2022-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows File Transfer Protocol In Non-Common Process Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AgentTesla"]
action.risk = 1
action.risk.param._risk_message = a process $Image$ is having a FTP connection to $DestinationHostname$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows File Transfer Protocol In Non-Common Process Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["AgentTesla"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1071.003", "T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=3 NOT(Image IN("*\\program files*", "*\\windows\\system32\\*","*\\windows\\SysWOW64\\*")) (DestinationPortName="ftp" OR DestinationPort=21) | stats count min(_time) as firstTime max(_time) as lastTime by Image DestinationPort DestinationPortName DestinationHostname DestinationIp SourcePort SourcePortName Protocol SourceHostname dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_transfer_protocol_in_non_common_process_path_filter`

[ESCU - Windows File Without Extension In Critical Folder - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for suspicious file creation in the critical folder like "System32\Drivers" folder without file extension. This artifacts was seen in latest hermeticwiper where it drops its driver component in Driver Directory both the compressed(without file extension) and the actual driver component (with .sys file extension). This TTP is really a good indication that a host might be compromised by this destructive malware that wipes the boot sector of the system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for suspicious file creation in the critical folder like "System32\Drivers" folder without file extension. This artifacts was seen in latest hermeticwiper where it drops its driver component in Driver Directory both the compressed(without file extension) and the actual driver component (with .sys file extension). This TTP is really a good indication that a host might be compromised by this destructive malware that wipes the boot sector of the system.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = Unknown at this point
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows File Without Extension In Critical Folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Hermetic Wiper"]
action.risk = 1
action.risk.param._risk_message = Driver file with out file extension drop in $file_path$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows File Without Extension In Critical Folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Hermetic Wiper"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1485"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to look for suspicious file creation in the critical folder like "System32\Drivers" folder without file extension. This artifacts was seen in latest hermeticwiper where it drops its driver component in Driver Directory both the compressed(without file extension) and the actual driver component (with .sys file extension). This TTP is really a good indication that a host might be compromised by this destructive malware that wipes the boot sector of the system.
action.notable.param.rule_title = Windows File Without Extension In Critical Folder
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\System32\\drivers\\*", "*\\syswow64\\drivers\\*") by _time span=5m Filesystem.dest Filesystem.user Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.file_create_time | `drop_dm_object_name(Filesystem)` | rex field="file_name" "\.(?<extension>[^\.]*$)" | where isnull(extension) | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time span=5m Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)`] | stats count min(_time) as firstTime max(_time) as lastTime by dest process_name process_guid file_name file_path file_create_time user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_without_extension_in_critical_folder_filter`

[ESCU - Windows Njrat Fileless Storage via Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification associated with NjRat, a telltale sign of its fileless technique. NjRat employs this method to manage its keylogs and execute downloaded DLL module plugins discreetly on the compromised host. This approach is particularly effective at evading conventional file-based detection systems, as it stores indicators of compromise (IOCs) in the registry. Leveraging this TTP (Tactics, Techniques, and Procedures) detection can significantly enhance the identification of NjRAT infections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027.011", "T1027"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification associated with NjRat, a telltale sign of its fileless technique. NjRat employs this method to manage its keylogs and execute downloaded DLL module plugins discreetly on the compromised host. This approach is particularly effective at evading conventional file-based detection systems, as it stores indicators of compromise (IOCs) in the registry. Leveraging this TTP (Tactics, Techniques, and Procedures) detection can significantly enhance the identification of NjRAT infections.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-09-14
action.escu.modification_date = 2023-09-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Njrat Fileless Storage via Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = a suspicious registry entry related to NjRAT keylloging registry in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Njrat Fileless Storage via Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1027.011", "T1027"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious registry modification associated with NjRat, a telltale sign of its fileless technique. NjRat employs this method to manage its keylogs and execute downloaded DLL module plugins discreetly on the compromised host. This approach is particularly effective at evading conventional file-based detection systems, as it stores indicators of compromise (IOCs) in the registry. Leveraging this TTP (Tactics, Techniques, and Procedures) detection can significantly enhance the identification of NjRAT infections.
action.notable.param.rule_title = Windows Njrat Fileless Storage via Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\[kl]" OR  Registry.registry_value_data IN ("*[ENTER]*", "*[TAP]*", "*[Back]*") by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_njrat_fileless_storage_via_registry_filter`

[ESCU - Windows Files and Dirs Access Rights Modification Via Icacls - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic aims to identify potential adversaries who manipulate the security permissions of specific files or directories. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. By modifying the security permissions, adversaries seek to evade detection and impede access to their component files. Such actions indicate a deliberate effort to maintain control over compromised systems and hinder investigation or remediation efforts. Detecting these security permission changes can serve as a valuable indicator of an ongoing attack and enable timely response to mitigate the impact of the adversary's activities.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1222.001", "T1222"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic aims to identify potential adversaries who manipulate the security permissions of specific files or directories. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. By modifying the security permissions, adversaries seek to evade detection and impede access to their component files. Such actions indicate a deliberate effort to maintain control over compromised systems and hinder investigation or remediation efforts. Detecting these security permission changes can serve as a valuable indicator of an ongoing attack and enable timely response to mitigate the impact of the adversary's activities.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. It is possible some administrative scripts use ICacls. Filter as needed.
action.escu.creation_date = 2023-06-06
action.escu.modification_date = 2023-06-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Files and Dirs Access Rights Modification Via Icacls - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Amadey"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ with access right modification argument executed by $user$ to change security permission of a specific file or directory on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Files and Dirs Access Rights Modification Via Icacls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Amadey"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1222.001", "T1222"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic aims to identify potential adversaries who manipulate the security permissions of specific files or directories. This technique is frequently observed in the tradecraft of Advanced Persistent Threats (APTs) and coinminer scripts. By modifying the security permissions, adversaries seek to evade detection and impede access to their component files. Such actions indicate a deliberate effort to maintain control over compromised systems and hinder investigation or remediation efforts. Detecting these security permission changes can serve as a valuable indicator of an ongoing attack and enable timely response to mitigate the impact of the adversary's activities.
action.notable.param.rule_title = Windows Files and Dirs Access Rights Modification Via Icacls
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe","xcacls.exe") AND Processes.process IN ("*:R*", "*:W*", "*:F*", "*:C*",, "*:N*","*/P*", "*/E*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_files_and_dirs_access_rights_modification_via_icacls_filter`

[ESCU - Windows Find Domain Organizational Units with GetDomainOU - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Get-DomainOU` commandlet. `Get-DomainOU` is a component of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Identifying the use of `Get-DomainOU` is crucial as adversaries and Red Teams might employ it to gain insights into organizational units within Active Directory, potentially aiding in lateral movement or privilege escalation strategies.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Get-DomainOU` commandlet. `Get-DomainOU` is a component of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Identifying the use of `Get-DomainOU` is crucial as adversaries and Red Teams might employ it to gain insights into organizational units within Active Directory, potentially aiding in lateral movement or privilege escalation strategies.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
action.escu.creation_date = 2023-08-31
action.escu.modification_date = 2023-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Find Domain Organizational Units with GetDomainOU - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-DomainOU was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Find Domain Organizational Units with GetDomainOU - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Get-DomainOU` commandlet. `Get-DomainOU` is a component of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Identifying the use of `Get-DomainOU` is crucial as adversaries and Red Teams might employ it to gain insights into organizational units within Active Directory, potentially aiding in lateral movement or privilege escalation strategies.
action.notable.param.rule_title = Windows Find Domain Organizational Units with GetDomainOU
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Get-DomainOU*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_find_domain_organizational_units_with_getdomainou_filter`

[ESCU - Windows Find Interesting ACL with FindInterestingDomainAcl - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Find-InterestingDomainAcl` commandlet. `Find-InterestingDomainAcl` is part of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Find-InterestingDomainAcl` is crucial as adversaries and Red Teams might employ it to identify unusual or misconfigured Access Control Lists (ACLs) within the domain. Such ACLs can provide attackers with insights into potential privilege escalation opportunities or weak security postures within Active Directory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Find-InterestingDomainAcl` commandlet. `Find-InterestingDomainAcl` is part of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Find-InterestingDomainAcl` is crucial as adversaries and Red Teams might employ it to identify unusual or misconfigured Access Control Lists (ACLs) within the domain. Such ACLs can provide attackers with insights into potential privilege escalation opportunities or weak security postures within Active Directory.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
action.escu.creation_date = 2023-08-31
action.escu.modification_date = 2023-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Find Interesting ACL with FindInterestingDomainAcl - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Find-InterestingDomainAcl was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Find Interesting ACL with FindInterestingDomainAcl - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Find-InterestingDomainAcl` commandlet. `Find-InterestingDomainAcl` is part of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Find-InterestingDomainAcl` is crucial as adversaries and Red Teams might employ it to identify unusual or misconfigured Access Control Lists (ACLs) within the domain. Such ACLs can provide attackers with insights into potential privilege escalation opportunities or weak security postures within Active Directory.
action.notable.param.rule_title = Windows Find Interesting ACL with FindInterestingDomainAcl
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Find-InterestingDomainAcl*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_find_interesting_acl_with_findinterestingdomainacl_filter`

[ESCU - Windows Findstr GPP Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of the findstr command employed to search for unsecured credentials Group Policy Preferences (GPP). GPP are tools that allow administrators to create domain policies with embedded credentials. These policies allow administrators to set local accounts. These group policies are stored in SYSVOL on a domain controller. This means that any domain user can view the SYSVOL share and decrypt the password (using the AES key that has been made public). While Microsoft released a patch that impedes Administrators to create unsecure credentials, existing Group Policy Preferences files with passwords are not removed from SYSVOL.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552", "T1552.006"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of the findstr command employed to search for unsecured credentials Group Policy Preferences (GPP). GPP are tools that allow administrators to create domain policies with embedded credentials. These policies allow administrators to set local accounts. These group policies are stored in SYSVOL on a domain controller. This means that any domain user can view the SYSVOL share and decrypt the password (using the AES key that has been made public). While Microsoft released a patch that impedes Administrators to create unsecure credentials, existing Group Policy Preferences files with passwords are not removed from SYSVOL.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may leverage findstr to find passwords in GPO to validate exposure. Filter as needed.
action.escu.creation_date = 2023-03-16
action.escu.modification_date = 2023-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Findstr GPP Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = Findstr was executed to discover GPP credentials on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Findstr GPP Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1552", "T1552.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of the findstr command employed to search for unsecured credentials Group Policy Preferences (GPP). GPP are tools that allow administrators to create domain policies with embedded credentials. These policies allow administrators to set local accounts. These group policies are stored in SYSVOL on a domain controller. This means that any domain user can view the SYSVOL share and decrypt the password (using the AES key that has been made public). While Microsoft released a patch that impedes Administrators to create unsecure credentials, existing Group Policy Preferences files with passwords are not removed from SYSVOL.
action.notable.param.rule_title = Windows Findstr GPP Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=findstr.exe AND Processes.process=*sysvol* AND Processes.process=*cpassword*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_findstr_gpp_discovery_filter`

[ESCU - Windows Forest Discovery with GetForestDomain - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic utilizes PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Get-ForestDomain` commandlet. `Get-ForestDomain` is a component of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Get-ForestDomain` is essential as adversaries and Red Teams might employ it to gain insights into the forest and domain configurations of an Active Directory environment.  Such information can provide attackers with a broader understanding of the domain structure and potential avenues for lateral movement or privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic utilizes PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Get-ForestDomain` commandlet. `Get-ForestDomain` is a component of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Get-ForestDomain` is essential as adversaries and Red Teams might employ it to gain insights into the forest and domain configurations of an Active Directory environment.  Such information can provide attackers with a broader understanding of the domain structure and potential avenues for lateral movement or privilege escalation.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
action.escu.creation_date = 2023-08-31
action.escu.modification_date = 2023-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Forest Discovery with GetForestDomain - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-ForestDomain was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Forest Discovery with GetForestDomain - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic utilizes PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Get-ForestDomain` commandlet. `Get-ForestDomain` is a component of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Get-ForestDomain` is essential as adversaries and Red Teams might employ it to gain insights into the forest and domain configurations of an Active Directory environment.  Such information can provide attackers with a broader understanding of the domain structure and potential avenues for lateral movement or privilege escalation.
action.notable.param.rule_title = Windows Forest Discovery with GetForestDomain
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Get-ForestDomain*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_forest_discovery_with_getforestdomain_filter`

[ESCU - Windows Gather Victim Host Information Camera - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a powershell script that enumerate camera mounted to the targeted host. This technique was seen in DCRat malware, where it runs a powershell command to look for camera information that will be pass on to its C2 server. This anomaly detection can be a good pivot to check who and why this enumeration is needed and what parent process execute this powershell script command.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1592.001", "T1592"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects a powershell script that enumerate camera mounted to the targeted host. This technique was seen in DCRat malware, where it runs a powershell command to look for camera information that will be pass on to its C2 server. This anomaly detection can be a good pivot to check who and why this enumeration is needed and what parent process execute this powershell script command.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Administrators may execute this powershell command to get hardware information related to camera.
action.escu.creation_date = 2022-07-28
action.escu.modification_date = 2022-07-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Gather Victim Host Information Camera - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["DarkCrystal RAT"]
action.risk = 1
action.risk.param._risk_message = powershell script $ScriptBlockText$ to enumerate camera in $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Gather Victim Host Information Camera - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1592.001", "T1592"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText= "* Win32_PnPEntity *" ScriptBlockText= "*SELECT*" ScriptBlockText= "*WHERE*" ScriptBlockText = "*PNPClass*" ScriptBlockText IN ("*Image*", "*Camera*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_host_information_camera_filter`

[ESCU - Windows Gather Victim Identity SAM Info - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process that loads the samlib.dll module. This module is being abused by adversaries, threat actors and red teamers to access information of SAM objects or access credentials information in DC. This hunting query can be a good indicator that a process is capable of accessing the SAM object.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1589.001", "T1589"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a process that loads the samlib.dll module. This module is being abused by adversaries, threat actors and red teamers to access information of SAM objects or access credentials information in DC. This hunting query can be a good indicator that a process is capable of accessing the SAM object.
action.escu.how_to_implement = The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
action.escu.known_false_positives = this module can be loaded by a third party application. Filter is needed.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Gather Victim Identity SAM Info - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Gather Victim Identity SAM Info - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1589.001", "T1589"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7  (ImageLoaded = "*\\samlib.dll" AND OriginalFileName = "samlib.dll") OR (ImageLoaded = "*\\samcli.dll" AND OriginalFileName = "SAMCLI.DLL") AND NOT (Image IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_identity_sam_info_filter`

[ESCU - Windows Gather Victim Network Info Through Ip Check Web Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies process that attempts to connect to a known IP web services. This technique is commonly used by trickbot and other malware to perform reconnaissance against the infected machine and look for its IP address.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1590.005", "T1590"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies process that attempts to connect to a known IP web services. This technique is commonly used by trickbot and other malware to perform reconnaissance against the infected machine and look for its IP address.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA.
action.escu.known_false_positives = Filter internet browser application to minimize the false positive of this detection.
action.escu.creation_date = 2022-06-21
action.escu.modification_date = 2022-06-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Gather Victim Network Info Through Ip Check Web Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Azorult", "DarkCrystal RAT"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Gather Victim Network Info Through Ip Check Web Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult", "DarkCrystal RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1590.005", "T1590"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22  QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org", "*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net", "*iplogger.org*", "*ip-api.com*") |  stats  min(_time) as firstTime max(_time) as lastTime count by  Image ProcessId QueryName QueryStatus QueryResults EventCode Computer | rename Computer as dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_network_info_through_ip_check_web_services_filter`

[ESCU - Windows Get-AdComputer Unconstrained Delegation Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the Get-ADComputer commandlet used with specific parameters to discover Windows endpoints with Kerberos Unconstrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the Get-ADComputer commandlet used with specific parameters to discover Windows endpoints with Kerberos Unconstrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following  analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may leverage PowerView for system management or troubleshooting.
action.escu.creation_date = 2022-03-28
action.escu.modification_date = 2022-03-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Get-AdComputer Unconstrained Delegation Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-ADComputer was identified on endpoint $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Get-AdComputer Unconstrained Delegation Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the Get-ADComputer commandlet used with specific parameters to discover Windows endpoints with Kerberos Unconstrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Windows Get-AdComputer Unconstrained Delegation Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `powershell` EventCode=4104 (Message = "*Get-ADComputer*" AND Message = "*TrustedForDelegation*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_get_adcomputer_unconstrained_delegation_discovery_filter`

[ESCU - Windows Get Local Admin with FindLocalAdminAccess - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Find-LocalAdminAccess` commandlet. `Find-LocalAdminAccess` is part of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Find-LocalAdminAccess` is vital as adversaries and Red Teams might employ it to identify machines where the current user context has local administrator access. Such information can provide attackers with potential targets for lateral movement or privilege escalation within the network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Find-LocalAdminAccess` commandlet. `Find-LocalAdminAccess` is part of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Find-LocalAdminAccess` is vital as adversaries and Red Teams might employ it to identify machines where the current user context has local administrator access. Such information can provide attackers with potential targets for lateral movement or privilege escalation within the network.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
action.escu.creation_date = 2023-08-31
action.escu.modification_date = 2023-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Get Local Admin with FindLocalAdminAccess - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Find-LocalAdminAccess was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Get Local Admin with FindLocalAdminAccess - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087", "T1087.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic leverages PowerShell Script Block Logging (EventCode=4104) to detect the execution of the `Find-LocalAdminAccess` commandlet. `Find-LocalAdminAccess` is part of PowerView, a PowerShell toolkit designed for Windows domain enumeration. Detecting the use of `Find-LocalAdminAccess` is vital as adversaries and Red Teams might employ it to identify machines where the current user context has local administrator access. Such information can provide attackers with potential targets for lateral movement or privilege escalation within the network.
action.notable.param.rule_title = Windows Get Local Admin with FindLocalAdminAccess
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*Find-LocalAdminAccess*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_get_local_admin_with_findlocaladminaccess_filter`

[ESCU - Windows Group Policy Object Created - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event IDs 5136 and 51137 to identify the creation of a new Group Policy Object. With GPOs, system administrators can manage and configure applications, software operations, and user settings throughout an entire organization. GPOs can be abused and leveraged by adversaries to escalate privileges or deploy malware across an Active Directory network. As an example, the Lockbit ransomware malware will create new group policies on the domain controller that are then pushed out to every device on the network. Security teams should monitor the creation of new Group Policy Objects.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1484", "T1484.001", "T1078.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event IDs 5136 and 51137 to identify the creation of a new Group Policy Object. With GPOs, system administrators can manage and configure applications, software operations, and user settings throughout an entire organization. GPOs can be abused and leveraged by adversaries to escalate privileges or deploy malware across an Active Directory network. As an example, the Lockbit ransomware malware will create new group policies on the domain controller that are then pushed out to every device on the network. Security teams should monitor the creation of new Group Policy Objects.
action.escu.how_to_implement = To successfully implement this search, the Advanced Security Audit policy setting `Audit Directory Service Changes` within `DS Access` needs to be enabled. Furthermore, the appropriate system access control lists (SACL) need to be created as the used events are not logged by default. A good guide to accomplish this can be found here https://jgspiers.com/audit-group-policy-changes/.
action.escu.known_false_positives = Group Policy Objects are created as part of regular administrative operations, filter as needed.
action.escu.creation_date = 2023-03-27
action.escu.modification_date = 2023-03-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Group Policy Object Created - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = A new group policy objected was created by $User$
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Group Policy Object Created - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1484", "T1484.001", "T1078.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event IDs 5136 and 51137 to identify the creation of a new Group Policy Object. With GPOs, system administrators can manage and configure applications, software operations, and user settings throughout an entire organization. GPOs can be abused and leveraged by adversaries to escalate privileges or deploy malware across an Active Directory network. As an example, the Lockbit ransomware malware will create new group policies on the domain controller that are then pushed out to every device on the network. Security teams should monitor the creation of new Group Policy Objects.
action.notable.param.rule_title = Windows Group Policy Object Created
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=5137 OR (EventCode=5136 AttributeValue!="New Group Policy Object" AND (AttributeLDAPDisplayName=displayName OR AttributeLDAPDisplayName=gPCFileSysPath) ) ObjectClass=groupPolicyContainer | stats values(AttributeValue) as details values(SubjectUserSid) as User values(ObjectDN) as ObjectDN by ObjectGUID Computer | eval GPO_Name = mvindex(details, 0) | eval GPO_Path = mvindex(details, 1) | fields - details | `windows_group_policy_object_created_filter`

[ESCU - Windows Hidden Schedule Task Settings - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects creation of hidden scheculed tasks such that it this task is not visible on the UI. Such behavior is indicative of certain malware, such as Industroyer2, or attacks leveraging living-off-the-land binaries (LOLBINs) to download additional payloads to a compromised machine. This analytic relies on the Windows Security EventCode 4698, indicating the creation of a scheduled task. The search focuses on identifying instances where the 'Hidden' setting is enabled, signaling potential nefarious activity. To implement this search, you need to ingest logs with task scheduling details from your endpoints. As false positives are currently unknown, it is advised to tune and filter based on the known use of task scheduling in your environment. This analytic provides crucial visibility into stealthy, potentially harmful scheduled tasks on Windows systems.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects creation of hidden scheculed tasks such that it this task is not visible on the UI. Such behavior is indicative of certain malware, such as Industroyer2, or attacks leveraging living-off-the-land binaries (LOLBINs) to download additional payloads to a compromised machine. This analytic relies on the Windows Security EventCode 4698, indicating the creation of a scheduled task. The search focuses on identifying instances where the 'Hidden' setting is enabled, signaling potential nefarious activity. To implement this search, you need to ingest logs with task scheduling details from your endpoints. As false positives are currently unknown, it is advised to tune and filter based on the known use of task scheduling in your environment. This analytic provides crucial visibility into stealthy, potentially harmful scheduled tasks on Windows systems.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Hidden Schedule Task Settings - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["CISA AA22-257A", "Active Directory Discovery", "Industroyer2", "Data Destruction", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A schedule task with hidden setting enable in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Hidden Schedule Task Settings - Rule
action.correlationsearch.annotations = {"analytic_story": ["CISA AA22-257A", "Active Directory Discovery", "Industroyer2", "Data Destruction", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects creation of hidden scheculed tasks such that it this task is not visible on the UI. Such behavior is indicative of certain malware, such as Industroyer2, or attacks leveraging living-off-the-land binaries (LOLBINs) to download additional payloads to a compromised machine. This analytic relies on the Windows Security EventCode 4698, indicating the creation of a scheduled task. The search focuses on identifying instances where the 'Hidden' setting is enabled, signaling potential nefarious activity. To implement this search, you need to ingest logs with task scheduling details from your endpoints. As false positives are currently unknown, it is advised to tune and filter based on the known use of task scheduling in your environment. This analytic provides crucial visibility into stealthy, potentially harmful scheduled tasks on Windows systems.
action.notable.param.rule_title = Windows Hidden Schedule Task Settings
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Hidden = true | stats count min(_time) as firstTime max(_time) as lastTime by  Task_Name, Command, Author, Hidden, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hidden_schedule_task_settings_filter`

[ESCU - Windows Hide Notification Features Through Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious registry modification to hide common windows notification feature from compromised host. This technique was seen in some ransomware family to add more impact to its payload that are visually seen by user aside from the encrypted files and ransomware notes. Even this a good anomaly detection, administrator may implement this changes for auditing or security reason. In this scenario filter is needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious registry modification to hide common windows notification feature from compromised host. This technique was seen in some ransomware family to add more impact to its payload that are visually seen by user aside from the encrypted files and ransomware notes. Even this a good anomaly detection, administrator may implement this changes for auditing or security reason. In this scenario filter is needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Hide Notification Features Through Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Registry modification to hide windows notification on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Hide Notification Features Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" Registry.registry_value_name IN ("HideClock", "HideSCAHealth", "HideSCANetwork", "HideSCAPower", "HideSCAVolume") Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hide_notification_features_through_registry_filter`

[ESCU - Windows High File Deletion Frequency - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1485"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = user may delete bunch of pictures or files in a folder.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows High File Deletion Frequency - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Clop Ransomware", "DarkCrystal RAT", "Swift Slicer", "Data Destruction", "WhisperGate", "Sandworm Tools"]
action.risk = 1
action.risk.param._risk_message = High frequency file deletion activity detected on host $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "deleted_files", "risk_object_type": "other", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows High File Deletion Frequency - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware", "DarkCrystal RAT", "Swift Slicer", "Data Destruction", "WhisperGate", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 80, "impact": 90, "mitre_attack": ["T1485"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=23 TargetFilename IN ("*.cmd", "*.ini","*.gif", "*.jpg", "*.jpeg", "*.db", "*.ps1", "*.doc*", "*.xls*", "*.ppt*", "*.bmp","*.zip", "*.rar", "*.7z", "*.chm", "*.png", "*.log", "*.vbs", "*.js", "*.vhd", "*.bak", "*.wbcat", "*.bkf" , "*.backup*", "*.dsk", "*.win") | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by dest user EventCode Image ProcessID |where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_high_file_deletion_frequency_filter`

[ESCU - Windows Hijack Execution Flow Version Dll Side Load - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a process loading version.dll that is not in %windir%\\system32 or %windir%\\syswow64 dir path. This event is seen in ransomware and APT malware that executes malicious version.dll placed in the same folder of onedrive application that will execute that module. This technique is known to be DLL side loading. This technique was used to execute an agent of Brute Ratel C4 red teaming tools to serve as remote admin tool to collect and compromise target host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.001", "T1574"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to detect a process loading version.dll that is not in %windir%\\system32 or %windir%\\syswow64 dir path. This event is seen in ransomware and APT malware that executes malicious version.dll placed in the same folder of onedrive application that will execute that module. This technique is known to be DLL side loading. This technique was used to execute an agent of Brute Ratel C4 red teaming tools to serve as remote admin tool to collect and compromise target host.
action.escu.how_to_implement = The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Hijack Execution Flow Version Dll Side Load - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
action.risk = 1
action.risk.param._risk_message = a process $Image$ loading $ImageLoaded$ as a side load dll in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Hijack Execution Flow Version Dll Side Load - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1574.001", "T1574"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 ImageLoaded = "*\\version.dll" AND (Signed = "false" OR NOT(ImageLoaded IN("*\\windows\\system32*",  "*\\windows\\syswow64\\*"))) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hijack_execution_flow_version_dll_side_load_filter`

[ESCU - Windows Hunting System Account Targeting Lsass - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies all processes requesting access into Lsass.exe. his behavior may be related to credential dumping or applications requiring access to credentials. Triaging this event will require understanding the GrantedAccess from the SourceImage. In addition, whether the account is privileged or not. Review the process requesting permissions and review parallel processes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic identifies all processes requesting access into Lsass.exe. his behavior may be related to credential dumping or applications requiring access to credentials. Triaging this event will require understanding the GrantedAccess from the SourceImage. In addition, whether the account is privileged or not. Review the process requesting permissions and review parallel processes.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
action.escu.known_false_positives = False positives will occur based on GrantedAccess and SourceUser, filter based on source image as needed. Utilize this hunting analytic to tune out false positives in TTP or anomaly analytics.
action.escu.creation_date = 2022-01-12
action.escu.modification_date = 2022-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Hunting System Account Targeting Lsass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Hunting System Account Targeting Lsass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetImage, GrantedAccess, SourceImage, SourceProcessId, SourceUser, TargetUser  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hunting_system_account_targeting_lsass_filter`

[ESCU - Windows Identify Protocol Handlers - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic will identify any protocol handlers utilized on the command-line. A protocol handler is an application that knows how to handle particular types of links: for example, a mail client is a protocol handler for "mailto:" links. When the user clicks a "mailto:" link, the browser opens the application selected as the handler for the "mailto:" protocol (or offers them a choice of handlers, depending on their settings). To identify protocol handlers we can use NirSoft https://www.nirsoft.net/utils/url_protocol_view.html URLProtocolView or query the registry using PowerShell.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic will identify any protocol handlers utilized on the command-line. A protocol handler is an application that knows how to handle particular types of links: for example, a mail client is a protocol handler for "mailto:" links. When the user clicks a "mailto:" link, the browser opens the application selected as the handler for the "mailto:" protocol (or offers them a choice of handlers, depending on their settings). To identify protocol handlers we can use NirSoft https://www.nirsoft.net/utils/url_protocol_view.html URLProtocolView or query the registry using PowerShell.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be found. https and http is a URL Protocol handler that will trigger this analytic. Tune based on process or command-line.
action.escu.creation_date = 2022-09-13
action.escu.modification_date = 2022-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Identify Protocol Handlers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Identify Protocol Handlers - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 20, "impact": 30, "mitre_attack": ["T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes  by Processes.dest Processes.parent_process_name Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup windows_protocol_handlers handler AS process OUTPUT handler ishandler | where ishandler="TRUE" | `windows_identify_protocol_handlers_filter`

[ESCU - Windows IIS Components Add New Module - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the process AppCmd.exe installing a new module into IIS. AppCmd is a utility to manage IIS web sites and App Pools. An adversary may run this command to install a webshell or backdoor. This has been found to be used for credit card scraping, persistence, and further post-exploitation. An administrator may run this to install new modules for a web site or during IIS updates.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the process AppCmd.exe installing a new module into IIS. AppCmd is a utility to manage IIS web sites and App Pools. An adversary may run this command to install a webshell or backdoor. This has been found to be used for credit card scraping, persistence, and further post-exploitation. An administrator may run this to install new modules for a web site or during IIS updates.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present until properly tuned. Filter as needed.
action.escu.creation_date = 2022-12-19
action.escu.modification_date = 2022-12-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows IIS Components Add New Module - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IIS Components"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to install a new IIS module.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows IIS Components Add New Module - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name  IN ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process IN ("*install *", "*module *") AND Processes.process="*image*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_add_new_module_filter`

[ESCU - Windows IIS Components Get-WebGlobalModule Module Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic requires the use of PowerShell inputs to run Get-WebGlobalModule to list out all the IIS Modules installed. The output is a list of Module names and the Image path of the DLL.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505.004", "T1505"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic requires the use of PowerShell inputs to run Get-WebGlobalModule to list out all the IIS Modules installed. The output is a list of Module names and the Image path of the DLL.
action.escu.how_to_implement = You must ingest the PwSh cmdlet Get-WebGlobalModule in order to utilize this analytic. Follow https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040
action.escu.known_false_positives = This analytic is meant to assist with hunting modules across a fleet of IIS servers. Filter and modify as needed.
action.escu.creation_date = 2022-12-20
action.escu.modification_date = 2022-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows IIS Components Get-WebGlobalModule Module Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IIS Components", "WS FTP Server Critical Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows IIS Components Get-WebGlobalModule Module Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components", "WS FTP Server Critical Vulnerabilities"], "cis20": ["CIS 10"], "confidence": 10, "impact": 10, "mitre_attack": ["T1505.004", "T1505"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `iis_get_webglobalmodule` | stats count min(_time) as firstTime max(_time) as lastTime by host name image | rename host as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_get_webglobalmodule_module_query_filter`

[ESCU - Windows IIS Components Module Failed to Load - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes EventCode 2282 which generates when a Module DLL could not be loaded due to a configuration problem. This typically occurs when a IIS module is installed but is failing to load. This typically results in thousands of events until the issue is resolved. Review the module that is failing and determine if it is legitimate or not.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes EventCode 2282 which generates when a Module DLL could not be loaded due to a configuration problem. This typically occurs when a IIS module is installed but is failing to load. This typically results in thousands of events until the issue is resolved. Review the module that is failing and determine if it is legitimate or not.
action.escu.how_to_implement = IIS must be installed and Application event logs must be collected in order to utilize this analytic.
action.escu.known_false_positives = False positives will be present until all module failures are resolved or reviewed.
action.escu.creation_date = 2022-12-20
action.escu.modification_date = 2022-12-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows IIS Components Module Failed to Load - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IIS Components"]
action.risk = 1
action.risk.param._risk_message = A new IIS Module has been loaded and should be reviewed on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows IIS Components Module Failed to Load - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_application` EventCode=2282 | stats  count min(_time) as firstTime max(_time) as lastTime by  EventCode dest Name ModuleDll | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_module_failed_to_load_filter`

[ESCU - Windows IIS Components New Module Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses the Windows Event log - Microsoft-IIS-Configuration/Operational - which must be enabled and logged on Windows IIS servers before it can be Splunked. The following analytic identifies newly installed IIS modules. Per Microsoft, IIS modules are not commonly added to a production IIS server, so alerting on this event ID should be enabled.IIS modules can be installed at a global level or at a site level. In detecting malicious IIS modules, it is important to check both the global and site level for unauthorized modules. Regular monitoring of these locations for such modules and comparing against a known good list can help detect and identify malicious IIS modules.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic uses the Windows Event log - Microsoft-IIS-Configuration/Operational - which must be enabled and logged on Windows IIS servers before it can be Splunked. The following analytic identifies newly installed IIS modules. Per Microsoft, IIS modules are not commonly added to a production IIS server, so alerting on this event ID should be enabled.IIS modules can be installed at a global level or at a site level. In detecting malicious IIS modules, it is important to check both the global and site level for unauthorized modules. Regular monitoring of these locations for such modules and comparing against a known good list can help detect and identify malicious IIS modules.
action.escu.how_to_implement = You must enabled the IIS Configuration Operational log before ingesting in Splunk. Setup and inputs may be found here https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040.
action.escu.known_false_positives = False positives may be present when updates or an administrator adds a new module to IIS. Monitor and filter as needed.
action.escu.creation_date = 2022-12-19
action.escu.modification_date = 2022-12-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows IIS Components New Module Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["IIS Components"]
action.risk = 1
action.risk.param._risk_message = A new IIS Module has been loaded and should be reviewed on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows IIS Components New Module Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic uses the Windows Event log - Microsoft-IIS-Configuration/Operational - which must be enabled and logged on Windows IIS servers before it can be Splunked. The following analytic identifies newly installed IIS modules. Per Microsoft, IIS modules are not commonly added to a production IIS server, so alerting on this event ID should be enabled.IIS modules can be installed at a global level or at a site level. In detecting malicious IIS modules, it is important to check both the global and site level for unauthorized modules. Regular monitoring of these locations for such modules and comparing against a known good list can help detect and identify malicious IIS modules.
action.notable.param.rule_title = Windows IIS Components New Module Added
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `iis_operational_logs` EventCode=29 | stats  count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | rename ComputerName AS dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_new_module_added_filter`

[ESCU - Windows Impair Defense Add Xml Applocker Rules - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a process that imports applocker xml policy using PowerShell commandlet. This technique was seen in Azorult malware where it drop an xml Applocker policy that will deny several AV products and further executed the PowerShell Applocker commandlet.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a process that imports applocker xml policy using PowerShell commandlet. This technique was seen in Azorult malware where it drop an xml Applocker policy that will deny several AV products and further executed the PowerShell Applocker commandlet.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may execute this command that may cause some false positive.
action.escu.creation_date = 2022-06-24
action.escu.modification_date = 2022-06-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Impair Defense Add Xml Applocker Rules - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Impair Defense Add Xml Applocker Rules - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` AND Processes.process="*Import-Module Applocker*" AND Processes.process="*Set-AppLockerPolicy *"  AND Processes.process="* -XMLPolicy *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_add_xml_applocker_rules_filter`

[ESCU - Windows Impair Defense Delete Win Defender Context Menu - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for the deletion of Windows Defender context menu within the registry. This is consistent behavior with RAT malware across a fleet of endpoints. This particular behavior is executed when an adversary gains access to an endpoint and begins to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for the deletion of Windows Defender context menu within the registry. This is consistent behavior with RAT malware across a fleet of endpoints. This particular behavior is executed when an adversary gains access to an endpoint and begins to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
action.escu.known_false_positives = It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
action.escu.creation_date = 2022-06-07
action.escu.modification_date = 2022-06-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Impair Defense Delete Win Defender Context Menu - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Impair Defense Delete Win Defender Context Menu - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\shellex\\ContextMenuHandlers\\EPP" Registry.action = deleted by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_context_menu_filter`

[ESCU - Windows Impair Defense Delete Win Defender Profile Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for the deletion of Windows Defender main profile within the registry. This was used by RAT malware across a fleet of endpoints. This particular behavior is typically executed when an adversary gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for the deletion of Windows Defender main profile within the registry. This was used by RAT malware across a fleet of endpoints. This particular behavior is typically executed when an adversary gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
action.escu.known_false_positives = It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
action.escu.creation_date = 2022-06-07
action.escu.modification_date = 2022-06-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Impair Defense Delete Win Defender Profile Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Windows Defender Logger registry key set to 'disabled' on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Impair Defense Delete Win Defender Profile Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender" Registry.action = deleted by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action  Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_profile_registry_filter`

[ESCU - Windows Impair Defense Deny Security Software With Applocker - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a modification in the Windows registry by the Applocker utility that contains details or registry data values related to denying the execution of several security products. This technique was seen in Azorult malware where it drops an xml Applocker policy that will deny several AV products and then loaded by using PowerShell Applocker commandlet.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a modification in the Windows registry by the Applocker utility that contains details or registry data values related to denying the execution of several security products. This technique was seen in Azorult malware where it drops an xml Applocker policy that will deny several AV products and then loaded by using PowerShell Applocker commandlet.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = False positives may be present based on organization use of Applocker. Filter as needed.
action.escu.creation_date = 2022-06-24
action.escu.modification_date = 2022-06-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Impair Defense Deny Security Software With Applocker - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = Applocker registry modification to deny the action of several AV products on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Impair Defense Deny Security Software With Applocker - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a modification in the Windows registry by the Applocker utility that contains details or registry data values related to denying the execution of several security products. This technique was seen in Azorult malware where it drops an xml Applocker policy that will deny several AV products and then loaded by using PowerShell Applocker commandlet.
action.notable.param.rule_title = Windows Impair Defense Deny Security Software With Applocker
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Group Policy Objects\\*" AND Registry.registry_path= "*}Machine\\Software\\Policies\\Microsoft\\Windows\\SrpV2*") OR Registry.registry_path="*\\Software\\Policies\\Microsoft\\Windows\\SrpV2*" AND Registry.registry_value_data = "*Action\=\"Deny\"*" AND Registry.registry_value_data IN("*O=SYMANTEC*","*O=MCAFEE*","*O=KASPERSKY*","*O=BLEEPING COMPUTER*", "*O=PANDA SECURITY*","*O=SYSTWEAK SOFTWARE*", "*O=TREND MICRO*", "*O=AVAST*", "*O=GRIDINSOFT*", "*O=MICROSOFT*", "*O=NANO SECURITY*", "*O=SUPERANTISPYWARE.COM*", "*O=DOCTOR WEB*", "*O=MALWAREBYTES*", "*O=ESET*", "*O=AVIRA*", "*O=WEBROOT*") by  Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.registry_key_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_deny_security_software_with_applocker_filter`

[ESCU - Windows Impair Defenses Disable HVCI - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic refers to a detection mechanism designed to identify when the Hypervisor-protected Code Integrity (HVCI) feature is disabled within the Windows registry. HVCI is a security feature in Windows 10 and Windows Server 2016 that helps protect the kernel and system processes from being tampered with by malicious code. HVCI relies on hardware-assisted virtualization and Microsoft's Hyper-V hypervisor to ensure that only kernel-mode code that has been signed by Microsoft or the system's hardware manufacturer can be executed. This prevents attackers from exploiting vulnerabilities to run unsigned code, like kernel-mode rootkits or other malicious software, at the kernel level. Disabling HVCI may expose the system to security risks and could be an indicator of a potential compromise or unauthorized activity. The analytic aims to detect and report events or configurations that lead to the disabling of HVCI.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic refers to a detection mechanism designed to identify when the Hypervisor-protected Code Integrity (HVCI) feature is disabled within the Windows registry. HVCI is a security feature in Windows 10 and Windows Server 2016 that helps protect the kernel and system processes from being tampered with by malicious code. HVCI relies on hardware-assisted virtualization and Microsoft's Hyper-V hypervisor to ensure that only kernel-mode code that has been signed by Microsoft or the system's hardware manufacturer can be executed. This prevents attackers from exploiting vulnerabilities to run unsigned code, like kernel-mode rootkits or other malicious software, at the kernel level. Disabling HVCI may expose the system to security risks and could be an indicator of a potential compromise or unauthorized activity. The analytic aims to detect and report events or configurations that lead to the disabling of HVCI.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives will be limited to administrative scripts disabling HVCI. Filter as needed.
action.escu.creation_date = 2023-04-13
action.escu.modification_date = 2023-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Impair Defenses Disable HVCI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackLotus Campaign", "Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = HVCI has been disabled on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Impair Defenses Disable HVCI - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackLotus Campaign", "Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic refers to a detection mechanism designed to identify when the Hypervisor-protected Code Integrity (HVCI) feature is disabled within the Windows registry. HVCI is a security feature in Windows 10 and Windows Server 2016 that helps protect the kernel and system processes from being tampered with by malicious code. HVCI relies on hardware-assisted virtualization and Microsoft's Hyper-V hypervisor to ensure that only kernel-mode code that has been signed by Microsoft or the system's hardware manufacturer can be executed. This prevents attackers from exploiting vulnerabilities to run unsigned code, like kernel-mode rootkits or other malicious software, at the kernel level. Disabling HVCI may expose the system to security risks and could be an indicator of a potential compromise or unauthorized activity. The analytic aims to detect and report events or configurations that lead to the disabling of HVCI.
action.notable.param.rule_title = Windows Impair Defenses Disable HVCI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\CurrentControlSet\\Control\\DeviceGuard\\Scenarios\\HypervisorEnforcedCodeIntegrity\\Enabled" Registry.registry_value_data="0x00000000"  by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action  Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_hvci_filter`

[ESCU - Windows Impair Defenses Disable Win Defender Auto Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for the Registry Key DefenderApiLogger or DefenderAuditLogger set to disable. This is consistent with RAT malware across a fleet of endpoints. This particular behavior is typically executed when an adversary gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for the Registry Key DefenderApiLogger or DefenderAuditLogger set to disable. This is consistent with RAT malware across a fleet of endpoints. This particular behavior is typically executed when an adversary gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
action.escu.known_false_positives = It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
action.escu.creation_date = 2022-06-07
action.escu.modification_date = 2022-06-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Impair Defenses Disable Win Defender Auto Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Windows Defender Logger registry key set to 'disabled' on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Impair Defenses Disable Win Defender Auto Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path = "*WMI\\Autologger\\DefenderApiLogger\\Start" OR Registry.registry_path = "*WMI\\Autologger\\DefenderAuditLogger\\Start")  Registry.registry_value_data ="0x00000000" by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_win_defender_auto_logging_filter`

[ESCU - Windows Indirect Command Execution Via forfiles - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects programs that have been started by forfiles.exe. According to Microsoft, the 'The forfiles command lets you run a command on or pass arguments to multiple files'. While this tool can be used to start legitimate programs, usually within the context of a batch script, it has been observed being used to evade protections on command line execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1202"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects programs that have been started by forfiles.exe. According to Microsoft, the 'The forfiles command lets you run a command on or pass arguments to multiple files'. While this tool can be used to start legitimate programs, usually within the context of a batch script, it has been observed being used to evade protections on command line execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legacy applications may be run using pcalua.exe. Similarly, forfiles.exe may be used in legitimate batch scripts.  Filter these results as needed.
action.escu.creation_date = 2022-04-05
action.escu.modification_date = 2022-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Indirect Command Execution Via forfiles - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land", "Windows Post-Exploitation"]
action.risk = 1
action.risk.param._risk_message = The Program Compatability Assistant (pcalua.exe) launched the process $process_name$
action.risk.param._risk = [{"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Indirect Command Execution Via forfiles - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land", "Windows Post-Exploitation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1202"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects programs that have been started by forfiles.exe. According to Microsoft, the 'The forfiles command lets you run a command on or pass arguments to multiple files'. While this tool can be used to start legitimate programs, usually within the context of a batch script, it has been observed being used to evade protections on command line execution.
action.notable.param.rule_title = Windows Indirect Command Execution Via forfiles
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*forfiles* /c *" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_forfiles_filter`

[ESCU - Windows Indirect Command Execution Via pcalua - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects programs that have been started by pcalua.exe. pcalua.exe is the Microsoft Windows Program Compatability Assistant.  While this tool can be used to start legitimate programs, it has been observed being used to evade protections on command line execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1202"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects programs that have been started by pcalua.exe. pcalua.exe is the Microsoft Windows Program Compatability Assistant.  While this tool can be used to start legitimate programs, it has been observed being used to evade protections on command line execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some legacy applications may be run using pcalua.exe.  Filter these results as needed.
action.escu.creation_date = 2022-04-05
action.escu.modification_date = 2022-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Indirect Command Execution Via pcalua - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = The Program Compatability Assistant (pcalua.exe) launched the process $process_name$
action.risk.param._risk = [{"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Indirect Command Execution Via pcalua - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1202"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects programs that have been started by pcalua.exe. pcalua.exe is the Microsoft Windows Program Compatability Assistant.  While this tool can be used to start legitimate programs, it has been observed being used to evade protections on command line execution.
action.notable.param.rule_title = Windows Indirect Command Execution Via pcalua
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*pcalua* -a*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_pcalua_filter`

[ESCU - Windows Indirect Command Execution Via Series Of Forfiles - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect suspicious excessive usage of forfiles.exe process. This event was seen in post exploitation tool WINPEAS that was used by Ransomware Prestige. Forfiles command lets you run a command on or pass arguments to multiple files. This Windows OS built-in tool being abused to list all files in specific directory or drive.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1202"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect suspicious excessive usage of forfiles.exe process. This event was seen in post exploitation tool WINPEAS that was used by Ransomware Prestige. Forfiles command lets you run a command on or pass arguments to multiple files. This Windows OS built-in tool being abused to list all files in specific directory or drive.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Indirect Command Execution Via Series Of Forfiles - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = excessive forfiles process execution in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Indirect Command Execution Via Series Of Forfiles - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1202"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_guid) as process_guid  values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "forfiles.exe" OR Processes.original_file_name = "forfiles.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m | where count >=20 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_series_of_forfiles_filter`

[ESCU - Windows Information Discovery Fsutil - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of Windows OS built-in tool FSUTIL to discover file system information. This tool is being abused or used by several adversaries or threat actor to query/list all drives, drive type, volume information or volume statistics by using the FSINFO parameter of this tool. This technique was seen in WINPEAS post exploitation tool that is being used by ransomware prestige to gain privilege and persistence to the targeted host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1082"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of Windows OS built-in tool FSUTIL to discover file system information. This tool is being abused or used by several adversaries or threat actor to query/list all drives, drive type, volume information or volume statistics by using the FSINFO parameter of this tool. This technique was seen in WINPEAS post exploitation tool that is being used by ransomware prestige to gain privilege and persistence to the targeted host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Information Discovery Fsutil - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ with commandline $process$ is executed in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Information Discovery Fsutil - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1082"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="fsutil.exe" OR Processes.original_file_name = "fsutil.exe" AND Processes.process = "*fsinfo*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_information_discovery_fsutil_filter`

[ESCU - Windows Ingress Tool Transfer Using Explorer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows Explorer process with a URL within the command-line. Explorer.exe is known Windows process that handles start menu, taskbar, desktop and file manager. Many adversaries abuse this process, like DCRat malware, where it attempts to open the URL with the default browser application on the target host by putting the URL as a parameter on explorer.exe process. This anomaly detection might be a good pivot to check which user and how this process was executed, what is the parent process and what is the URL link. This technique is not commonly used to open an URL.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Windows Explorer process with a URL within the command-line. Explorer.exe is known Windows process that handles start menu, taskbar, desktop and file manager. Many adversaries abuse this process, like DCRat malware, where it attempts to open the URL with the default browser application on the target host by putting the URL as a parameter on explorer.exe process. This anomaly detection might be a good pivot to check which user and how this process was executed, what is the parent process and what is the URL link. This technique is not commonly used to open an URL.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present based on legitimate applications or third party utilities. Filter out any additional parent process names.
action.escu.creation_date = 2022-08-30
action.escu.modification_date = 2022-08-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Ingress Tool Transfer Using Explorer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a remote payload.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Ingress Tool Transfer Using Explorer - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1105"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = explorer.exe OR Processes.original_file_name = explorer.exe) AND NOT (Processes.parent_process_name IN("userinit.exe", "svchost.exe")) Processes.process IN ("* http://*", "* https://*") by  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ingress_tool_transfer_using_explorer_filter`

[ESCU - Windows Input Capture Using Credential UI Dll - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process that loads the credui.dll module. This legitimate module is typically abused by adversaries, threat actors and red teamers to create a credential UI prompt dialog box to lure users for possible credential theft or can be used to dump the credentials of a targeted host. This hunting query is a good pivot to check why the process loaded this dll and if it is a legitimate file. This hunting query may hit false positive for a third party application that uses a credential login UI for user login.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1056.002", "T1056"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a process that loads the credui.dll module. This legitimate module is typically abused by adversaries, threat actors and red teamers to create a credential UI prompt dialog box to lure users for possible credential theft or can be used to dump the credentials of a targeted host. This hunting query is a good pivot to check why the process loaded this dll and if it is a legitimate file. This hunting query may hit false positive for a third party application that uses a credential login UI for user login.
action.escu.how_to_implement = The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
action.escu.known_false_positives = this module can be loaded by a third party application. Filter is needed.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Input Capture Using Credential UI Dll - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Input Capture Using Credential UI Dll - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1056.002", "T1056"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7  (ImageLoaded = "*\\credui.dll" AND OriginalFileName = "credui.dll") OR (ImageLoaded = "*\\wincredui.dll" AND OriginalFileName = "wincredui.dll") AND NOT(Image IN("*\\windows\\explorer.exe", "*\\windows\\system32\\*", "*\\windows\\sysWow64\\*", "*:\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName dest EventCode Signed ProcessId ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_input_capture_using_credential_ui_dll_filter`

[ESCU - Windows InstallUtil Credential Theft - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows InstallUtil.exe binary loading `vaultcli.dll` and Samlib.dll`. This technique may be used to execute code to bypassing application control and capture credentials by utilizing a tool like MimiKatz. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the Windows InstallUtil.exe binary loading `vaultcli.dll` and Samlib.dll`. This technique may be used to execute code to bypassing application control and capture credentials by utilizing a tool like MimiKatz. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and module loads from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Typically this will not trigger as by it's very nature InstallUtil does not need credentials. Filter as needed.
action.escu.creation_date = 2022-08-25
action.escu.modification_date = 2022-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows InstallUtil Credential Theft - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $ImageLoaded$ was identified on endpoint $dest$ loading samlib.dll and vaultcli.dll to potentially capture credentials in memory.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows InstallUtil Credential Theft - Rule
action.correlationsearch.annotations = {"analytic_story": ["Signed Binary Proxy Execution InstallUtil"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows InstallUtil.exe binary loading `vaultcli.dll` and Samlib.dll`. This technique may be used to execute code to bypassing application control and capture credentials by utilizing a tool like MimiKatz. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.notable.param.rule_title = Windows InstallUtil Credential Theft
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7  parent_process_name=installutil.exe ImageLoaded IN ("*\\samlib.dll", "*\\vaultcli.dll") | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, ImageLoaded, OriginalFileName, ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_credential_theft_filter`

[ESCU - Windows InstallUtil in Non Standard Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows binary InstallUtil.exe running from a non-standard location. The analytic utilizes a macro for InstallUtil and identifies both the process_name and original_file_name.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1036", "T1036.003", "T1218", "T1218.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Windows binary InstallUtil.exe running from a non-standard location. The analytic utilizes a macro for InstallUtil and identifies both the process_name and original_file_name.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering may be required. Certain utilities will run from non-standard paths based on the third-party application in use.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows InstallUtil in Non Standard Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Masquerading - Rename System Utilities", "Ransomware", "Unusual Processes", "Signed Binary Proxy Execution InstallUtil", "Living Off The Land", "Data Destruction", "WhisperGate"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ from a non-standard path was identified on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows InstallUtil in Non Standard Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Masquerading - Rename System Utilities", "Ransomware", "Unusual Processes", "Signed Binary Proxy Execution InstallUtil", "Living Off The Land", "Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1036", "T1036.003", "T1218", "T1218.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows binary InstallUtil.exe running from a non-standard location. The analytic utilizes a macro for InstallUtil and identifies both the process_name and original_file_name.
action.notable.param.rule_title = Windows InstallUtil in Non Standard Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where `process_installutil` NOT (Processes.process_path IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*", "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_in_non_standard_path_filter`

[ESCU - Windows InstallUtil Remote Network Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows InstallUtil.exe binary making a remote network connection. This technique may be used to download and execute code while bypassing application control. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies the Windows InstallUtil.exe binary making a remote network connection. This technique may be used to download and execute code while bypassing application control. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements.
action.escu.creation_date = 2022-03-16
action.escu.modification_date = 2022-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows InstallUtil Remote Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ generating a remote download.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows InstallUtil Remote Network Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows InstallUtil.exe binary making a remote network connection. This technique may be used to download and execute code while bypassing application control. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.notable.param.rule_title = Windows InstallUtil Remote Network Connection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_installutil` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 | `windows_installutil_remote_network_connection_filter`

[ESCU - Windows InstallUtil Uninstall Option - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows InstallUtil.exe binary. This will execute code while bypassing application control using the `/u` (uninstall) switch. \
InstallUtil uses the functions install and uninstall within the System.Configuration.Install namespace to process .net assembly. Install function requires admin privileges, however, uninstall function can be run as an unprivileged user.\
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Windows InstallUtil.exe binary. This will execute code while bypassing application control using the `/u` (uninstall) switch. \
InstallUtil uses the functions install and uninstall within the System.Configuration.Install namespace to process .net assembly. Install function requires admin privileges, however, uninstall function can be run as an unprivileged user.\
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present. Filter as needed by parent process or application.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows InstallUtil Uninstall Option - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing an uninstall.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows InstallUtil Uninstall Option - Rule
action.correlationsearch.annotations = {"analytic_story": ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows InstallUtil.exe binary. This will execute code while bypassing application control using the `/u` (uninstall) switch. \
InstallUtil uses the functions install and uninstall within the System.Configuration.Install namespace to process .net assembly. Install function requires admin privileges, however, uninstall function can be run as an unprivileged user.\
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.notable.param.rule_title = Windows InstallUtil Uninstall Option
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*/u*", "*uninstall*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_uninstall_option_filter`

[ESCU - Windows InstallUtil Uninstall Option with Network - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows InstallUtil.exe binary making a remote network connection. This technique may be used to download and execute code while bypassing application control using the `/u` (uninstall) switch. \
InstallUtil uses the functions install and uninstall within the System.Configuration.Install namespace to process .net assembly. Install function requires admin privileges, however, uninstall function can be run as an unprivileged user.\
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies the Windows InstallUtil.exe binary making a remote network connection. This technique may be used to download and execute code while bypassing application control using the `/u` (uninstall) switch. \
InstallUtil uses the functions install and uninstall within the System.Configuration.Install namespace to process .net assembly. Install function requires admin privileges, however, uninstall function can be run as an unprivileged user.\
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements.
action.escu.creation_date = 2022-03-16
action.escu.modification_date = 2022-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows InstallUtil Uninstall Option with Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing an uninstall.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows InstallUtil Uninstall Option with Network - Rule
action.correlationsearch.annotations = {"analytic_story": ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows InstallUtil.exe binary making a remote network connection. This technique may be used to download and execute code while bypassing application control using the `/u` (uninstall) switch. \
InstallUtil uses the functions install and uninstall within the System.Configuration.Install namespace to process .net assembly. Install function requires admin privileges, however, uninstall function can be run as an unprivileged user.\
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.notable.param.rule_title = Windows InstallUtil Uninstall Option with Network
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*/u*", "*uninstall*") by _time span=1h  Processes.user Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 | `windows_installutil_uninstall_option_with_network_filter`

[ESCU - Windows InstallUtil URL in Command Line - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows InstallUtil.exe binary passing a HTTP request on the command-line. This technique may be used to download and execute code while bypassing application control. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Windows InstallUtil.exe binary passing a HTTP request on the command-line. This technique may be used to download and execute code while bypassing application control. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements.
action.escu.creation_date = 2021-11-12
action.escu.modification_date = 2021-11-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows InstallUtil URL in Command Line - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ passing a URL on the command-line.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows InstallUtil URL in Command Line - Rule
action.correlationsearch.annotations = {"analytic_story": ["Signed Binary Proxy Execution InstallUtil", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1218.004", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows InstallUtil.exe binary passing a HTTP request on the command-line. This technique may be used to download and execute code while bypassing application control. \
When `InstallUtil.exe` is used in a malicous manner, the path to an executable on the filesystem is typically specified. Take note of the parent process. In a suspicious instance, this will be spawned from a non-standard process like `Cmd.exe`, `PowerShell.exe` or `Explorer.exe`. \
If used by a developer, typically this will be found with multiple command-line switches/arguments and spawn from Visual Studio. \
During triage review resulting network connections, file modifications, and parallel processes. Capture any artifacts and review further.
action.notable.param.rule_title = Windows InstallUtil URL in Command Line
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*http://*","*https://*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_url_in_command_line_filter`

[ESCU - Windows ISO LNK File Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of a delivered ISO file that has been mounted and the afformention lnk or file opened within it. When the ISO file is opened, the files are saved in the %USER%\AppData\Local\Temp\<random folder name>\ path. The analytic identifies .iso.lnk written to the path. The name of the ISO file is prepended.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566", "T1204.001", "T1204"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of a delivered ISO file that has been mounted and the afformention lnk or file opened within it. When the ISO file is opened, the files are saved in the %USER%\AppData\Local\Temp\<random folder name>\ path. The analytic identifies .iso.lnk written to the path. The name of the ISO file is prepended.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be high depending on the environment and consistent use of ISOs mounting. Restrict to servers, or filter out based on commonly used ISO names. Filter as needed.
action.escu.creation_date = 2022-09-19
action.escu.modification_date = 2022-09-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows ISO LNK File Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Brute Ratel C4", "AgentTesla", "Qakbot", "IcedID", "Azorult", "Remcos", "Warzone RAT"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows ISO LNK File Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Brute Ratel C4", "AgentTesla", "Qakbot", "IcedID", "Azorult", "Remcos", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1566.001", "T1566", "T1204.001", "T1204"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Microsoft\\Windows\\Recent\\*") Filesystem.file_name IN ("*.iso.lnk", "*.img.lnk", "*.vhd.lnk", "*vhdx.lnk") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iso_lnk_file_creation_filter`

[ESCU - Windows Java Spawning Shells - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the process name of java.exe and w3wp.exe spawning a Windows shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "cmd.exe", "powershell.exe". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the process name of java.exe and w3wp.exe spawning a Windows shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "cmd.exe", "powershell.exe". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on that.
action.escu.creation_date = 2023-01-23
action.escu.modification_date = 2023-01-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Java Spawning Shells - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Windows shell, potentially indicative of exploitation.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Java Spawning Shells - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-44228", "CVE-2022-47966"], "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the process name of java.exe and w3wp.exe spawning a Windows shell. This is potentially indicative of exploitation of the Java application and may be related to current event CVE-2021-44228 (Log4Shell). The shells included in the macro are "cmd.exe", "powershell.exe". Upon triage, review parallel processes and command-line arguments to determine legitimacy.
action.notable.param.rule_title = Windows Java Spawning Shells
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java.exe OR Processes.parent_process_name=w3wp.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_java_spawning_shells_filter`

[ESCU - Windows Kerberos Local Successful Logon - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a local successful authentication event on a Windows endpoint using the Kerberos package. The target user security identified will be set to the built-in local Administrator account, along with the remote address as localhost - 127.0.0.1. This may be indicative of a kerberos relay attack. Upon triage, review for recently ran binaries on disk. In addition, look for new computer accounts added to Active Directory and other anomolous AD events.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
action.escu.data_models = ["Authentication"]
action.escu.eli5 = The following analytic identifies a local successful authentication event on a Windows endpoint using the Kerberos package. The target user security identified will be set to the built-in local Administrator account, along with the remote address as localhost - 127.0.0.1. This may be indicative of a kerberos relay attack. Upon triage, review for recently ran binaries on disk. In addition, look for new computer accounts added to Active Directory and other anomolous AD events.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4624 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = False positives are possible, filtering may be required to restrict to workstations vs domain controllers. Filter as needed.
action.escu.creation_date = 2022-04-27
action.escu.modification_date = 2022-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Kerberos Local Successful Logon - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"]
action.risk = 1
action.risk.param._risk_message = A successful localhost Kerberos authentication event occurred on $dest$, possibly indicative of Kerberos relay attack.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Kerberos Local Successful Logon - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks", "Local Privilege Escalation With KrbRelayUp"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1558"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a local successful authentication event on a Windows endpoint using the Kerberos package. The target user security identified will be set to the built-in local Administrator account, along with the remote address as localhost - 127.0.0.1. This may be indicative of a kerberos relay attack. Upon triage, review for recently ran binaries on disk. In addition, look for new computer accounts added to Active Directory and other anomolous AD events.
action.notable.param.rule_title = Windows Kerberos Local Successful Logon
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security`  EventCode=4624 Logon_Type=3 Authentication_Package=Kerberos action=success src_ip=127.0.0.1 | stats count min(_time) as firstTime max(_time) as lastTime by dest, subject, action, Security_ID, user, Account_Name, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_kerberos_local_successful_logon_filter`

[ESCU - Windows KrbRelayUp Service Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the default service name created by KrbRelayUp. Defenders should be aware that attackers could change the hardcoded service name of the KrbRelayUp tool and bypass this detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the default service name created by KrbRelayUp. Defenders should be aware that attackers could change the hardcoded service name of the KrbRelayUp tool and bypass this detection.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows System Event Logs with 7045 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = False positives should be limited as this is specific to KrbRelayUp based attack. Filter as needed.
action.escu.creation_date = 2022-05-02
action.escu.modification_date = 2022-05-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows KrbRelayUp Service Creation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Local Privilege Escalation With KrbRelayUp"]
action.risk = 1
action.risk.param._risk_message = A service was created on $dest$, related to KrbRelayUp.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows KrbRelayUp Service Creation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Local Privilege Escalation With KrbRelayUp"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the default service name created by KrbRelayUp. Defenders should be aware that attackers could change the hardcoded service name of the KrbRelayUp tool and bypass this detection.
action.notable.param.rule_title = Windows KrbRelayUp Service Creation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045 Service_Name IN ("KrbSCM") | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_krbrelayup_service_creation_filter`

[ESCU - Windows Large Number of Computer Service Tickets Requested - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event ID 4769, `A Kerberos service ticket was requested`, to identify more than 30 computer service ticket requests from one source. When a domain joined endpoint connects to other remote endpoint, it will first request a Kerberos Service Ticket with the computer name as the Service Name. A user requesting a large number of computer service tickets for different endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.\
Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1135", "T1078"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event ID 4769, `A Kerberos service ticket was requested`, to identify more than 30 computer service ticket requests from one source. When a domain joined endpoint connects to other remote endpoint, it will first request a Kerberos Service Ticket with the computer name as the Service Name. A user requesting a large number of computer service tickets for different endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.\
Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold as needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = An single endpoint requesting a large number of kerberos service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems.
action.escu.creation_date = 2023-03-20
action.escu.modification_date = 2023-03-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Large Number of Computer Service Tickets Requested - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A large number of kerberos computer service tickets were requested by $IpAddress$ within 5 minutes.
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Large Number of Computer Service Tickets Requested - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1135", "T1078"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4769 ServiceName="*$" TargetUserName!="*$" | bucket span=5m _time | stats dc(ServiceName) AS unique_targets values(ServiceName) as host_targets by _time, IpAddress, TargetUserName | where unique_targets > 30 | `windows_large_number_of_computer_service_tickets_requested_filter`

[ESCU - Windows Lateral Tool Transfer RemCom - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the use of RemCom.exe - The open source psexec. This utility provides the ability to move laterally and run scripts or commands remotely.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1570"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies the use of RemCom.exe - The open source psexec. This utility provides the ability to move laterally and run scripts or commands remotely.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present based on Administrative use. Filter as needed.
action.escu.creation_date = 2023-03-20
action.escu.modification_date = 2023-03-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Lateral Tool Transfer RemCom - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to move laterally.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Lateral Tool Transfer RemCom - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1570"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies the use of RemCom.exe - The open source psexec. This utility provides the ability to move laterally and run scripts or commands remotely.
action.notable.param.rule_title = Windows Lateral Tool Transfer RemCom
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=remcom.exe OR Processes.original_file_name=RemCom.exe) Processes.process="*\\*" Processes.process IN ("*/user:*", "*/pwd:*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lateral_tool_transfer_remcom_filter`

[ESCU - Windows Ldifde Directory Object Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Ldifde.exe, which provides the ability to create, modify, or delete LDAP directory objects. Natively, the binary is only installed on a domain controller. However, adversaries or administrators may install the Windows Remote Server Admin Tools for ldifde.exe. Ldifde.exe is a Microsoft Windows command-line utility used to import or export LDAP directory entries. LDAP stands for Lightweight Directory Access Protocol, which is a protocol used for accessing and managing directory information services over an IP network. LDIF, on the other hand, stands for LDAP Data Interchange Format, a standard plain-text data interchange format for representing LDAP directory entries. -i This is a flag used with Ldifde.exe to denote import mode. In import mode, Ldifde.exe takes an LDIF file and imports its contents into the LDAP directory. The data in the LDIF file might include new objects to be created, or modifications or deletions to existing objects. -f This flag is used to specify the filename of the LDIF file that Ldifde.exe will import from (in the case of the -i flag) or export to (without the -i flag). For example, if you wanted to import data from a file called data.ldif, you would use the command ldifde -i -f data.ldif. Keep in mind that while the use of Ldifde.exe is legitimate in many contexts, it can also be used maliciously. For instance, an attacker who has gained access to a domain controller could potentially use Ldifde.exe to export sensitive data or make unauthorized changes to the directory. Therefore, it's important to monitor for unusual or unauthorized use of this tool.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105", "T1069.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Ldifde.exe, which provides the ability to create, modify, or delete LDAP directory objects. Natively, the binary is only installed on a domain controller. However, adversaries or administrators may install the Windows Remote Server Admin Tools for ldifde.exe. Ldifde.exe is a Microsoft Windows command-line utility used to import or export LDAP directory entries. LDAP stands for Lightweight Directory Access Protocol, which is a protocol used for accessing and managing directory information services over an IP network. LDIF, on the other hand, stands for LDAP Data Interchange Format, a standard plain-text data interchange format for representing LDAP directory entries. -i This is a flag used with Ldifde.exe to denote import mode. In import mode, Ldifde.exe takes an LDIF file and imports its contents into the LDAP directory. The data in the LDIF file might include new objects to be created, or modifications or deletions to existing objects. -f This flag is used to specify the filename of the LDIF file that Ldifde.exe will import from (in the case of the -i flag) or export to (without the -i flag). For example, if you wanted to import data from a file called data.ldif, you would use the command ldifde -i -f data.ldif. Keep in mind that while the use of Ldifde.exe is legitimate in many contexts, it can also be used maliciously. For instance, an attacker who has gained access to a domain controller could potentially use Ldifde.exe to export sensitive data or make unauthorized changes to the directory. Therefore, it's important to monitor for unusual or unauthorized use of this tool.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2023-05-25
action.escu.modification_date = 2023-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Ldifde Directory Object Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing ldifde on a domain controller.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Ldifde Directory Object Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1105", "T1069.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of Ldifde.exe, which provides the ability to create, modify, or delete LDAP directory objects. Natively, the binary is only installed on a domain controller. However, adversaries or administrators may install the Windows Remote Server Admin Tools for ldifde.exe. Ldifde.exe is a Microsoft Windows command-line utility used to import or export LDAP directory entries. LDAP stands for Lightweight Directory Access Protocol, which is a protocol used for accessing and managing directory information services over an IP network. LDIF, on the other hand, stands for LDAP Data Interchange Format, a standard plain-text data interchange format for representing LDAP directory entries. -i This is a flag used with Ldifde.exe to denote import mode. In import mode, Ldifde.exe takes an LDIF file and imports its contents into the LDAP directory. The data in the LDIF file might include new objects to be created, or modifications or deletions to existing objects. -f This flag is used to specify the filename of the LDIF file that Ldifde.exe will import from (in the case of the -i flag) or export to (without the -i flag). For example, if you wanted to import data from a file called data.ldif, you would use the command ldifde -i -f data.ldif. Keep in mind that while the use of Ldifde.exe is legitimate in many contexts, it can also be used maliciously. For instance, an attacker who has gained access to a domain controller could potentially use Ldifde.exe to export sensitive data or make unauthorized changes to the directory. Therefore, it's important to monitor for unusual or unauthorized use of this tool.
action.notable.param.rule_title = Windows Ldifde Directory Object Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ldifde.exe Processes.process IN ("*-i *", "*-f *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ldifde_directory_object_behavior_filter`

[ESCU - Windows Linked Policies In ADSI Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain organizational unit for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate domain organizational unit for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Linked Policies In ADSI Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Data Destruction", "Active Directory Discovery", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = Windows PowerShell [Adsisearcher] was used user enumeration on $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Linked Policies In ADSI Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Active Directory Discovery", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*objectcategory=organizationalunit*" ScriptBlockText = "*findAll()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_linked_policies_in_adsi_discovery_filter`

[ESCU - Windows Local Administrator Credential Stuffing - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages events 4625 and 4624 to identify an endpoint using the builtin local Administrator account to authenticate to a large numbers of endpoints. Specifically, the logic will trigger when an endpoints attempts to authenticate to more than 30 target computers within a 5 minute timespan. This behavior could represent an adversary who has obtained access to local credentials and is trying to validate if these credentials work on other hosts to escalate their privileges. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110", "T1110.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages events 4625 and 4624 to identify an endpoint using the builtin local Administrator account to authenticate to a large numbers of endpoints. Specifically, the logic will trigger when an endpoints attempts to authenticate to more than 30 target computers within a 5 minute timespan. This behavior could represent an adversary who has obtained access to local credentials and is trying to validate if these credentials work on other hosts to escalate their privileges. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed.
action.escu.creation_date = 2023-03-22
action.escu.modification_date = 2023-03-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Local Administrator Credential Stuffing - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = Local Administrator credential stuffing attack coming from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "host_targets", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Local Administrator Credential Stuffing - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1110", "T1110.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages events 4625 and 4624 to identify an endpoint using the builtin local Administrator account to authenticate to a large numbers of endpoints. Specifically, the logic will trigger when an endpoints attempts to authenticate to more than 30 target computers within a 5 minute timespan. This behavior could represent an adversary who has obtained access to local credentials and is trying to validate if these credentials work on other hosts to escalate their privileges. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.notable.param.rule_title = Windows Local Administrator Credential Stuffing
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4625 OR EventCode=4624 Logon_Type=3 TargetUserName=Administrator | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets by _time, IpAddress, TargetUserName, EventCode | where unique_targets > 30 | `windows_local_administrator_credential_stuffing_filter`

[ESCU - Windows Mail Protocol In Non-Common Process Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a possible windows application having a SMTP connection in a non common installation path in windows operating system.This network protocol is being used by adversaries, threat actors and malware like AgentTesla as a Command And Control communication to transfer its collected stolen information like the desktop screenshots, browser information and system information of a targeted or compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1071.003", "T1071"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a possible windows application having a SMTP connection in a non common installation path in windows operating system.This network protocol is being used by adversaries, threat actors and malware like AgentTesla as a Command And Control communication to transfer its collected stolen information like the desktop screenshots, browser information and system information of a targeted or compromised host.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 3 connection events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = third party application may use this network protocol as part of its feature. Filter is needed.
action.escu.creation_date = 2022-09-16
action.escu.modification_date = 2022-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Mail Protocol In Non-Common Process Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AgentTesla"]
action.risk = 1
action.risk.param._risk_message = a process $Image$ is having a SMTP connection to $DestinationHostname$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Mail Protocol In Non-Common Process Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["AgentTesla"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1071.003", "T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=3 NOT(Image IN("*\\program files*", "*\\thunderbird.exe","*\\outlook.exe")) (DestinationPortName="smtp" OR DestinationPort=25 OR DestinationPort=587) | stats count min(_time) as firstTime max(_time) as lastTime by Image DestinationPort DestinationPortName DestinationHostname SourceHostname SourcePort SourcePortName Protocol DestinationIp dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mail_protocol_in_non_common_process_path_filter`

[ESCU - Windows Mark Of The Web Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious process that delete mark-of-the-web data stream. This technique has been observed in various instances of malware and adversarial activities aimed at circumventing security restrictions within the Windows Operating System, particularly pertaining to files downloaded from the internet. An example of this scenario is demonstrated by Ave Maria RAT, which attempts to delete this data stream as a means to evade such restrictions.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1553.005"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious process that delete mark-of-the-web data stream. This technique has been observed in various instances of malware and adversarial activities aimed at circumventing security restrictions within the Windows Operating System, particularly pertaining to files downloaded from the internet. An example of this scenario is demonstrated by Ave Maria RAT, which attempts to delete this data stream as a means to evade such restrictions.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-08-14
action.escu.modification_date = 2023-08-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Mark Of The Web Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = A mark-of-the-web data stream is deleted on $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Mark Of The Web Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Warzone RAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1553.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious process that delete mark-of-the-web data stream. This technique has been observed in various instances of malware and adversarial activities aimed at circumventing security restrictions within the Windows Operating System, particularly pertaining to files downloaded from the internet. An example of this scenario is demonstrated by Ave Maria RAT, which attempts to delete this data stream as a means to evade such restrictions.
action.notable.param.rule_title = Windows Mark Of The Web Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=23 TargetFilename = "*:Zone.Identifier" | stats  min(_time) as firstTime max(_time) as lastTime count by user EventCode Image TargetFilename ProcessID dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mark_of_the_web_bypass_filter`

[ESCU - Windows Masquerading Explorer As Child Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious parent process of explorer.exe. Explorer is usually executed by userinit.exe that will exit after execution that causes the main explorer.exe no parent process. Some malware like qakbot spawn another explorer.exe to inject its code. This TTP detection is a good indicator that a process spawning explorer.exe might inject code or masquerading its parent child process to evade detections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious parent process of explorer.exe. Explorer is usually executed by userinit.exe that will exit after execution that causes the main explorer.exe no parent process. Some malware like qakbot spawn another explorer.exe to inject its code. This TTP detection is a good indicator that a process spawning explorer.exe might inject code or masquerading its parent child process to evade detections.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-20
action.escu.modification_date = 2022-10-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Masquerading Explorer As Child Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = explorer.exe hash a suspicious parent process $parent_process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Masquerading Explorer As Child Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1574.002", "T1574"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious parent process of explorer.exe. Explorer is usually executed by userinit.exe that will exit after execution that causes the main explorer.exe no parent process. Some malware like qakbot spawn another explorer.exe to inject its code. This TTP detection is a good indicator that a process spawning explorer.exe might inject code or masquerading its parent child process to evade detections.
action.notable.param.rule_title = Windows Masquerading Explorer As Child Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN("cmd.exe", "powershell.exe", "regsvr32.exe") AND Processes.process_name = "explorer.exe" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_masquerading_explorer_as_child_process_filter`

[ESCU - Windows Mimikatz Binary Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = As simple as it sounds, this analytic identifies when the native mimikatz.exe binary executes on Windows. It does look for the original file name as well, just in case the binary is renamed. Adversaries sometimes bring in the default binary and run it directly. Benjamin Delpy originally created Mimikatz as a proof of concept to show Microsoft that its authentication protocols were vulnerable to an attack. Instead, he inadvertently created one of the most widely used and downloaded threat actor tools of the past 20 years. Mimikatz is an open-source application that allows users to view and save authentication credentials such as Kerberos tickets.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = As simple as it sounds, this analytic identifies when the native mimikatz.exe binary executes on Windows. It does look for the original file name as well, just in case the binary is renamed. Adversaries sometimes bring in the default binary and run it directly. Benjamin Delpy originally created Mimikatz as a proof of concept to show Microsoft that its authentication protocols were vulnerable to an attack. Instead, he inadvertently created one of the most widely used and downloaded threat actor tools of the past 20 years. Mimikatz is an open-source application that allows users to view and save authentication credentials such as Kerberos tickets.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as this is directly looking for Mimikatz, the credential dumping utility.
action.escu.creation_date = 2022-11-16
action.escu.modification_date = 2022-11-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Mimikatz Binary Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Credential Dumping", "CISA AA22-320A", "Sandworm Tools", "Volt Typhoon", "Flax Typhoon"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting dump credentials.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 100}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Mimikatz Binary Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "CISA AA22-320A", "Sandworm Tools", "Volt Typhoon", "Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = As simple as it sounds, this analytic identifies when the native mimikatz.exe binary executes on Windows. It does look for the original file name as well, just in case the binary is renamed. Adversaries sometimes bring in the default binary and run it directly. Benjamin Delpy originally created Mimikatz as a proof of concept to show Microsoft that its authentication protocols were vulnerable to an attack. Instead, he inadvertently created one of the most widely used and downloaded threat actor tools of the past 20 years. Mimikatz is an open-source application that allows users to view and save authentication credentials such as Kerberos tickets.
action.notable.param.rule_title = Windows Mimikatz Binary Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=mimikatz.exe OR Processes.original_file_name=mimikatz.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mimikatz_binary_execution_filter`

[ESCU - Windows Mimikatz Crypto Export File Extensions - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies hardcoded extensions related to the Crypo module within Mimikatz. Moving certificates or downloading them is not malicious, however with Mimikatz having hardcoded names it helps to identify potential usage of certificates being exported.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies hardcoded extensions related to the Crypo module within Mimikatz. Moving certificates or downloading them is not malicious, however with Mimikatz having hardcoded names it helps to identify potential usage of certificates being exported.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be present and may need to be reviewed before this can be turned into a TTP. In addition, remove .pfx (standalone) if it's too much volume.
action.escu.creation_date = 2023-02-07
action.escu.modification_date = 2023-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Mimikatz Crypto Export File Extensions - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Certificate Services", "Sandworm Tools"]
action.risk = 1
action.risk.param._risk_message = Certificate file extensions realted to Mimikatz were identified on disk on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 28}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Mimikatz Crypto Export File Extensions - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services", "Sandworm Tools"], "cis20": ["CIS 10"], "confidence": 70, "impact": 40, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.keyx.rsa.pvk","*sign.rsa.pvk","*sign.dsa.pvk","*dsa.ec.p8k","*dh.ec.p8k", "*.pfx", "*.der") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `windows_mimikatz_crypto_export_file_extensions_filter`

[ESCU - Windows Modify Registry Auto Minor Updates - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will "Treat minor updates like other updates".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint", "Updates"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will "Treat minor updates like other updates".
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Auto Minor Updates - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Auto Minor Updates - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" AND Registry.registry_value_data="0x00000000" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_minor_updates_filter`

[ESCU - Windows Modify Registry Auto Update Notif - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update notification. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will switch the automatic windows update to "Notify before download".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update notification. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will switch the automatic windows update to "Notify before download".
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Auto Update Notif - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A registry modification in Windows auto update notification on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Auto Update Notif - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AUOptions" AND Registry.registry_value_data="0x00000002" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_update_notif_filter`

[ESCU - Windows Modify Registry Default Icon Setting - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect suspicious registry modification to change the default icon association of windows to ransomware . This technique was seen in Lockbit ransomware where it modified the default icon association of the compromised Windows OS  host with its dropped ransomware icon file as part of its  defacement payload. This registry is not commonly modified by a normal user so having this anomaly detection may help to catch possible lockbit ransomware infection or other malware.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect suspicious registry modification to change the default icon association of windows to ransomware . This technique was seen in Lockbit ransomware where it modified the default icon association of the compromised Windows OS  host with its dropped ransomware icon file as part of its  defacement payload. This registry is not commonly modified by a normal user so having this anomaly detection may help to catch possible lockbit ransomware infection or other malware.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-01-16
action.escu.modification_date = 2023-01-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Default Icon Setting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["LockBit Ransomware"]
action.risk = 1
action.risk.param._risk_message = a registry $Registry.registry_path$ was modified or created to modify defaulticon settings of the $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Default Icon Setting - Rule
action.correlationsearch.annotations = {"analytic_story": ["LockBit Ransomware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\defaultIcon\\(Default)*" Registry.registry_path = "*HKCR\\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.action Registry.dest  Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_modify_registry_default_icon_setting_filter`

[ESCU - Windows Modify Registry Disable Toast Notifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows registry to disable toast notifications. This Windows Operating System feature is responsible for alerting or notifying user if application or OS need some updates. Adversaries and malwares like Azorult abuse this technique to disable important update notification in compromised host. This anomaly detection is a good pivot to look for further events related to defense evasion and execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows registry to disable toast notifications. This Windows Operating System feature is responsible for alerting or notifying user if application or OS need some updates. Adversaries and malwares like Azorult abuse this technique to disable important update notification in compromised host. This anomaly detection is a good pivot to look for further events related to defense evasion and execution.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Disable Toast Notifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry for DisallowRun settings was modified to enable in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Disable Toast Notifications - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_toast_notifications_filter`

[ESCU - Windows Modify Registry Disable Win Defender Raw Write Notif - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a modification in the Windows registry to disable Windows Defender raw write notification feature. This policy controls whether raw volume write notifications are sent to behavior monitoring or not. This registry was recently identified in Azorult malware to bypass Windows Defender detections or behavior monitoring in terms of volume write.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a modification in the Windows registry to disable Windows Defender raw write notification feature. This policy controls whether raw volume write notifications are sent to behavior monitoring or not. This registry was recently identified in Azorult malware to bypass Windows Defender detections or behavior monitoring in terms of volume write.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive. Filter as needed.
action.escu.creation_date = 2022-06-23
action.escu.modification_date = 2022-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Disable Win Defender Raw Write Notif - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = The registry for raw write notification settings was modified to disable in $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Disable Win Defender Raw Write Notif - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Real-Time Protection\\DisableRawWriteNotification*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_win_defender_raw_write_notif_filter`

[ESCU - Windows Modify Registry Disable WinDefender Notifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification to disable Windows Defender notification. This technique was being abused by several adversaries, malware authors and also red-teamers to evade detection on the targeted machine. RedLine Stealer is one of the malware we've seen that uses this technique to bypass Windows defender detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification to disable Windows Defender notification. This technique was being abused by several adversaries, malware authors and also red-teamers to evade detection on the targeted machine. RedLine Stealer is one of the malware we've seen that uses this technique to bypass Windows defender detection.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Disable WinDefender Notifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A registry modification to disable Windows Defender notification on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Disable WinDefender Notifications - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious registry modification to disable Windows Defender notification. This technique was being abused by several adversaries, malware authors and also red-teamers to evade detection on the targeted machine. RedLine Stealer is one of the malware we've seen that uses this technique to bypass Windows defender detection.
action.notable.param.rule_title = Windows Modify Registry Disable WinDefender Notifications
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender Security Center\\Notifications\\DisableNotifications" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_disable_windefender_notifications_filter`

[ESCU - Windows Modify Registry Disable Windows Security Center Notif - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows registry to disable windows center notifications. This Windows Operating System feature is responsible for alerting or notifying user if application or OS need some updates. Adversaries and malwares like Azorult abuse this technique to disable important update notification in compromised host. This anomaly detection is a good pivot to look for further events related to defense evasion and execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows registry to disable windows center notifications. This Windows Operating System feature is responsible for alerting or notifying user if application or OS need some updates. Adversaries and malwares like Azorult abuse this technique to disable important update notification in compromised host. This anomaly detection is a good pivot to look for further events related to defense evasion and execution.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Disable Windows Security Center Notif - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry for security center notification settings was modified to disable mode in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Disable Windows Security Center Notif - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_windows_security_center_notif_filter`

[ESCU - Windows Modify Registry Disabling WER Settings - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a modification in the Windows registry to disable Windows error reporting settings. This Windows feature allows the user to report bugs, errors, failure or problems encountered in specific application or processes. Adversaries use this technique to hide any error or failure that some of its malicious components trigger.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a modification in the Windows registry to disable Windows error reporting settings. This Windows feature allows the user to report bugs, errors, failure or problems encountered in specific application or processes. Adversaries use this technique to hide any error or failure that some of its malicious components trigger.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Disabling WER Settings - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry for WER settings was modified to be disabled on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Disabling WER Settings - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a modification in the Windows registry to disable Windows error reporting settings. This Windows feature allows the user to report bugs, errors, failure or problems encountered in specific application or processes. Adversaries use this technique to hide any error or failure that some of its malicious components trigger.
action.notable.param.rule_title = Windows Modify Registry Disabling WER Settings
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\disable*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disabling_wer_settings_filter`

[ESCU - Windows Modify Registry DisAllow Windows App - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies modification in the Windows registry to prevent user running specific computer programs that could aid them in manually removing malware or detecting it using security products. This technique was recently identified in Azorult malware where it uses this registry value to prevent several AV products to execute on the compromised host machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies modification in the Windows registry to prevent user running specific computer programs that could aid them in manually removing malware or detecting it using security products. This technique was recently identified in Azorult malware where it uses this registry value to prevent several AV products to execute on the compromised host machine.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive. Filter as needed.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry DisAllow Windows App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = The registry for DisallowRun settings was modified to enable in $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry DisAllow Windows App - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies modification in the Windows registry to prevent user running specific computer programs that could aid them in manually removing malware or detecting it using security products. This technique was recently identified in Azorult malware where it uses this registry value to prevent several AV products to execute on the compromised host machine.
action.notable.param.rule_title = Windows Modify Registry DisAllow Windows App
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disallow_windows_app_filter`

[ESCU - Windows Modify Registry Do Not Connect To Win Update - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will disable Windos update functionality, and may cause connection to public services such as the Windows Store to stop working. This policy applies only when this PC is configured to connect to an intranet update service using the "Specify intranet Microsoft update service location" policy.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will disable Windos update functionality, and may cause connection to public services such as the Windows Store to stop working. This policy applies only when this PC is configured to connect to an intranet update service using the "Specify intranet Microsoft update service location" policy.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Do Not Connect To Win Update - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = a registry modification in Windows auto update configuration in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Do Not Connect To Win Update - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_do_not_connect_to_win_update_filter`

[ESCU - Windows Modify Registry EnableLinkedConnections - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows linked connection configuration. This technique was being abused by several adversaries, malware like BlackByte ransomware to enable the linked connections feature, that allows network shares to be accessed using both standard and administrator-level privileges simultaneously. By default, Windows does not enable this feature to enhance security.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows linked connection configuration. This technique was being abused by several adversaries, malware like BlackByte ransomware to enable the linked connections feature, that allows network shares to be accessed using both standard and administrator-level privileges simultaneously. By default, Windows does not enable this feature to enhance security.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry EnableLinkedConnections - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = A registry modification in Windows EnableLinkedConnections configuration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry EnableLinkedConnections - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious registry modification of Windows linked connection configuration. This technique was being abused by several adversaries, malware like BlackByte ransomware to enable the linked connections feature, that allows network shares to be accessed using both standard and administrator-level privileges simultaneously. By default, Windows does not enable this feature to enhance security.
action.notable.param.rule_title = Windows Modify Registry EnableLinkedConnections
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_enablelinkedconnections_filter`

[ESCU - Windows Modify Registry LongPathsEnabled - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows long path enable configuration. This technique was being abused by several adversaries, malware like BlackByte to enable long file path support in the operating system. By default, Windows has a limitation on the maximum length of a file path, which is set to 260 characters. Enabling the LongPathsEnabled setting allows you to work with file paths longer than 260 characters.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows long path enable configuration. This technique was being abused by several adversaries, malware like BlackByte to enable long file path support in the operating system. By default, Windows has a limitation on the maximum length of a file path, which is set to 260 characters. Enabling the LongPathsEnabled setting allows you to work with file paths longer than 260 characters.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry LongPathsEnabled - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackByte Ransomware"]
action.risk = 1
action.risk.param._risk_message = A registry modification in Windows LongPathEnable configuration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 16}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry LongPathsEnabled - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 40, "impact": 40, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\CurrentControlSet\\Control\\FileSystem\\LongPathsEnabled" Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_longpathsenabled_filter`

[ESCU - Windows Modify Registry MaxConnectionPerServer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows max connection per server configuration. This particular technique has been observed in various threat actors, adversaries, and even in malware such as the Warzone (Ave Maria) RAT. By altering the max connection per server setting in the Windows registry, attackers can potentially increase the number of concurrent connections allowed to a remote server. This modification could be exploited for various malicious purposes, including facilitating distributed denial-of-service (DDoS) attacks or enabling more effective lateral movement within a compromised network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows max connection per server configuration. This particular technique has been observed in various threat actors, adversaries, and even in malware such as the Warzone (Ave Maria) RAT. By altering the max connection per server setting in the Windows registry, attackers can potentially increase the number of concurrent connections allowed to a remote server. This modification could be exploited for various malicious purposes, including facilitating distributed denial-of-service (DDoS) attacks or enabling more effective lateral movement within a compromised network.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-07-26
action.escu.modification_date = 2023-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry MaxConnectionPerServer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = A registry modification in max connection per server configuration in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry MaxConnectionPerServer - Rule
action.correlationsearch.annotations = {"analytic_story": ["Warzone RAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\\MaxConnectionsPerServer*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\\MaxConnectionsPer1_0Server*")  Registry.registry_value_data = "0x0000000a" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_maxconnectionperserver_filter`

[ESCU - Windows Modify Registry No Auto Reboot With Logon User - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will allow "Logged-on user gets to choose whether or not to restart his or her compute".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will allow "Logged-on user gets to choose whether or not to restart his or her compute".
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry No Auto Reboot With Logon User - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A registry modification in Windows auto update configuration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry No Auto Reboot With Logon User - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoRebootWithLoggedOnUsers" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_reboot_with_logon_user_filter`

[ESCU - Windows Modify Registry No Auto Update - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will "Disable Automatic Updates".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will "Disable Automatic Updates".
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry No Auto Update - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A registry modification in Windows auto update configuration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry No Auto Update - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoUpdate" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_update_filter`

[ESCU - Windows Modify Registry Qakbot Binary Data Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry entry created by Qakbot malware as part of its malicious execution. This "Binary Data" Registry  was created by newly spawn explorer.exe where its malicious code is injected to it. The registry consist of 8 random registry value name with encrypted binary data on its registry value data. This anomaly detections can be a good pivot for possible Qakbot malware infection or other malware that uses registry to save or store there config or malicious code on the registry data stream.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry entry created by Qakbot malware as part of its malicious execution. This "Binary Data" Registry  was created by newly spawn explorer.exe where its malicious code is injected to it. The registry consist of 8 random registry value name with encrypted binary data on its registry value data. This anomaly detections can be a good pivot for possible Qakbot malware infection or other malware that uses registry to save or store there config or malicious code on the registry data stream.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-10
action.escu.modification_date = 2022-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Qakbot Binary Data Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = registry with binary data $registry_path$ created by $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Qakbot Binary Data Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count dc(registry_value_name) as registry_value_name_count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\*" AND Registry.registry_value_data = "Binary Data" by _time span=1m Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.process_id Registry.registry_key_name | `drop_dm_object_name(Registry)` | rename process_guid as proc_guid | eval registry_key_name_len = len(registry_key_name) | eval registry_value_name_len = len(registry_value_name) | regex registry_value_name="^[0-9a-fA-F]{8}" | where registry_key_name_len < 80 AND registry_value_name_len == 8 | join proc_guid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN  ("explorer.exe", "wermgr.exe","dxdiag.exe", "OneDriveSetup.exe", "mobsync.exe", "msra.exe", "xwizard.exe") by _time span=1m Processes.process_id Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid Processes.process_path | `drop_dm_object_name(Processes)` | rename process_guid as proc_guid | fields _time dest user parent_process_name parent_process process_name process_path process proc_guid registry_path registry_value_name registry_value_data process_id registry_key_name registry_value_name_count] | stats min(_time) as firstTime max(_time) as lastTime values(registry_value_name) as registry_value_name dc(registry_value_name) as registry_value_name_count by process_path registry_key_name registry_value_data proc_guid registry_key_name_len registry_value_name_len | where registry_value_name_count >= 5 | `windows_modify_registry_qakbot_binary_data_registry_filter`

[ESCU - Windows Modify Registry Reg Restore - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of reg.exe with "restore" parameter. This reg.exe parameter is commonly used to restore registry backup data in a targeted host. This approach or technique was also seen in post-exploitation tool like winpeas where it uses "reg save" and "reg restore" to check the registry modification restriction in targeted host after gaining access to it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of reg.exe with "restore" parameter. This reg.exe parameter is commonly used to restore registry backup data in a targeted host. This approach or technique was also seen in post-exploitation tool like winpeas where it uses "reg save" and "reg restore" to check the registry modification restriction in targeted host after gaining access to it.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network administrator can use this command tool to backup registry before updates or modifying critical registries.
action.escu.creation_date = 2022-12-12
action.escu.modification_date = 2022-12-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Reg Restore - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Reg Restore - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process = "* restore *" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_reg_restore_filter`

[ESCU - Windows Modify Registry Regedit Silent Reg Import - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies modification of Windows registry using regedit.exe application with silent mode parameter. regedit.exe windows application is commonly used as GUI app to check or modify registry. This application is also has undocumented command-line parameter and one of those are silent mode parameter that performs action without stopping for confirmation with dialog box. Importing registry from .reg files need to monitor in a production environment since it can be used adversaries to import RMS registry in compromised host.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies modification of Windows registry using regedit.exe application with silent mode parameter. regedit.exe windows application is commonly used as GUI app to check or modify registry. This application is also has undocumented command-line parameter and one of those are silent mode parameter that performs action without stopping for confirmation with dialog box. Importing registry from .reg files need to monitor in a production environment since it can be used adversaries to import RMS registry in compromised host.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may execute this command that may cause some false positive. Filter as needed.
action.escu.creation_date = 2022-06-24
action.escu.modification_date = 2022-06-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Regedit Silent Reg Import - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = The regedit app was executed with silet mode parameter to import .reg file on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Regedit Silent Reg Import - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="regedit.exe" OR Processes.original_file_name="regedit.exe") AND Processes.process="* /s *" AND Processes.process="*.reg*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_regedit_silent_reg_import_filter`

[ESCU - Windows Modify Registry Risk Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to identify instances where three or more distinct analytics associated with Mitre ID T1112 - Modification of registry information are triggered. Such occurrences could indicate the presence of multiple malicious registry modifications on a host. Malicious actors frequently manipulate the Windows Registry to hide important configuration details within specific Registry keys. This technique allows them to obscure their activities, erase any evidence during cleanup operations, and establish continuous access and execution of malicious code.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = This analytic is designed to identify instances where three or more distinct analytics associated with Mitre ID T1112 - Modification of registry information are triggered. Such occurrences could indicate the presence of multiple malicious registry modifications on a host. Malicious actors frequently manipulate the Windows Registry to hide important configuration details within specific Registry keys. This technique allows them to obscure their activities, erase any evidence during cleanup operations, and establish continuous access and execution of malicious code.
action.escu.how_to_implement = Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
action.escu.known_false_positives = False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
action.escu.creation_date = 2023-06-15
action.escu.modification_date = 2023-06-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Risk Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Registry Abuse"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Windows Modify Registry Risk Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is designed to identify instances where three or more distinct analytics associated with Mitre ID T1112 - Modification of registry information are triggered. Such occurrences could indicate the presence of multiple malicious registry modifications on a host. Malicious actors frequently manipulate the Windows Registry to hide important configuration details within specific Registry keys. This technique allows them to obscure their activities, erase any evidence during cleanup operations, and establish continuous access and execution of malicious code.
action.notable.param.rule_title = RBA: Windows Modify Registry Risk Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*registry*") All_Risk.annotations.mitre_attack.mitre_technique_id IN ("*T1112*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 3 | `windows_modify_registry_risk_behavior_filter`

[ESCU - Windows Modify Registry Suppress Win Defender Notif - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows registry to suppress windows defender notification. This technique was abuse by adversaries and threat actor to bypassed windows defender on the targeted host. Azorult malware is one of the malware use this technique that also disable toast notification and other windows features as part of its malicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows registry to suppress windows defender notification. This technique was abuse by adversaries and threat actor to bypassed windows defender on the targeted host. Azorult malware is one of the malware use this technique that also disable toast notification and other windows features as part of its malicious behavior.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Suppress Win Defender Notif - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry for suppresing windows fdefender notification settings was modified to disabled in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Suppress Win Defender Notif - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\UX Configuration\\Notification_Suppress*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_suppress_win_defender_notif_filter`

[ESCU - Windows Modify Registry Tamper Protection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification to tamper Windows Defender protection. This technique was being abused by several adversaries, malware authors and also red-teamers to evade detection on the targeted machine. RedLine Stealer is one of the malware we've seen that uses this technique to bypass Windows defender detection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification to tamper Windows Defender protection. This technique was being abused by several adversaries, malware authors and also red-teamers to evade detection on the targeted machine. RedLine Stealer is one of the malware we've seen that uses this technique to bypass Windows defender detection.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry Tamper Protection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A registry modification to tamper Windows Defender protection on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry Tamper Protection - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious registry modification to tamper Windows Defender protection. This technique was being abused by several adversaries, malware authors and also red-teamers to evade detection on the targeted machine. RedLine Stealer is one of the malware we've seen that uses this technique to bypass Windows defender detection.
action.notable.param.rule_title = Windows Modify Registry Tamper Protection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows Defender\\Features\\TamperProtection" AND Registry.registry_value_data="0x00000000" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_tamper_protection_filter`

[ESCU - Windows Modify Registry UpdateServiceUrlAlternate - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that specifies an intranet server to host updates from Microsoft Update.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that specifies an intranet server to host updates from Microsoft Update.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry UpdateServiceUrlAlternate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A registry modification in Windows auto update configuration on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry UpdateServiceUrlAlternate - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\UpdateServiceUrlAlternate" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_updateserviceurlalternate_filter`

[ESCU - Windows Modify Registry USeWuServer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will use "The WUServer value unless this key is set".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification that will use "The WUServer value unless this key is set".
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry USeWuServer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry USeWuServer - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\UseWUServer" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_usewuserver_filter`

[ESCU - Windows Modify Registry With MD5 Reg Key Name - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to identify potentially malicious registry modification characterized by MD5-like registry key names. This technique has been notably observed in NjRAT malware, which employs such registries for fileless storage of keylogs and .DLL plugins. Detecting this tactic serves as an effective means of identifying possible NjRAT malware instances that create or modify registries as part of their malicious activities.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is designed to identify potentially malicious registry modification characterized by MD5-like registry key names. This technique has been notably observed in NjRAT malware, which employs such registries for fileless storage of keylogs and .DLL plugins. Detecting this tactic serves as an effective means of identifying possible NjRAT malware instances that create or modify registries as part of their malicious activities.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-09-25
action.escu.modification_date = 2023-09-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry With MD5 Reg Key Name - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = A md5 registry value name $registry_value_name$ is created on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry With MD5 Reg Key Name - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is designed to identify potentially malicious registry modification characterized by MD5-like registry key names. This technique has been notably observed in NjRAT malware, which employs such registries for fileless storage of keylogs and .DLL plugins. Detecting this tactic serves as an effective means of identifying possible NjRAT malware instances that create or modify registries as part of their malicious activities.
action.notable.param.rule_title = Windows Modify Registry With MD5 Reg Key Name
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly`  count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | eval dropped_reg_path = split(registry_path, "\\") | eval dropped_reg_path_split_count = mvcount(dropped_reg_path) | eval validation_result= if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5") | where validation_result = "md5" AND dropped_reg_path_split_count <= 5 | table dest user registry_path registry_value_name registry_value_data registry_key_name reg_key_name dropped_reg_path_split_count validation_result | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_with_md5_reg_key_name_filter`

[ESCU - Windows Modify Registry WuServer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification related to the WSUS server used by Automatic Updates and (by default) API callers. This policy is paired with WUStatusServer; both must be set to the same value in order for them to be valid.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification related to the WSUS server used by Automatic Updates and (by default) API callers. This policy is paired with WUStatusServer; both must be set to the same value in order for them to be valid.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry WuServer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry WuServer - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUServer" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_wuserver_filter`

[ESCU - Windows Modify Registry wuStatusServer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification related to the server to which reporting information will be sent for client computers that use the WSUS server configured by the WUServer key. This policy is paired with WUServer; both must be set to the same value in order for them to be valid.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious registry modification of Windows auto update configuration. This technique was being abused by several adversaries, malware authors and also red-teamers to bypass detection or to be able to compromise the target host with zero day exploit or as an additional defense evasion technique. RedLine Stealer is one of the malware we've seen that uses this technique to evade detection and add more payload on the target host. This detection looks for registry modification related to the server to which reporting information will be sent for client computers that use the WSUS server configured by the WUServer key. This policy is paired with WUServer; both must be set to the same value in order for them to be valid.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Registry wuStatusServer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["RedLine Stealer"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Registry wuStatusServer - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUStatusServer" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_wustatusserver_filter`

[ESCU - Windows Modify Show Compress Color And Info Tip Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for suspicious registry modification related to file compression color and information tips. This IOC was seen in hermetic wiper where it has a thread that will create this registry entry to change the color of compressed or encrypted files in NTFS file system as well as the pop up information tips. This is a good indicator that a process tries to modified one of the registry GlobalFolderOptions related to file compression attribution in terms of color in NTFS file system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for suspicious registry modification related to file compression color and information tips. This IOC was seen in hermetic wiper where it has a thread that will create this registry entry to change the color of compressed or encrypted files in NTFS file system as well as the pop up information tips. This is a good indicator that a process tries to modified one of the registry GlobalFolderOptions related to file compression attribution in terms of color in NTFS file system.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Modify Show Compress Color And Info Tip Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "Windows Defense Evasion Tactics", "Windows Registry Abuse", "Hermetic Wiper"]
action.risk = 1
action.risk.param._risk_message = Registry modification in "ShowCompColor" and "ShowInfoTips" on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Modify Show Compress Color And Info Tip Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Windows Defense Evasion Tactics", "Windows Registry Abuse", "Hermetic Wiper"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to look for suspicious registry modification related to file compression color and information tips. This IOC was seen in hermetic wiper where it has a thread that will create this registry entry to change the color of compressed or encrypted files in NTFS file system as well as the pop up information tips. This is a good indicator that a process tries to modified one of the registry GlobalFolderOptions related to file compression attribution in terms of color in NTFS file system.
action.notable.param.rule_title = Windows Modify Show Compress Color And Info Tip Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced*" AND Registry.registry_value_name  IN("ShowCompColor", "ShowInfoTip")) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_show_compress_color_and_info_tip_registry_filter`

[ESCU - Windows MOF Event Triggered Execution via WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following anaytic identifies MOFComp.exe loading a MOF file. The Managed Object Format (MOF) compiler parses a file containing MOF statements and adds the classes and class instances defined in the file to the WMI repository. Typically, MOFComp.exe does not reach out to the public internet or load a MOF file from User Profile paths. A filter and consumer is typically registered in WMI. Review parallel processes and query WMI subscriptions to gather artifacts. The default path of mofcomp.exe is C:\Windows\System32\wbem.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following anaytic identifies MOFComp.exe loading a MOF file. The Managed Object Format (MOF) compiler parses a file containing MOF statements and adds the classes and class instances defined in the file to the WMI repository. Typically, MOFComp.exe does not reach out to the public internet or load a MOF file from User Profile paths. A filter and consumer is typically registered in WMI. Review parallel processes and query WMI subscriptions to gather artifacts. The default path of mofcomp.exe is C:\Windows\System32\wbem.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present from automation based applications (SCCM), filtering may be required. In addition, break the query out based on volume of usage. Filter process names or f
action.escu.creation_date = 2022-07-15
action.escu.modification_date = 2022-07-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MOF Event Triggered Execution via WMI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ loading a MOF file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MOF Event Triggered Execution via WMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1546.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following anaytic identifies MOFComp.exe loading a MOF file. The Managed Object Format (MOF) compiler parses a file containing MOF statements and adds the classes and class instances defined in the file to the WMI repository. Typically, MOFComp.exe does not reach out to the public internet or load a MOF file from User Profile paths. A filter and consumer is typically registered in WMI. Review parallel processes and query WMI subscriptions to gather artifacts. The default path of mofcomp.exe is C:\Windows\System32\wbem.
action.notable.param.rule_title = Windows MOF Event Triggered Execution via WMI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name IN ("cmd.exe", "powershell.exe") Processes.process_name=mofcomp.exe) OR (Processes.process_name=mofcomp.exe Processes.process IN ("*\\AppData\\Local\\*","*\\Users\\Public\\*", "*\\WINDOWS\\Temp\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mof_event_triggered_execution_via_wmi_filter`

[ESCU - Windows MOVEit Transfer Writing ASPX - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of new ASPX files in the MOVEit Transfer application's "wwwroot" directory. This activity is indicative of the recent critical vulnerability found in MOVEit Transfer, where threat actors have been observed exploiting a zero-day vulnerability to install a malicious ASPX file (e.g., "human2.aspx") in the wwwroot directory. The injected file could then be used to exfiltrate sensitive data, including user credentials and file metadata. The vulnerability affects the MOVEit Transfer managed file transfer software developed by Progress, a subsidiary of US-based Progress Software Corporation. This analytic requires endpoint data reflecting process and filesystem activity. The identified process must be responsible for the creation of new ASPX or ASHX files in the specified directory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of new ASPX files in the MOVEit Transfer application's "wwwroot" directory. This activity is indicative of the recent critical vulnerability found in MOVEit Transfer, where threat actors have been observed exploiting a zero-day vulnerability to install a malicious ASPX file (e.g., "human2.aspx") in the wwwroot directory. The injected file could then be used to exfiltrate sensitive data, including user credentials and file metadata. The vulnerability affects the MOVEit Transfer managed file transfer software developed by Progress, a subsidiary of US-based Progress Software Corporation. This analytic requires endpoint data reflecting process and filesystem activity. The identified process must be responsible for the creation of new ASPX or ASHX files in the specified directory.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
action.escu.known_false_positives = The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
action.escu.creation_date = 2023-06-01
action.escu.modification_date = 2023-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MOVEit Transfer Writing ASPX - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["MOVEit Transfer Critical Vulnerability"]
action.risk = 1
action.risk.param._risk_message = The MOVEit application on $dest$ has written a new ASPX file to disk.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MOVEit Transfer Writing ASPX - Rule
action.correlationsearch.annotations = {"analytic_story": ["MOVEit Transfer Critical Vulnerability"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of new ASPX files in the MOVEit Transfer application's "wwwroot" directory. This activity is indicative of the recent critical vulnerability found in MOVEit Transfer, where threat actors have been observed exploiting a zero-day vulnerability to install a malicious ASPX file (e.g., "human2.aspx") in the wwwroot directory. The injected file could then be used to exfiltrate sensitive data, including user credentials and file metadata. The vulnerability affects the MOVEit Transfer managed file transfer software developed by Progress, a subsidiary of US-based Progress Software Corporation. This analytic requires endpoint data reflecting process and filesystem activity. The identified process must be responsible for the creation of new ASPX or ASHX files in the specified directory.
action.notable.param.rule_title = Windows MOVEit Transfer Writing ASPX
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\MOVEitTransfer\\wwwroot\\*") Filesystem.file_name IN("*.aspx", "*.ashx", "*.asp*") OR Filesystem.file_name IN ("human2.aspx","_human2.aspx") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `windows_moveit_transfer_writing_aspx_filter`

[ESCU - Windows MSExchange Management Mailbox Cmdlet Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses the Exchange Management logs, that are enabled by default, to identify suspicious Cmdlet usage related to ProxyShell and ProxyNotShell abuse.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic uses the Exchange Management logs, that are enabled by default, to identify suspicious Cmdlet usage related to ProxyShell and ProxyNotShell abuse.
action.escu.how_to_implement = The following analytic requires collecting the Exchange Management logs via a input. An example inputs is here https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178. We used multiline as the XML format of the logs will require props/transforms. Multiline gives us everything we need in Message for now. Update the macro with your correct sourcetype.
action.escu.known_false_positives = False positives may be present when an Administrator utilizes the cmdlets in the query. Filter or monitor as needed.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MSExchange Management Mailbox Cmdlet Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"]
action.risk = 1
action.risk.param._risk_message = Cmdlets related to ProxyShell and ProxyNotShell have been identified on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 32}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MSExchange Management Mailbox Cmdlet Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"], "cis20": ["CIS 10"], "confidence": 80, "impact": 40, "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `msexchange_management` EventCode=1 Message IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "*Search-Mailbox*") | stats count min(_time) as firstTime max(_time) as lastTime by host Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename host AS dest | `windows_msexchange_management_mailbox_cmdlet_usage_filter`

[ESCU - Windows Mshta Execution In Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the usage of mshta.exe Windows binary in registry to execute malicious script. This technique was seen in kovter malware where it create several registry entry which is a encoded javascript and will be executed by another registry containing mshta and javascript activexobject to execute the encoded script using wscript.shell. This TTP is a good indication of kovter malware or other adversaries or threat actors leveraging fileless detection that survive system reboot.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the usage of mshta.exe Windows binary in registry to execute malicious script. This technique was seen in kovter malware where it create several registry entry which is a encoded javascript and will be executed by another registry containing mshta and javascript activexobject to execute the encoded script using wscript.shell. This TTP is a good indication of kovter malware or other adversaries or threat actors leveraging fileless detection that survive system reboot.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-14
action.escu.modification_date = 2022-10-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Mshta Execution In Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A registry $registry_path$ contains mshta $registry_value_data$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Mshta Execution In Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1218.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the usage of mshta.exe Windows binary in registry to execute malicious script. This technique was seen in kovter malware where it create several registry entry which is a encoded javascript and will be executed by another registry containing mshta and javascript activexobject to execute the encoded script using wscript.shell. This TTP is a good indication of kovter malware or other adversaries or threat actors leveraging fileless detection that survive system reboot.
action.notable.param.rule_title = Windows Mshta Execution In Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_value_data = "*mshta*" OR Registry.registry_value_data IN ("*javascript:*", "*vbscript:*","*WScript.Shell*") by Registry.registry_key_name Registry.registry_path Registry.registry_value_data Registry.action Registry.dest Registry.user| `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mshta_execution_in_registry_filter`

[ESCU - Windows MSIExec DLLRegisterServer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the usage of msiexec.exe using the /y switch parameter, which grants the ability for msiexec to load DLLRegisterServer. Upon triage, review parent process and capture any artifacts for further review.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the usage of msiexec.exe using the /y switch parameter, which grants the ability for msiexec to load DLLRegisterServer. Upon triage, review parent process and capture any artifacts for further review.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This analytic will need to be tuned for your environment based on legitimate usage of msiexec.exe. Filter as needed.
action.escu.creation_date = 2022-06-14
action.escu.modification_date = 2022-06-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MSIExec DLLRegisterServer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows System Binary Proxy Execution MSIExec"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to register a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MSIExec DLLRegisterServer - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows System Binary Proxy Execution MSIExec"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the usage of msiexec.exe using the /y switch parameter, which grants the ability for msiexec to load DLLRegisterServer. Upon triage, review parent process and capture any artifacts for further review.
action.notable.param.rule_title = Windows MSIExec DLLRegisterServer
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process IN ("*/y*", "*-y*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_dllregisterserver_filter`

[ESCU - Windows MSIExec Remote Download - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies msiexec.exe with http in the command-line. This procedure will utilize msiexec.exe to download a remote file and load it. During triage, review parallel processes and capture any artifacts on disk for review.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies msiexec.exe with http in the command-line. This procedure will utilize msiexec.exe to download a remote file and load it. During triage, review parallel processes and capture any artifacts on disk for review.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, filter by destination or parent process as needed.
action.escu.creation_date = 2022-06-16
action.escu.modification_date = 2022-06-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MSIExec Remote Download - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows System Binary Proxy Execution MSIExec"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a remote file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MSIExec Remote Download - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows System Binary Proxy Execution MSIExec"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies msiexec.exe with http in the command-line. This procedure will utilize msiexec.exe to download a remote file and load it. During triage, review parallel processes and capture any artifacts on disk for review.
action.notable.param.rule_title = Windows MSIExec Remote Download
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process IN ("*http://*", "*https://*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_remote_download_filter`

[ESCU - Windows MSIExec Spawn Discovery Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies MSIExec spawning multiple discovery commands, including spawning Cmd.exe or PowerShell.exe. Typically, child processes are not common from MSIExec other than MSIExec spawning itself.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies MSIExec spawning multiple discovery commands, including spawning Cmd.exe or PowerShell.exe. Typically, child processes are not common from MSIExec other than MSIExec spawning itself.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present with MSIExec spawning Cmd or PowerShell. Filtering will be needed. In addition, add other known discovery processes to enhance query.
action.escu.creation_date = 2022-06-13
action.escu.modification_date = 2022-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MSIExec Spawn Discovery Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows System Binary Proxy Execution MSIExec"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running different discovery commands.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MSIExec Spawn Discovery Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows System Binary Proxy Execution MSIExec"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies MSIExec spawning multiple discovery commands, including spawning Cmd.exe or PowerShell.exe. Typically, child processes are not common from MSIExec other than MSIExec spawning itself.
action.notable.param.rule_title = Windows MSIExec Spawn Discovery Command
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe Processes.process_name IN ("powershell.exe","cmd.exe", "nltest.exe","ipconfig.exe","systeminfo.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_spawn_discovery_command_filter`

[ESCU - Windows MSIExec Unregister DLLRegisterServer - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the usage of msiexec.exe using the /z switch parameter, which grants the ability for msiexec to unload DLLRegisterServer. Upon triage, review parent process and capture any artifacts for further review.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the usage of msiexec.exe using the /z switch parameter, which grants the ability for msiexec to unload DLLRegisterServer. Upon triage, review parent process and capture any artifacts for further review.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This analytic will need to be tuned for your environment based on legitimate usage of msiexec.exe. Filter as needed.
action.escu.creation_date = 2022-06-14
action.escu.modification_date = 2022-06-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MSIExec Unregister DLLRegisterServer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows System Binary Proxy Execution MSIExec"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to unregister a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MSIExec Unregister DLLRegisterServer - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows System Binary Proxy Execution MSIExec"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the usage of msiexec.exe using the /z switch parameter, which grants the ability for msiexec to unload DLLRegisterServer. Upon triage, review parent process and capture any artifacts for further review.
action.notable.param.rule_title = Windows MSIExec Unregister DLLRegisterServer
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process IN ("*/z*", "*-z*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_unregister_dllregisterserver_filter`

[ESCU - Windows MSIExec With Network Connections - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies MSIExec with any network connection over port 443 or 80. Typically, MSIExec does not perform network communication to the internet.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies MSIExec with any network connection over port 443 or 80. Typically, MSIExec does not perform network communication to the internet.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present and filtering is required.
action.escu.creation_date = 2022-06-16
action.escu.modification_date = 2022-06-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows MSIExec With Network Connections - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows System Binary Proxy Execution MSIExec"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ contacting a remote destination $dest_ip$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 35}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 35}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows MSIExec With Network Connections - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows System Binary Proxy Execution MSIExec"], "cis20": ["CIS 10"], "confidence": 50, "impact": 70, "mitre_attack": ["T1218.007"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies MSIExec with any network connection over port 443 or 80. Typically, MSIExec does not perform network communication to the internet.
action.notable.param.rule_title = Windows MSIExec With Network Connections
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_msiexec` by _time Processes.user Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port IN ("80","443") by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port All_Traffic.dest_ip | `drop_dm_object_name(All_Traffic)` ] | table _time user dest parent_process_name process_name process_path process process_id dest_port dest_ip | `windows_msiexec_with_network_connections_filter`

[ESCU - Windows Multi hop Proxy TOR Website Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a dns query to a known TOR proxy website. This technique was seen in several adversaries, threat actors and malware like AgentTesla to To disguise the source of its malicious traffic. adversaries may chain together multiple proxies. This Anomaly detection might be a good pivot for a process trying to download or use TOR proxies in a compromised host machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1071.003", "T1071"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a dns query to a known TOR proxy website. This technique was seen in several adversaries, threat actors and malware like AgentTesla to To disguise the source of its malicious traffic. adversaries may chain together multiple proxies. This Anomaly detection might be a good pivot for a process trying to download or use TOR proxies in a compromised host machine.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 22 dns query events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = third party application may use this proxies if allowed in production environment. Filter is needed.
action.escu.creation_date = 2022-09-16
action.escu.modification_date = 2022-09-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multi hop Proxy TOR Website Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["AgentTesla"]
action.risk = 1
action.risk.param._risk_message = a process $Image$ is having a dns query in a tor domain $QueryName$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multi hop Proxy TOR Website Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["AgentTesla"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.003", "T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22 QueryName IN ("*.torproject.org", "www.theonionrouter.com") | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus ProcessId  Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_multi_hop_proxy_tor_website_query_filter`

[ESCU - Windows Multiple Disabled Users Failed To Authenticate Wth Kerberos - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with 30 unique disabled domain users using the Kerberos protocol within 5 minutes. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code `0x12` stands for `clients credentials have been revoked` (account disabled, expired or locked out).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with 30 unique disabled domain users using the Kerberos protocol within 5 minutes. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code `0x12` stands for `clients credentials have been revoked` (account disabled, expired or locked out).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems.
action.escu.creation_date = 2021-04-14
action.escu.modification_date = 2021-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Disabled Users Failed To Authenticate Wth Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Disabled Users Failed To Authenticate Wth Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies one source endpoint failing to authenticate with 30 unique disabled domain users using the Kerberos protocol within 5 minutes. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code `0x12` stands for `clients credentials have been revoked` (account disabled, expired or locked out).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Disabled Users Failed To Authenticate Wth Kerberos
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos_filter`

[ESCU - Windows Multiple Invalid Users Fail To Authenticate Using Kerberos - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with 30 unique invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for `client not found in Kerberos database` (the attempted user is not a valid domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with 30 unique invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for `client not found in Kerberos database` (the attempted user is not a valid domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems.
action.escu.creation_date = 2021-04-14
action.escu.modification_date = 2021-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Invalid Users Fail To Authenticate Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Invalid Users Fail To Authenticate Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies one source endpoint failing to authenticate with 30 unique invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for `client not found in Kerberos database` (the attempted user is not a valid domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Invalid Users Fail To Authenticate Using Kerberos
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_invalid_users_fail_to_authenticate_using_kerberos_filter` 

[ESCU - Windows Multiple Invalid Users Failed To Authenticate Using NTLM - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with 30 unique invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for `The username you typed does not exist` (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with 30 unique invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for `The username you typed does not exist` (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
action.escu.creation_date = 2021-04-15
action.escu.modification_date = 2021-04-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Invalid Users Failed To Authenticate Using NTLM - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential NTLM based password spraying attack from $Workstation$
action.risk.param._risk = [{"risk_object_field": "Workstation", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Invalid Users Failed To Authenticate Using NTLM - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies one source endpoint failing to authenticate with 30 unique invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for `The username you typed does not exist` (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Invalid Users Failed To Authenticate Using NTLM
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | where unique_accounts > 30 | `windows_multiple_invalid_users_failed_to_authenticate_using_ntlm_filter`

[ESCU - Windows Multiple Users Fail To Authenticate Wth ExplicitCredentials - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a source user failing to authenticate with 30 unique users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a source user failing to authenticate with 30 unique users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc.
action.escu.creation_date = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Users Fail To Authenticate Wth ExplicitCredentials - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack from $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Users Fail To Authenticate Wth ExplicitCredentials - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a source user failing to authenticate with 30 unique users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.
action.notable.param.rule_title = Windows Multiple Users Fail To Authenticate Wth ExplicitCredentials
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) as  tried_account by _time, Computer, Caller_User_Name | where unique_accounts > 30 | `windows_multiple_users_fail_to_authenticate_wth_explicitcredentials_filter` 

[ESCU - Windows Multiple Users Failed To Authenticate From Host Using NTLM - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with 30 unique valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with 30 unique valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
action.escu.creation_date = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Users Failed To Authenticate From Host Using NTLM - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential NTLM based password spraying attack from $Workstation$
action.risk.param._risk = [{"risk_object_field": "Workstation", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Users Failed To Authenticate From Host Using NTLM - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies one source endpoint failing to authenticate with 30 unique valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Users Failed To Authenticate From Host Using NTLM
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_from_host_using_ntlm_filter`

[ESCU - Windows Multiple Users Failed To Authenticate From Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a source process name failing to authenticate with 30 uniquer users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a source process name failing to authenticate with 30 uniquer users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
action.escu.creation_date = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Users Failed To Authenticate From Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack from $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Users Failed To Authenticate From Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a source process name failing to authenticate with 30 uniquer users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Users Failed To Authenticate From Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, ProcessName, SubjectUserName, Computer | rename Computer as dest | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_from_process_filter`

[ESCU - Windows Multiple Users Failed To Authenticate Using Kerberos - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with 30 unique users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for `wrong password provided` (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with 30 unique users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for `wrong password provided` (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms.
action.escu.creation_date = 2021-04-08
action.escu.modification_date = 2021-04-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Users Failed To Authenticate Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Users Failed To Authenticate Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies one source endpoint failing to authenticate with 30 unique users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for `wrong password provided` (the attempted user is a legitimate domain user).\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Users Failed To Authenticate Using Kerberos
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_using_kerberos_filter`

[ESCU - Windows Multiple Users Remotely Failed To Authenticate From Host - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a source host failing to authenticate against a remote host with 30 unique users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a source host failing to authenticate against a remote host with 30 unique users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc.
action.escu.creation_date = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Multiple Users Remotely Failed To Authenticate From Host - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Multiple Users Remotely Failed To Authenticate From Host - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a source host failing to authenticate against a remote host with 30 unique users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Active Directory environments can be very different depending on the organization. Users should test this detection and customize the arbitrary threshold when needed. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.\
This logic can be used for real time security monitoring as well as threat hunting exercises. This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.notable.param.rule_title = Windows Multiple Users Remotely Failed To Authenticate From Host
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress, Computer | rename Computer as dest| where unique_accounts > 30 | `windows_multiple_users_remotely_failed_to_authenticate_from_host_filter` 

[ESCU - Windows Ngrok Reverse Proxy Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of ngrok.exe being utilized on the Windows operating system. Unfortunately, there is no original file name for Ngrok, so it may be worth an additional hunt to identify any command-line arguments. The sign of someone using Ngrok is not malicious, however, more recently it has become an adversary tool.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1572", "T1090", "T1102"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of ngrok.exe being utilized on the Windows operating system. Unfortunately, there is no original file name for Ngrok, so it may be worth an additional hunt to identify any command-line arguments. The sign of someone using Ngrok is not malicious, however, more recently it has become an adversary tool.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed.
action.escu.creation_date = 2023-01-12
action.escu.modification_date = 2023-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Ngrok Reverse Proxy Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Reverse Network Proxy", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = A reverse proxy was identified spawning from $parent_process_name$ -  $process_name$ on endpoint $dest$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 50}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 50}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 50}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Ngrok Reverse Proxy Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["Reverse Network Proxy", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 50, "mitre_attack": ["T1572", "T1090", "T1102"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok.exe Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", "*tcp*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_ngrok_reverse_proxy_usage_filter`

[ESCU - Windows NirSoft AdvancedRun - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of AdvancedRun.exe. AdvancedRun.exe has similar capabilities as other remote programs like psexec. AdvancedRun may also ingest a configuration file with all settings defined and perform its activity. The analytic is written in a way to identify a renamed binary and also the common command-line arguments.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1588.002"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of AdvancedRun.exe. AdvancedRun.exe has similar capabilities as other remote programs like psexec. AdvancedRun may also ingest a configuration file with all settings defined and perform its activity. The analytic is written in a way to identify a renamed binary and also the common command-line arguments.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as it is specific to AdvancedRun. Filter as needed based on legitimate usage.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows NirSoft AdvancedRun - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Unusual Processes", "Data Destruction", "WhisperGate"]
action.risk = 1
action.risk.param._risk_message = An instance of advancedrun.exe, $process_name$, was spawned by $parent_process_name$ on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 60}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 60}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 60}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows NirSoft AdvancedRun - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Unusual Processes", "Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 100, "impact": 60, "mitre_attack": ["T1588.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of AdvancedRun.exe. AdvancedRun.exe has similar capabilities as other remote programs like psexec. AdvancedRun may also ingest a configuration file with all settings defined and perform its activity. The analytic is written in a way to identify a renamed binary and also the common command-line arguments.
action.notable.param.rule_title = Windows NirSoft AdvancedRun
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=advancedrun.exe OR Processes.original_file_name=advancedrun.exe) Processes.process IN ("*EXEFilename*","*/cfg*","*RunAs*", "*WindowState*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_nirsoft_advancedrun_filter`

[ESCU - Windows NirSoft Utilities - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic assists with identifying the proces execution of commonly used utilities from NirSoft. Potentially not adversary behavior, but worth identifying to know if the software is present and being used.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1588.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic assists with identifying the proces execution of commonly used utilities from NirSoft. Potentially not adversary behavior, but worth identifying to know if the software is present and being used.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present. Filtering may be required before setting to alert.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows NirSoft Utilities - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Data Destruction", "WhisperGate"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows NirSoft Utilities - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "WhisperGate"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1588.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_path Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `is_nirsoft_software` | `windows_nirsoft_utilities_filter`

[ESCU - Windows Non-System Account Targeting Lsass - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies non SYSTEM accounts requesting access to lsass.exe. This behavior may be related to credential dumping or applications requiring access to credentials. Triaging this event will require understanding the GrantedAccess from the SourceImage. In addition, whether the account is privileged or not. Review the process requesting permissions and review parallel processes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies non SYSTEM accounts requesting access to lsass.exe. This behavior may be related to credential dumping or applications requiring access to credentials. Triaging this event will require understanding the GrantedAccess from the SourceImage. In addition, whether the account is privileged or not. Review the process requesting permissions and review parallel processes.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
action.escu.known_false_positives = False positives will occur based on legitimate application requests, filter based on source image as needed.
action.escu.creation_date = 2022-07-30
action.escu.modification_date = 2022-07-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Non-System Account Targeting Lsass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = A process, $parent_process_path$, has loaded $TargetImage$ that are typically related to credential dumping on $dest$. Review for further details.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_path", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Non-System Account Targeting Lsass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies non SYSTEM accounts requesting access to lsass.exe. This behavior may be related to credential dumping or applications requiring access to credentials. Triaging this event will require understanding the GrantedAccess from the SourceImage. In addition, whether the account is privileged or not. Review the process requesting permissions and review parallel processes.
action.notable.param.rule_title = Windows Non-System Account Targeting Lsass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage=*lsass.exe NOT (SourceUser="NT AUTHORITY\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, parent_process_path ,parent_process_id, TargetImage, GrantedAccess, SourceUser, TargetUser | rename TargetUser as user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_non_system_account_targeting_lsass_filter`

[ESCU - Windows Odbcconf Hunting - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies Odbcconf.exe running in the environment to assist with identifying tuning higher fidelity analytics related to Odbcconf.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.008"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies Odbcconf.exe running in the environment to assist with identifying tuning higher fidelity analytics related to Odbcconf.exe.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present as this is meant to assist with filtering and tuning.
action.escu.creation_date = 2022-06-30
action.escu.modification_date = 2022-06-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Odbcconf Hunting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Odbcconf Hunting - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 20, "impact": 30, "mitre_attack": ["T1218.008"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_hunting_filter`

[ESCU - Windows Odbcconf Load DLL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies odbcconf.exe, Windows Open Database Connectivity utility, utilizing the action function of regsvr to load a DLL. An example will look like - odbcconf.exe /A { REGSVR T1218-2.dll }. During triage, review parent process, parallel procesess and file modifications.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.008"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies odbcconf.exe, Windows Open Database Connectivity utility, utilizing the action function of regsvr to load a DLL. An example will look like - odbcconf.exe /A { REGSVR T1218-2.dll }. During triage, review parent process, parallel procesess and file modifications.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering may need to occur based on legitimate application usage. Filter as needed.
action.escu.creation_date = 2022-06-28
action.escu.modification_date = 2022-06-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Odbcconf Load DLL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 42}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Odbcconf Load DLL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1218.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies odbcconf.exe, Windows Open Database Connectivity utility, utilizing the action function of regsvr to load a DLL. An example will look like - odbcconf.exe /A { REGSVR T1218-2.dll }. During triage, review parent process, parallel procesess and file modifications.
action.notable.param.rule_title = Windows Odbcconf Load DLL
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe Processes.process IN ("*/a *",  "*-a*") Processes.process="*regsvr*"  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_load_dll_filter`

[ESCU - Windows Odbcconf Load Response File - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the odbcconf.exe, Windows Open Database Connectivity utility, loading up a resource file. The file extension is arbitrary and may be named anything. The resource file itself may have different commands supported by Odbcconf to load up a DLL (REGSVR) on disk or additional commands. During triage, review file modifications and parallel processes.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.008"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the odbcconf.exe, Windows Open Database Connectivity utility, loading up a resource file. The file extension is arbitrary and may be named anything. The resource file itself may have different commands supported by Odbcconf to load up a DLL (REGSVR) on disk or additional commands. During triage, review file modifications and parallel processes.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present and filtering may need to occur based on legitimate application usage. Filter as needed.
action.escu.creation_date = 2022-06-30
action.escu.modification_date = 2022-06-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Odbcconf Load Response File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 42}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Odbcconf Load Response File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1218.008"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the odbcconf.exe, Windows Open Database Connectivity utility, loading up a resource file. The file extension is arbitrary and may be named anything. The resource file itself may have different commands supported by Odbcconf to load up a DLL (REGSVR) on disk or additional commands. During triage, review file modifications and parallel processes.
action.notable.param.rule_title = Windows Odbcconf Load Response File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe Processes.process IN ("*-f *","*/f *") Processes.process="*.rsp*"  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_load_response_file_filter`

[ESCU - Windows Office Product Spawning MSDT - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a Microsoft Office product spawning the Windows msdt.exe process. MSDT is a Diagnostics Troubleshooting Wizard native to Windows. This behavior is related to a recently identified sample utilizing protocol handlers to evade preventative controls, including if macros are disabled in the document. During triage, review file modifications for html. In addition, parallel processes including PowerShell and CertUtil.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a Microsoft Office product spawning the Windows msdt.exe process. MSDT is a Diagnostics Troubleshooting Wizard native to Windows. This behavior is related to a recently identified sample utilizing protocol handlers to evade preventative controls, including if macros are disabled in the document. During triage, review file modifications for html. In addition, parallel processes including PowerShell and CertUtil.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, however filter as needed.
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Office Product Spawning MSDT - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"]
action.risk = 1
action.risk.param._risk_message = Office parent process $parent_process_name$ has spawned a child process $process_name$ on host $dest$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 100}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Office Product Spawning MSDT - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190"], "cis20": ["CIS 10"], "confidence": 100, "cve": ["CVE-2022-30190"], "impact": 100, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a Microsoft Office product spawning the Windows msdt.exe process. MSDT is a Diagnostics Troubleshooting Wizard native to Windows. This behavior is related to a recently identified sample utilizing protocol handlers to evade preventative controls, including if macros are disabled in the document. During triage, review file modifications for html. In addition, parallel processes including PowerShell and CertUtil.
action.notable.param.rule_title = Windows Office Product Spawning MSDT
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","outlook.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") Processes.process_name=msdt.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_office_product_spawning_msdt_filter`

[ESCU - Windows PaperCut NG Spawn Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect instances where the PaperCut NG application (pc-app.exe) spawns a Windows shell, specifically cmd.exe or PowerShell. This behavior may indicate potential malicious activity, such as an attacker attempting to gain unauthorized access or execute harmful commands on the affected system.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059", "T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is designed to detect instances where the PaperCut NG application (pc-app.exe) spawns a Windows shell, specifically cmd.exe or PowerShell. This behavior may indicate potential malicious activity, such as an attacker attempting to gain unauthorized access or execute harmful commands on the affected system.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present, but most likely not. Filter as needed.
action.escu.creation_date = 2023-05-15
action.escu.modification_date = 2023-05-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PaperCut NG Spawn Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["PaperCut MF NG Vulnerability"]
action.risk = 1
action.risk.param._risk_message = The PaperCut NG application has spawned a shell $process_name$ on endpoint $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PaperCut NG Spawn Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["PaperCut MF NG Vulnerability"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1059", "T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect instances where the PaperCut NG application (pc-app.exe) spawns a Windows shell, specifically cmd.exe or PowerShell. This behavior may indicate potential malicious activity, such as an attacker attempting to gain unauthorized access or execute harmful commands on the affected system.
action.notable.param.rule_title = Windows PaperCut NG Spawn Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=pc-app.exe `process_cmd` OR `process_powershell` OR Processes.process_name=java.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_papercut_ng_spawn_shell_filter`

[ESCU - Windows Password Managers Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line that retrieves information related to password manager software. This technique was seen in several post exploitation tools like winpeas that are being used by Ransomware Prestige to gather this type of information. Password Managers applications are designed to store user credentials, normally in an encrypted database. Credentials are typically accessible after a user provides a master password that unlocks the database. After the database is unlocked, these credentials may be copied to memory. These databases can be stored as files on disk. Due to this password manager software designed adversaries may find or look for keywords related to the Password Manager databases that can be stolen or extracted for further attacks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1555.005"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line that retrieves information related to password manager software. This technique was seen in several post exploitation tools like winpeas that are being used by Ransomware Prestige to gather this type of information. Password Managers applications are designed to store user credentials, normally in an encrypted database. Credentials are typically accessible after a user provides a master password that unlocks the database. After the database is unlocked, these credentials may be copied to memory. These databases can be stored as files on disk. Due to this password manager software designed adversaries may find or look for keywords related to the Password Manager databases that can be stolen or extracted for further attacks.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Password Managers Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = a process with commandline $process$ that can retrieve information related to password manager databases in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Password Managers Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1555.005"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *" OR  Processes.process = "*findstr*" AND Processes.process IN ( "*.kdbx*", "*credential*", "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*", "*.htpasswd*", "*Ntds.dit*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_password_managers_discovery_filter`

[ESCU - Windows Phishing PDF File Executes URL Link - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect suspicious pdf viewer processes that have a browser application child processes. This event was seen in a pdf spear phishing attachment containing a malicious URL link to download the actual payload. When a user clicks the malicious link the pdf viewer application will execute a process of the host default browser to connect to the malicious site. This anomaly detection can be a good indicator that a possible pdf file has a link executed by a user. The pdf viewer and browser list in this detection is still in progress, add the common browser and pdf viewer you use in opening pdf in your network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect suspicious pdf viewer processes that have a browser application child processes. This event was seen in a pdf spear phishing attachment containing a malicious URL link to download the actual payload. When a user clicks the malicious link the pdf viewer application will execute a process of the host default browser to connect to the malicious site. This anomaly detection can be a good indicator that a possible pdf file has a link executed by a user. The pdf viewer and browser list in this detection is still in progress, add the common browser and pdf viewer you use in opening pdf in your network.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives in PDF file opened PDF Viewer having legitimate URL link, however filter as needed.
action.escu.creation_date = 2023-01-18
action.escu.modification_date = 2023-01-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Phishing PDF File Executes URL Link - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments"]
action.risk = 1
action.risk.param._risk_message = a pdf file opened in pdf viewer process $parent_process_name$ has a child process of a browser $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Phishing PDF File Executes URL Link - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name  IN ("AcroRd32.exe", "FoxitPDFReader.exe") Processes.process_name IN ("firefox.exe", "chrome.exe", "iexplore.exe") by Processes.user Processes.parent_process_name Processes.process_name  Processes.parent_process Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_pdf_file_executes_url_link_filter`

[ESCU - Windows Phishing Recent ISO Exec Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies registry artifacts when an ISO container is opened, clicked or mounted on the Windows operating system. As Microsoft makes changes to macro based document execution, adversaries have begun to utilize container based initial access based phishing campaigns to evade preventative controls. Once the ISO is clicked or mounted it will create a registry artifact related to this event as a recent application executed or opened.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies registry artifacts when an ISO container is opened, clicked or mounted on the Windows operating system. As Microsoft makes changes to macro based document execution, adversaries have begun to utilize container based initial access based phishing campaigns to evade preventative controls. Once the ISO is clicked or mounted it will create a registry artifact related to this event as a recent application executed or opened.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be high depending on the environment and consistent use of ISOs. Restrict to servers, or filter out based on commonly used ISO names. Filter as needed.
action.escu.creation_date = 2022-09-19
action.escu.modification_date = 2022-09-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Phishing Recent ISO Exec Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Brute Ratel C4", "AgentTesla", "Qakbot", "IcedID", "Azorult", "Remcos", "Warzone RAT"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Phishing Recent ISO Exec Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4", "AgentTesla", "Qakbot", "IcedID", "Azorult", "Remcos", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_key_name= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.iso" OR Registry.registry_key_name= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.img" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_recent_iso_exec_registry_filter`

[ESCU - Windows Possible Credential Dumping - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is an enhanced version of two previous analytics that identifies common GrantedAccess permission requests and CallTrace DLLs in order to detect credential dumping. \
GrantedAccess is the requested permissions by the SourceImage into the TargetImage. \
CallTrace Stack trace of where open process is called. Included is the DLL and the relative virtual address of the functions in the call stack right before the open process call. \
dbgcore.dll or dbghelp.dll  are two core Windows debug DLLs that have minidump functions which provide a way for applications to produce crashdump files that contain a useful subset of the entire process context. \
The idea behind using ntdll.dll is to blend in by using native api of ntdll.dll. For example in sekurlsa module there are many ntdll exported api, like RtlCopyMemory, used to execute this module which is related to lsass dumping.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic is an enhanced version of two previous analytics that identifies common GrantedAccess permission requests and CallTrace DLLs in order to detect credential dumping. \
GrantedAccess is the requested permissions by the SourceImage into the TargetImage. \
CallTrace Stack trace of where open process is called. Included is the DLL and the relative virtual address of the functions in the call stack right before the open process call. \
dbgcore.dll or dbghelp.dll  are two core Windows debug DLLs that have minidump functions which provide a way for applications to produce crashdump files that contain a useful subset of the entire process context. \
The idea behind using ntdll.dll is to blend in by using native api of ntdll.dll. For example in sekurlsa module there are many ntdll exported api, like RtlCopyMemory, used to execute this module which is related to lsass dumping.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
action.escu.known_false_positives = False positives will occur based on GrantedAccess 0x1010 and 0x1400, filter based on source image as needed or remove them. Concern is Cobalt Strike usage of Mimikatz will generate 0x1010 initially, but later be caught.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Possible Credential Dumping - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Credential Dumping", "Detect Zerologon Attack", "DarkSide Ransomware", "CISA AA22-257A", "CISA AA22-264A"]
action.risk = 1
action.risk.param._risk_message = A process, $SourceImage$, has loaded $TargetImage$ that are typically related to credential dumping on $dest$. Review for further details.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "SourceImage", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Possible Credential Dumping - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Detect Zerologon Attack", "DarkSide Ransomware", "CISA AA22-257A", "CISA AA22-264A"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is an enhanced version of two previous analytics that identifies common GrantedAccess permission requests and CallTrace DLLs in order to detect credential dumping. \
GrantedAccess is the requested permissions by the SourceImage into the TargetImage. \
CallTrace Stack trace of where open process is called. Included is the DLL and the relative virtual address of the functions in the call stack right before the open process call. \
dbgcore.dll or dbghelp.dll  are two core Windows debug DLLs that have minidump functions which provide a way for applications to produce crashdump files that contain a useful subset of the entire process context. \
The idea behind using ntdll.dll is to blend in by using native api of ntdll.dll. For example in sekurlsa module there are many ntdll exported api, like RtlCopyMemory, used to execute this module which is related to lsass dumping.
action.notable.param.rule_title = Windows Possible Credential Dumping
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage=*\\lsass.exe granted_access IN ("0x01000", "0x1010", "0x1038", "0x40", "0x1400", "0x1fffff", "0x1410", "0x143a", "0x1438", "0x1000") CallTrace IN ("*dbgcore.dll*", "*dbghelp.dll*", "*ntdll.dll*", "*kernelbase.dll*", "*kernel32.dll*") NOT SourceUser IN ("NT AUTHORITY\\SYSTEM", "NT AUTHORITY\\NETWORK SERVICE") | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage, GrantedAccess, TargetImage, SourceProcessId, SourceUser, TargetUser | rename SourceUser as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_possible_credential_dumping_filter`

[ESCU - Windows Post Exploitation Risk Behavior - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following correlation identifies a four or more number of distinct analytics associated with the Windows Post-Exploitation analytic story, which enables the identification of potentially suspicious behavior. Windows Post-Exploitation refers to the phase that occurs after an attacker successfully compromises a Windows system. During this stage, attackers strive to maintain persistence, gather sensitive information, escalate privileges, and exploit the compromised environment further. Timely detection of post-exploitation activities is crucial for prompt response and effective mitigation. Common post-exploitation detections encompass identifying suspicious processes or services running on the system, detecting unusual network connections or traffic patterns, identifying modifications to system files or registry entries, monitoring abnormal user account activities, and flagging unauthorized privilege escalations. Ensuring the detection of post-exploitation activities is essential to proactively prevent further compromise, minimize damage, and restore the security of the Windows environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012", "T1049", "T1069", "T1016", "T1003", "T1082", "T1115", "T1552"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following correlation identifies a four or more number of distinct analytics associated with the Windows Post-Exploitation analytic story, which enables the identification of potentially suspicious behavior. Windows Post-Exploitation refers to the phase that occurs after an attacker successfully compromises a Windows system. During this stage, attackers strive to maintain persistence, gather sensitive information, escalate privileges, and exploit the compromised environment further. Timely detection of post-exploitation activities is crucial for prompt response and effective mitigation. Common post-exploitation detections encompass identifying suspicious processes or services running on the system, detecting unusual network connections or traffic patterns, identifying modifications to system files or registry entries, monitoring abnormal user account activities, and flagging unauthorized privilege escalations. Ensuring the detection of post-exploitation activities is essential to proactively prevent further compromise, minimize damage, and restore the security of the Windows environment.
action.escu.how_to_implement = Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
action.escu.known_false_positives = False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
action.escu.creation_date = 2023-06-14
action.escu.modification_date = 2023-06-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Post Exploitation Risk Behavior - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Post-Exploitation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - Windows Post Exploitation Risk Behavior - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1012", "T1049", "T1069", "T1016", "T1003", "T1082", "T1115", "T1552"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following correlation identifies a four or more number of distinct analytics associated with the Windows Post-Exploitation analytic story, which enables the identification of potentially suspicious behavior. Windows Post-Exploitation refers to the phase that occurs after an attacker successfully compromises a Windows system. During this stage, attackers strive to maintain persistence, gather sensitive information, escalate privileges, and exploit the compromised environment further. Timely detection of post-exploitation activities is crucial for prompt response and effective mitigation. Common post-exploitation detections encompass identifying suspicious processes or services running on the system, detecting unusual network connections or traffic patterns, identifying modifications to system files or registry entries, monitoring abnormal user account activities, and flagging unauthorized privilege escalations. Ensuring the detection of post-exploitation activities is essential to proactively prevent further compromise, minimize damage, and restore the security of the Windows environment.
action.notable.param.rule_title = RBA: Windows Post Exploitation Risk Behavior
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("*Windows Post-Exploitation*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `windows_post_exploitation_risk_behavior_filter`

[ESCU - Windows PowerShell Add Module to Global Assembly Cache - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following PowerShell Script Block analytic identifies the native ability to add a DLL to the Windows Global Assembly Cache. Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. This is native and built into Windows. Per the Microsoft blog, the more high fidelity method may be to look for W3WP.exe spawning PowerShell that includes the same CommandLine as identified in this analytic.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following PowerShell Script Block analytic identifies the native ability to add a DLL to the Windows Global Assembly Cache. Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. This is native and built into Windows. Per the Microsoft blog, the more high fidelity method may be to look for W3WP.exe spawning PowerShell that includes the same CommandLine as identified in this analytic.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may be present based on developers or third party utilities adding items to the GAC.
action.escu.creation_date = 2023-01-18
action.escu.modification_date = 2023-01-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell Add Module to Global Assembly Cache - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["IIS Components"]
action.risk = 1
action.risk.param._risk_message = PowerShell was used to install a module to the Global Assembly Cache on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell Add Module to Global Assembly Cache - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following PowerShell Script Block analytic identifies the native ability to add a DLL to the Windows Global Assembly Cache. Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. This is native and built into Windows. Per the Microsoft blog, the more high fidelity method may be to look for W3WP.exe spawning PowerShell that includes the same CommandLine as identified in this analytic.
action.notable.param.rule_title = Windows PowerShell Add Module to Global Assembly Cache
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN("*system.enterpriseservices.internal.publish*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_add_module_to_global_assembly_cache_filter`

[ESCU - Windows Powershell Cryptography Namespace - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is processing cryptography namespace library. This technique was seen in several powershell malware, loader, downloader and stager that will decrypt or decode the next malicious stager or the actual payload. This Anomaly detection can be a good indicator that a powershell process to decrypt code. We recommend to further check the parent_process_name, the file or data it tries to decrypt, network connection and user who execute the script.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is processing cryptography namespace library. This technique was seen in several powershell malware, loader, downloader and stager that will decrypt or decode the next malicious stager or the actual payload. This Anomaly detection can be a good indicator that a powershell process to decrypt code. We recommend to further check the parent_process_name, the file or data it tries to decrypt, network connection and user who execute the script.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives should be limited. Filter as needed.
action.escu.creation_date = 2023-01-26
action.escu.modification_date = 2023-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Powershell Cryptography Namespace - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains cryptography command in $ScriptBlockText$ with EventCode $EventCode$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Powershell Cryptography Namespace - Rule
action.correlationsearch.annotations = {"analytic_story": ["AsyncRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*System.Security.Cryptography*" AND NOT(ScriptBlockText IN ("*SHA*", "*MD5*", "*DeriveBytes*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_cryptography_namespace_filter`

[ESCU - Windows PowerShell Disable HTTP Logging - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analtyic identifies the use of get-WebConfigurationProperty and Set-ItemProperty attempting to disable HTTP logging on windows via PowerShell. Adversaries may perform this action to disable HTTP logging to cover tracks and delete logs on disk.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562", "T1562.002", "T1505", "T1505.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analtyic identifies the use of get-WebConfigurationProperty and Set-ItemProperty attempting to disable HTTP logging on windows via PowerShell. Adversaries may perform this action to disable HTTP logging to cover tracks and delete logs on disk.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible administrators or scripts may run these commands, filtering may be required.
action.escu.creation_date = 2022-12-21
action.escu.modification_date = 2022-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell Disable HTTP Logging - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["IIS Components", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = A PowerShell Cmdlet related to disable or modifying a IIS HTTP logging has occurred on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell Disable HTTP Logging - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components", "Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562", "T1562.002", "T1505", "T1505.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analtyic identifies the use of get-WebConfigurationProperty and Set-ItemProperty attempting to disable HTTP logging on windows via PowerShell. Adversaries may perform this action to disable HTTP logging to cover tracks and delete logs on disk.
action.notable.param.rule_title = Windows PowerShell Disable HTTP Logging
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN("*get-WebConfigurationProperty*","*Set-ItemProperty*") AND ScriptBlockText IN ("*httpLogging*","*Logfile.enabled*") AND ScriptBlockText IN ("*dontLog*", "*false*")   | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_disable_http_logging_filter`

[ESCU - Windows PowerShell Export Certificate - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the PowerShell Cmdlet export-certificate utilizing Script Block Logging. This particular behavior is related to an adversary attempting to steal certificates local to the Windows endpoint within the Certificate Store.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.004", "T1552", "T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the PowerShell Cmdlet export-certificate utilizing Script Block Logging. This particular behavior is related to an adversary attempting to steal certificates local to the Windows endpoint within the Certificate Store.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible administrators or scripts may run these commands, filtering may be required.
action.escu.creation_date = 2023-02-01
action.escu.modification_date = 2023-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell Export Certificate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = A PowerShell Cmdlet related to exporting a Certificate was ran on $dest$, attempting to export a certificate.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell Export Certificate - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1552.004", "T1552", "T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*export-certificate*") | rename Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_export_certificate_filter`

[ESCU - Windows PowerShell Export PfxCertificate - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the PowerShell Cmdlet export-pfxcertificate utilizing Script Block Logging. This particular behavior is related to an adversary attempting to steal certificates local to the Windows endpoint within the Certificate Store.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.004", "T1552", "T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the PowerShell Cmdlet export-pfxcertificate utilizing Script Block Logging. This particular behavior is related to an adversary attempting to steal certificates local to the Windows endpoint within the Certificate Store.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible administrators or scripts may run these commands, filtering may be required.
action.escu.creation_date = 2023-02-01
action.escu.modification_date = 2023-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell Export PfxCertificate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = A PowerShell Cmdlet related to exporting a PFX Certificate was ran on $dest$, attempting to export a certificate.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell Export PfxCertificate - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1552.004", "T1552", "T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*export-pfxcertificate*") | rename Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_export_pfxcertificate_filter`

[ESCU - Windows PowerShell Get CIMInstance Remote Computer - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the use of Get-CimInstance cmdlet with the -ComputerName parameter, which indicates that the cmdlet is being used to retrieve information from a remote computer. This can be useful for detecting instances of remote access, such as when an attacker uses PowerShell to connect to a remote system and gather information. By monitoring for this cmdlet with the -ComputerName parameter, security analysts can identify potential malicious activity on remote systems and take appropriate action to mitigate any threats.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the use of Get-CimInstance cmdlet with the -ComputerName parameter, which indicates that the cmdlet is being used to retrieve information from a remote computer. This can be useful for detecting instances of remote access, such as when an attacker uses PowerShell to connect to a remote system and gather information. By monitoring for this cmdlet with the -ComputerName parameter, security analysts can identify potential malicious activity on remote systems and take appropriate action to mitigate any threats.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = This is meant to be a low risk RBA anomaly analytic or to be used for hunting. Enable this with a low risk score and let it generate risk in the risk index.
action.escu.creation_date = 2023-03-27
action.escu.modification_date = 2023-03-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell Get CIMInstance Remote Computer - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A PowerShell Cmdlet Get-CIMInstnace was ran on $Computer$, attempting to connect to a remote host.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell Get CIMInstance Remote Computer - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1059.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*get-ciminstance*" AND ScriptBlockText="*computername*"  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_get_ciminstance_remote_computer_filter`

[ESCU - Windows PowerShell IIS Components WebGlobalModule Usage - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the usage of PowerShell Cmdlets - New-WebGlobalModule, Enable-WebGlobalModule and Set-WebGlobalModule being utilized to create (new), enable (start) or modify a current IIS Module. These commands are equivalent to AppCmd.exe parameters. Adversaries may utilize these cmdlets as they are lesser known and perform the same activity as AppCmd.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies the usage of PowerShell Cmdlets - New-WebGlobalModule, Enable-WebGlobalModule and Set-WebGlobalModule being utilized to create (new), enable (start) or modify a current IIS Module. These commands are equivalent to AppCmd.exe parameters. Adversaries may utilize these cmdlets as they are lesser known and perform the same activity as AppCmd.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = It is possible administrators or scripts may run these commands, filtering may be required.
action.escu.creation_date = 2022-12-21
action.escu.modification_date = 2022-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell IIS Components WebGlobalModule Usage - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["IIS Components"]
action.risk = 1
action.risk.param._risk_message = A PowerShell Cmdlet related to enabling, creating or modifying a IIS module has occurred on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell IIS Components WebGlobalModule Usage - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN("*New-WebGlobalModule*","*Enable-WebGlobalModule*","*Set-WebGlobalModule*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  | `windows_powershell_iis_components_webglobalmodule_usage_filter`

[ESCU - Windows Powershell Import Applocker Policy - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify the imports of Windows PowerShell Applocker commandlets. This technique was seen in Azorult malware where it drops an xml Applocker policy that will deny several AV product and then loaded using PowerShell Applocker commandlet.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059", "T1562.001", "T1562"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic is to identify the imports of Windows PowerShell Applocker commandlets. This technique was seen in Azorult malware where it drops an xml Applocker policy that will deny several AV product and then loaded using PowerShell Applocker commandlet.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = administrators may execute this command that may cause some false positive.
action.escu.creation_date = 2022-06-30
action.escu.modification_date = 2022-06-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Powershell Import Applocker Policy - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = A PowerShell script contains Import Applocker Policy command $ScriptBlockText$ with EventCode $EventCode$ on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Powershell Import Applocker Policy - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1059.001", "T1059", "T1562.001", "T1562"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is to identify the imports of Windows PowerShell Applocker commandlets. This technique was seen in Azorult malware where it drops an xml Applocker policy that will deny several AV product and then loaded using PowerShell Applocker commandlet.
action.notable.param.rule_title = Windows Powershell Import Applocker Policy
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*Import-Module Applocker*" ScriptBlockText="*Set-AppLockerPolicy *" ScriptBlockText="* -XMLPolicy *" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_import_applocker_policy_filter`

[ESCU - Windows Powershell RemoteSigned File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the use of "remotesigned" execution policy for a file. This security setting determines whether PowerShell scripts can be executed on a computer. When the execution policy is set to "remotesigned," it allows locally created scripts to run without any restrictions, but scripts downloaded from the internet must have a digital signature from a trusted publisher.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies the use of "remotesigned" execution policy for a file. This security setting determines whether PowerShell scripts can be executed on a computer. When the execution policy is set to "remotesigned," it allows locally created scripts to run without any restrictions, but scripts downloaded from the internet must have a digital signature from a trusted publisher.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible administrators or scripts may run these commands, filtering may be required.
action.escu.creation_date = 2023-06-16
action.escu.modification_date = 2023-06-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Powershell RemoteSigned File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Amadey"]
action.risk = 1
action.risk.param._risk_message = A PowerShell commandline with remotesigned policy executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Powershell RemoteSigned File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Amadey"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="* remotesigned *" Processes.process="* -File *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_remotesigned_file_filter`

[ESCU - Windows PowerShell ScheduleTask - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential malicious activities related to PowerShell's task scheduling cmdlets. It looks for anomalies in PowerShell logs, specifically EventCode 4104, associated with script block logging. The analytic flags unusual or suspicious use patterns of key task-related cmdlets such as 'New-ScheduledTask', 'Set-ScheduledTask', and others, which are often used by attackers for persistence and remote execution of malicious code. If a true positive is found, it suggests an possible attacker is attempting to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. To implement this analytic, PowerShell Script Block Logging needs to be enabled on some or all endpoints. Analysts should be aware of benign administrative tasks that can trigger alerts and tune the analytic accordingly to reduce false positives. Upon triage, review the PowerShell logs for any unusual or unexpected cmdlet usage, IP addresses, user accounts, or timestamps. If these factors align with known malicious behavior patterns, immediate mitigation steps, such as isolation of the affected systems, user account changes, and relevant threat hunting activities, should be initiated. This proactive analysis significantly enhances an organization's capacity to swiftly respond to, and potentially prevent, the execution of advanced persistent threats in their network.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1059.001", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects potential malicious activities related to PowerShell's task scheduling cmdlets. It looks for anomalies in PowerShell logs, specifically EventCode 4104, associated with script block logging. The analytic flags unusual or suspicious use patterns of key task-related cmdlets such as 'New-ScheduledTask', 'Set-ScheduledTask', and others, which are often used by attackers for persistence and remote execution of malicious code. If a true positive is found, it suggests an possible attacker is attempting to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. To implement this analytic, PowerShell Script Block Logging needs to be enabled on some or all endpoints. Analysts should be aware of benign administrative tasks that can trigger alerts and tune the analytic accordingly to reduce false positives. Upon triage, review the PowerShell logs for any unusual or unexpected cmdlet usage, IP addresses, user accounts, or timestamps. If these factors align with known malicious behavior patterns, immediate mitigation steps, such as isolation of the affected systems, user account changes, and relevant threat hunting activities, should be initiated. This proactive analysis significantly enhances an organization's capacity to swiftly respond to, and potentially prevent, the execution of advanced persistent threats in their network.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Benign administrative tasks can also trigger alerts, necessitating a firm understanding of the typical system behavior and precise tuning of the analytic to reduce false positives.
action.escu.creation_date = 2023-06-12
action.escu.modification_date = 2023-06-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell ScheduleTask - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = The PowerShell cmdlets related to task creation, modification and start occurred on $Computer$ by $user_id$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user_id", "risk_object_type": "user", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell ScheduleTask - Rule
action.correlationsearch.annotations = {"analytic_story": ["Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1053.005", "T1059.001", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText IN ("*New-ScheduledTask*", "*New-ScheduledTaskAction*", "*New-ScheduledTaskSettingsSet*", "*New-ScheduledTaskTrigger*", "*Register-ClusteredScheduledTask*", "*Register-ScheduledTask*", "*Set-ClusteredScheduledTask*", "*Set-ScheduledTask*", "*Start-ScheduledTask*", "*Enable-ScheduledTask*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_scheduletask_filter`

[ESCU - Windows PowerShell WMI Win32 ScheduledJob - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the use of the PowerShell script block logging mechanism to detect the use of the Win32_ScheduledJob WMI class. This class allows the creation and management of scheduled tasks on Windows systems. However, due to security concerns, the class has been disabled by default in Windows systems, and its use must be explicitly enabled by modifying the registry. As a result, the detection of the use of this class may indicate malicious activity, especially if the class was enabled on the system by the attacker. Therefore, it is recommended to monitor the use of Win32_ScheduledJob through PowerShell script block logging and to investigate any suspicious activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the use of the PowerShell script block logging mechanism to detect the use of the Win32_ScheduledJob WMI class. This class allows the creation and management of scheduled tasks on Windows systems. However, due to security concerns, the class has been disabled by default in Windows systems, and its use must be explicitly enabled by modifying the registry. As a result, the detection of the use of this class may indicate malicious activity, especially if the class was enabled on the system by the attacker. Therefore, it is recommended to monitor the use of Win32_ScheduledJob through PowerShell script block logging and to investigate any suspicious activity.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = False positives may be present based on legacy applications or utilities. Win32_ScheduledJob uses the Remote Procedure Call (RPC) protocol to create scheduled tasks on remote computers. It uses the DCOM (Distributed Component Object Model) infrastructure to establish a connection with the remote computer and invoke the necessary methods. The RPC service needs to be running on both the local and remote computers for the communication to take place.
action.escu.creation_date = 2023-03-27
action.escu.modification_date = 2023-03-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerShell WMI Win32 ScheduledJob - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = PowerShell attempting to create a task via WMI - Win32_ScheduledJob,  was ran on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerShell WMI Win32 ScheduledJob - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 80, "mitre_attack": ["T1059.001", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the use of the PowerShell script block logging mechanism to detect the use of the Win32_ScheduledJob WMI class. This class allows the creation and management of scheduled tasks on Windows systems. However, due to security concerns, the class has been disabled by default in Windows systems, and its use must be explicitly enabled by modifying the registry. As a result, the detection of the use of this class may indicate malicious activity, especially if the class was enabled on the system by the attacker. Therefore, it is recommended to monitor the use of Win32_ScheduledJob through PowerShell script block logging and to investigate any suspicious activity.
action.notable.param.rule_title = Windows PowerShell WMI Win32 ScheduledJob
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText="*win32_scheduledjob*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_wmi_win32_scheduledjob_filter`

[ESCU - Windows PowerSploit GPP Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of the Get-GPPPassword PowerShell commandlet employed to search for unsecured credentials Group Policy Preferences (GPP). GPP are tools that allow administrators to create domain policies with embedded credentials. These policies allow administrators to set local accounts. These group policies are stored in SYSVOL on a domain controller. This means that any domain user can view the SYSVOL share and decrypt the password (using the AES key that has been made public). While Microsoft released a patch that impedes Administrators to create unsecure credentials, existing Group Policy Preferences files with passwords are not removed from SYSVOL.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552", "T1552.006"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the use of the Get-GPPPassword PowerShell commandlet employed to search for unsecured credentials Group Policy Preferences (GPP). GPP are tools that allow administrators to create domain policies with embedded credentials. These policies allow administrators to set local accounts. These group policies are stored in SYSVOL on a domain controller. This means that any domain user can view the SYSVOL share and decrypt the password (using the AES key that has been made public). While Microsoft released a patch that impedes Administrators to create unsecure credentials, existing Group Policy Preferences files with passwords are not removed from SYSVOL.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2023-03-16
action.escu.modification_date = 2023-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerSploit GPP Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = Commandlets leveraged to discover GPP credentials were executed on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "UserID", "risk_object_type": "user", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerSploit GPP Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1552", "T1552.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of the Get-GPPPassword PowerShell commandlet employed to search for unsecured credentials Group Policy Preferences (GPP). GPP are tools that allow administrators to create domain policies with embedded credentials. These policies allow administrators to set local accounts. These group policies are stored in SYSVOL on a domain controller. This means that any domain user can view the SYSVOL share and decrypt the password (using the AES key that has been made public). While Microsoft released a patch that impedes Administrators to create unsecure credentials, existing Group Policy Preferences files with passwords are not removed from SYSVOL.
action.notable.param.rule_title = Windows PowerSploit GPP Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `powershell` EventCode=4104  (ScriptBlockText=Get-GPPPassword OR ScriptBlockText=Get-CachedGPPPassword) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powersploit_gpp_discovery_filter`

[ESCU - Windows PowerView AD Access Control List Enumeration - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event ID 4104 to identify the execution of the PowerView powershell commandlets `Get-ObjectAcl` or `Get-DomainObjectAcl`. This commandlets are used to enumerate Access Control List permissions given to Active Directory objects. In an active directory environment, an object is an entity that represents an available resource within the organizations network, such as domain controllers, users, groups, computers, shares, etc.  Maintaining Active Directory permissions is complicated and hard to manage, especially in complex and large environments with multiple domains. Weak permissions may allow adversaries and red teamers to escalate their privileges in Active Directory. PowerView is a common tool leveraged by attackers to identify and exploit configuration weaknesses.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1078.002", "T1069"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event ID 4104 to identify the execution of the PowerView powershell commandlets `Get-ObjectAcl` or `Get-DomainObjectAcl`. This commandlets are used to enumerate Access Control List permissions given to Active Directory objects. In an active directory environment, an object is an entity that represents an available resource within the organizations network, such as domain controllers, users, groups, computers, shares, etc.  Maintaining Active Directory permissions is complicated and hard to manage, especially in complex and large environments with multiple domains. Weak permissions may allow adversaries and red teamers to escalate their privileges in Active Directory. PowerView is a common tool leveraged by attackers to identify and exploit configuration weaknesses.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
action.escu.known_false_positives = Administrators may leverage PowerView for legitimate purposes, filter as needed.
action.escu.creation_date = 2023-04-20
action.escu.modification_date = 2023-04-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerView AD Access Control List Enumeration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Discovery", "Active Directory Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = PowerView AD acccess control list enumeration detected on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerView AD Access Control List Enumeration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery", "Active Directory Privilege Escalation"], "cis20": ["CIS 10"], "confidence": 50, "impact": 40, "mitre_attack": ["T1078.002", "T1069"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event ID 4104 to identify the execution of the PowerView powershell commandlets `Get-ObjectAcl` or `Get-DomainObjectAcl`. This commandlets are used to enumerate Access Control List permissions given to Active Directory objects. In an active directory environment, an object is an entity that represents an available resource within the organizations network, such as domain controllers, users, groups, computers, shares, etc.  Maintaining Active Directory permissions is complicated and hard to manage, especially in complex and large environments with multiple domains. Weak permissions may allow adversaries and red teamers to escalate their privileges in Active Directory. PowerView is a common tool leveraged by attackers to identify and exploit configuration weaknesses.
action.notable.param.rule_title = Windows PowerView AD Access Control List Enumeration
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `powershell` EventCode=4104  (ScriptBlockText=*get-objectacl* OR ScriptBlockText=*Get-DomainObjectAcl* ) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_ad_access_control_list_enumeration_filter`

[ESCU - Windows PowerView Constrained Delegation Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify commandlets used by the PowerView hacking tool leveraged to discover Windows endpoints with Kerberos Constrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify commandlets used by the PowerView hacking tool leveraged to discover Windows endpoints with Kerberos Constrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following  analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may leverage PowerView for system management or troubleshooting.
action.escu.creation_date = 2022-03-31
action.escu.modification_date = 2022-03-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerView Constrained Delegation Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-DomainComputer was identified on endpoint $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerView Constrained Delegation Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify commandlets used by the PowerView hacking tool leveraged to discover Windows endpoints with Kerberos Constrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Windows PowerView Constrained Delegation Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (Message = "*Get-DomainComputer*" OR Message = "*Get-NetComputer*") AND (Message = "*-TrustedToAuth*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_constrained_delegation_discovery_filter`

[ESCU - Windows PowerView Kerberos Service Ticket Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainSPNTicket` commandlets with specific parameters. This commandlet is a part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the name suggests, this commandlet is used to request the kerberos ticket for a specified service principal name (SPN). Once the ticket is received, it may be cracked using password cracking tools like hashcat to extract the password of the SPN account. Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainSPNTicket` commandlets with specific parameters. This commandlet is a part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the name suggests, this commandlet is used to request the kerberos ticket for a specified service principal name (SPN). Once the ticket is received, it may be cracked using password cracking tools like hashcat to extract the password of the SPN account. Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
action.escu.how_to_implement = The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = False positive may include Administrators using PowerView for troubleshooting and management.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerView Kerberos Service Ticket Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = PowerView commandlets used for requesting SPN service ticket executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 27}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerView Kerberos Service Ticket Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 30, "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainSPNTicket` commandlets with specific parameters. This commandlet is a part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the name suggests, this commandlet is used to request the kerberos ticket for a specified service principal name (SPN). Once the ticket is received, it may be cracked using password cracking tools like hashcat to extract the password of the SPN account. Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
action.notable.param.rule_title = Windows PowerView Kerberos Service Ticket Request
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText=*Get-DomainSPNTicket* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_kerberos_service_ticket_request_filter`

[ESCU - Windows PowerView SPN Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` or `Get-NetUSer` commandlets with specific parameters. These commandlets are part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the names suggest, these commandlets are used to identify domain users in a network and combining them with the `-SPN` parameter allows adversaries to discover domain accounts associated with a Service Principal Name (SPN). Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` or `Get-NetUSer` commandlets with specific parameters. These commandlets are part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the names suggest, these commandlets are used to identify domain users in a network and combining them with the `-SPN` parameter allows adversaries to discover domain accounts associated with a Service Principal Name (SPN). Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
action.escu.how_to_implement = The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = False positive may include Administrators using PowerView for troubleshooting and management.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerView SPN Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = PowerView commandlets used for SPN discovery executed on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 27}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerView SPN Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 90, "impact": 30, "mitre_attack": ["T1558", "T1558.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the `Get-DomainUser` or `Get-NetUSer` commandlets with specific parameters. These commandlets are part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the names suggest, these commandlets are used to identify domain users in a network and combining them with the `-SPN` parameter allows adversaries to discover domain accounts associated with a Service Principal Name (SPN). Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
action.notable.param.rule_title = Windows PowerView SPN Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (ScriptBlockText =*Get-NetUser* OR ScriptBlockText=*Get-DomainUser*) ScriptBlockText= *-SPN* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_powerview_spn_discovery_filter`

[ESCU - Windows PowerView Unconstrained Delegation Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify commandlets used by the PowerView hacking tool leveraged to discover Windows endpoints with Kerberos Unconstrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify commandlets used by the PowerView hacking tool leveraged to discover Windows endpoints with Kerberos Unconstrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following  analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may leverage PowerView for system management or troubleshooting.
action.escu.creation_date = 2022-03-28
action.escu.modification_date = 2022-03-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows PowerView Unconstrained Delegation Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Kerberos Attacks"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-DomainComputer was identified on endpoint $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 35}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 35}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows PowerView Unconstrained Delegation Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Kerberos Attacks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 50, "mitre_attack": ["T1018"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify commandlets used by the PowerView hacking tool leveraged to discover Windows endpoints with Kerberos Unconstrained Delegation. Red Teams and adversaries alike may leverage use this technique for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Windows PowerView Unconstrained Delegation Discovery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 (Message = "*Get-DomainComputer*" OR Message = "*Get-NetComputer*") AND (Message = "*-Unconstrained*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_unconstrained_delegation_discovery_filter`

[ESCU - Windows Private Keys Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line that retrieves information related to private keys files. This technique was seen in several post exploitation tools like winpeas that are being used by Ransomware Prestige to search for private key certificates on the compromised host for insecurely stored credentials. This files can be used by adversaries to gain privileges, persistence or remote service authentication to collect more sensitive information. Some private keys required password for operation, so in this case adversaries may need to have that passphrase either via keylogging or brute force attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1552.004", "T1552"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line that retrieves information related to private keys files. This technique was seen in several post exploitation tools like winpeas that are being used by Ransomware Prestige to search for private key certificates on the compromised host for insecurely stored credentials. This files can be used by adversaries to gain privileges, persistence or remote service authentication to collect more sensitive information. Some private keys required password for operation, so in this case adversaries may need to have that passphrase either via keylogging or brute force attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Private Keys Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = a process with commandline $process$ that can retrieve information related to private keys in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Private Keys Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1552.004", "T1552"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *" OR  Processes.process = "*findstr*" AND Processes.process IN ( "*.rdg*", "*.gpg*", "*.pgp*", "*.p12*", "*.der*", "*.csr*", "*.cer*", "*.ovpn*", "*.key*",  "*.ppk*", "*.p12*", "*.pem*", "*.pfx*", "*.p7b*", "*.asc*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_private_keys_discovery_filter`

[ESCU - Windows Process Injection into Notepad - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Sysmon to identify process injection into Notepad.exe, based on GrantedAccess requests - 0x40 and 0x1fffff. This particular behavior is attributed to the defaults of the SliverC2 framework by BishopFox. By default, the analytic filters out any SourceImage paths of System32, Syswow64 and program files. Add more as needed, or remove and monitor what is consistently injecting into notepad.exe. This particular behavior will occur from a source image that is the initial payload dropped.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055", "T1055.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Sysmon to identify process injection into Notepad.exe, based on GrantedAccess requests - 0x40 and 0x1fffff. This particular behavior is attributed to the defaults of the SliverC2 framework by BishopFox. By default, the analytic filters out any SourceImage paths of System32, Syswow64 and program files. Add more as needed, or remove and monitor what is consistently injecting into notepad.exe. This particular behavior will occur from a source image that is the initial payload dropped.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = False positives may be present based on SourceImage paths. If removing the paths is important, realize svchost and many native binaries inject into notepad consistently. Restrict or tune as needed.
action.escu.creation_date = 2023-02-22
action.escu.modification_date = 2023-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Process Injection into Notepad - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["BishopFox Sliver Adversary Emulation Framework"]
action.risk = 1
action.risk.param._risk_message = An instance of $SourceImage$ injecting into $TargetImage$ was identified on endpoint $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 32}, {"risk_object_field": "SourceImage", "risk_object_type": "other", "risk_score": 32}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 32}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Process Injection into Notepad - Rule
action.correlationsearch.annotations = {"analytic_story": ["BishopFox Sliver Adversary Emulation Framework"], "cis20": ["CIS 10"], "confidence": 80, "impact": 40, "mitre_attack": ["T1055", "T1055.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage IN (*\\notepad.exe) NOT (SourceImage IN ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*")) GrantedAccess IN ("0x40","0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by dest SourceImage TargetImage GrantedAccess CallTrace | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_into_notepad_filter`

[ESCU - Windows Process Injection Of Wermgr to Known Browser - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies the suspicious Remote Thread execution of wermgr.exe process to "firefox.exe", "chrome.exe" and other known browsers. This technique was seen in Qakbot malware that executes its malicious code by injecting its code in legitimate Windows Operating System processes such as wermgr.exe to steal information in the compromised host. This TTP detection can be a good pivot to detect wermgr.exe process injected with qakbot code that tries to remote thread code execution in known browsers like firefox and edge which is not a common behavior of this wermgr.exe application.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055.001", "T1055"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the suspicious Remote Thread execution of wermgr.exe process to "firefox.exe", "chrome.exe" and other known browsers. This technique was seen in Qakbot malware that executes its malicious code by injecting its code in legitimate Windows Operating System processes such as wermgr.exe to steal information in the compromised host. This TTP detection can be a good pivot to detect wermgr.exe process injected with qakbot code that tries to remote thread code execution in known browsers like firefox and edge which is not a common behavior of this wermgr.exe application.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-28
action.escu.modification_date = 2022-10-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Process Injection Of Wermgr to Known Browser - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = wermgr.exe process $SourceImage$ create a remote thread to a browser process $TargetImage$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Process Injection Of Wermgr to Known Browser - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055.001", "T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies the suspicious Remote Thread execution of wermgr.exe process to "firefox.exe", "chrome.exe" and other known browsers. This technique was seen in Qakbot malware that executes its malicious code by injecting its code in legitimate Windows Operating System processes such as wermgr.exe to steal information in the compromised host. This TTP detection can be a good pivot to detect wermgr.exe process injected with qakbot code that tries to remote thread code execution in known browsers like firefox and edge which is not a common behavior of this wermgr.exe application.
action.notable.param.rule_title = Windows Process Injection Of Wermgr to Known Browser
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=8 SourceImage = "*\\wermgr.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGuid SourceProcessId StartAddress StartFunction TargetProcessGuid TargetProcessId  EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_of_wermgr_to_known_browser_filter`

[ESCU - Windows Process Injection Remote Thread - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious remote thread execution in some process being abused by threat actor and malware like qakbot. Qakbot is one of the malware using this technique to load its malicious dll module or malicious code in the targeted host. This TTP can be a good pivot to verify what is the behavior of the targeted Image process after this detection trigger. look for network connection, child process execution, file access and many more that helps to verify the indication of malware infection.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055", "T1055.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious remote thread execution in some process being abused by threat actor and malware like qakbot. Qakbot is one of the malware using this technique to load its malicious dll module or malicious code in the targeted host. This TTP can be a good pivot to verify what is the behavior of the targeted Image process after this detection trigger. look for network connection, child process execution, file access and many more that helps to verify the indication of malware infection.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts like remote thread EventCode=8 of sysmon. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-06-15
action.escu.modification_date = 2023-06-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Process Injection Remote Thread - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot", "Graceful Wipe Out Attack", "Warzone RAT"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ create a remote thread to process $TargetImage$ on host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "SourceImage", "threat_object_type": "process"}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Process Injection Remote Thread - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot", "Graceful Wipe Out Attack", "Warzone RAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1055", "T1055.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a suspicious remote thread execution in some process being abused by threat actor and malware like qakbot. Qakbot is one of the malware using this technique to load its malicious dll module or malicious code in the targeted host. This TTP can be a good pivot to verify what is the behavior of the targeted Image process after this detection trigger. look for network connection, child process execution, file access and many more that helps to verify the indication of malware infection.
action.notable.param.rule_title = Windows Process Injection Remote Thread
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=8 TargetImage IN ("*\\Taskmgr.exe", "*\\calc.exe", "*\\notepad.exe", "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe","*\\cmd.exe", "*\\powershell.exe") | stats count min(_time) as firstTime max(_time) as lastTime by  TargetImage TargetProcessId SourceProcessId  EventCode StartAddress SourceImage dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_remote_thread_filter`

[ESCU - Windows Process Injection Wermgr Child Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious wermgr.exe parent process having a child process not related to error, fault or windows werfault event. This technique was seen in Qakbot malware where it inject its malicious code in wermgr to evade detections and hide from the analyst to execute its recon and its malicious behavior. This Anomaly detection can be a good pivot to start investigating a possible qakbot infection in the network. The Wermgr.exe process is not known to have other child processes aside from itself or werfault.exe
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a suspicious wermgr.exe parent process having a child process not related to error, fault or windows werfault event. This technique was seen in Qakbot malware where it inject its malicious code in wermgr to evade detections and hide from the analyst to execute its recon and its malicious behavior. This Anomaly detection can be a good pivot to start investigating a possible qakbot infection in the network. The Wermgr.exe process is not known to have other child processes aside from itself or werfault.exe
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-27
action.escu.modification_date = 2022-10-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Process Injection Wermgr Child Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot", "Windows Error Reporting Service Elevation of Privilege Vulnerability"]
action.risk = 1
action.risk.param._risk_message = wermgr parent process has a child process $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Process Injection Wermgr Child Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot", "Windows Error Reporting Service Elevation of Privilege Vulnerability"], "cis20": ["CIS 10"], "confidence": 70, "impact": 80, "mitre_attack": ["T1055"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" AND NOT (Processes.process_name IN ("WerFaultSecure.exe", "wermgr.exe", "WerFault.exe")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_wermgr_child_process_filter`

[ESCU - Windows Process Injection With Public Source Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process in a non-standard file path on Windows attempting to create a remote thread into a process. This Windows API,CreateRemoteThread, is commonly used by adversaries for process injection to evade detections or gain privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055", "T1055.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a process in a non-standard file path on Windows attempting to create a remote thread into a process. This Windows API,CreateRemoteThread, is commonly used by adversaries for process injection to evade detections or gain privilege escalation.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Some security products or third party applications may utilize CreateRemoteThread, filter as needed before enabling as a notable.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Process Injection With Public Source Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Process Injection With Public Source Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1055", "T1055.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=8 TargetImage = "*.exe" AND NOT(SourceImage IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage signature TargetProcessGuid SourceProcessGuid TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_with_public_source_path_filter`

[ESCU - Windows Process With NamedPipe CommandLine - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for process commandline that contains named pipe. This technique was seen in some adversaries, threat actor and malware like olympic destroyer to communicate to its other child processes after process injection that serve as defense evasion and privilege escalation. On the other hand this analytic may catch some normal process that using this technique for example browser application. In that scenario we include common process path we've seen during testing that cause false positive which is the program files. False positive may still be arise if the normal application is in other folder path.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for process commandline that contains named pipe. This technique was seen in some adversaries, threat actor and malware like olympic destroyer to communicate to its other child processes after process injection that serve as defense evasion and privilege escalation. On the other hand this analytic may catch some normal process that using this technique for example browser application. In that scenario we include common process path we've seen during testing that cause false positive which is the program files. False positive may still be arise if the normal application is in other folder path.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Normal browser application may use this technique. Please update the filter macros to remove false positives.
action.escu.creation_date = 2022-02-23
action.escu.modification_date = 2022-02-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Process With NamedPipe CommandLine - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Process with named pipe in $process$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Process With NamedPipe CommandLine - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\\\.\\pipe\\*" NOT (Processes.process_path IN ("*\\program files*")) by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_path Processes.process_guid Processes.parent_process_id Processes.dest Processes.user Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_with_namedpipe_commandline_filter`

[ESCU - Windows Processes Killed By Industroyer2 Malware - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to look for known processes killed by industroyer2 malware. This technique was seen in the industroyer2 malware attack that tries to kill several processes of windows host machines related to the energy facility network. This anomaly might be a good indicator to check which process kill these processes or why the process was killed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic is to look for known processes killed by industroyer2 malware. This technique was seen in the industroyer2 malware attack that tries to kill several processes of windows host machines related to the energy facility network. This anomaly might be a good indicator to check which process kill these processes or why the process was killed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = False positives are possible if legitimate applications are allowed to terminate this process during testing or updates. Filter as needed based on paths that are used legitimately.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Processes Killed By Industroyer2 Malware - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = process was terminated $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Processes Killed By Industroyer2 Malware - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=5 process_name IN ("PServiceControl.exe", "PService_PPD.exe") | stats min(_time) as firstTime max(_time) as lastTime count by process_name process process_path process_guid process_id EventCode dest user_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_processes_killed_by_industroyer2_malware_filter`

[ESCU - Windows Protocol Tunneling with Plink - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Plink being utilized to proxy egress or laterally in an organization. The analytic is limited to specific Plink options on the command-line, including -R -L and -D which will have the remote and local IP address or port and -l for a username. Modify the options as seen fit for your organization.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1572", "T1021.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Plink being utilized to proxy egress or laterally in an organization. The analytic is limited to specific Plink options on the command-line, including -R -L and -D which will have the remote and local IP address or port and -l for a username. Modify the options as seen fit for your organization.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present if the organization allows for SSH tunneling outbound or internally. Filter as needed.
action.escu.creation_date = 2022-09-15
action.escu.modification_date = 2022-09-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Protocol Tunneling with Plink - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["CISA AA22-257A"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to tunnel to a remote destination.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 56}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Protocol Tunneling with Plink - Rule
action.correlationsearch.annotations = {"analytic_story": ["CISA AA22-257A"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1572", "T1021.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of Plink being utilized to proxy egress or laterally in an organization. The analytic is limited to specific Plink options on the command-line, including -R -L and -D which will have the remote and local IP address or port and -l for a username. Modify the options as seen fit for your organization.
action.notable.param.rule_title = Windows Protocol Tunneling with Plink
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=plink.exe OR Processes.original_file_name=Plink Processes.process IN ("*-R *", "*-L *", "*-D *", "*-l *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_protocol_tunneling_with_plink_filter`

[ESCU - Windows Proxy Via Netsh - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes launching netsh.exe for connection proxy. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1090.001", "T1090"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes launching netsh.exe for connection proxy. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.
action.escu.creation_date = 2023-05-25
action.escu.modification_date = 2023-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Proxy Via Netsh - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ has launched netsh with command-line $process$ on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Proxy Via Netsh - Rule
action.correlationsearch.annotations = {"analytic_story": ["Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1090.001", "T1090"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process = "* portproxy *" Processes.process = "* v4tov4 *" by Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_proxy_via_netsh_filter`

[ESCU - Windows Proxy Via Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes launching netsh.exe for connection proxy. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1090.001", "T1090"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for processes launching netsh.exe for connection proxy. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-05-25
action.escu.modification_date = 2023-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Proxy Via Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = A registry modification for port proxy in$dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Proxy Via Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1090.001", "T1090"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.action Registry.dest  Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_proxy_via_registry_filter`

[ESCU - Windows Query Registry Browser List Application - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious process accessing default internet browsers registry entry. This registry is used by Windows to store information about default internet browsers installed on a system. Malware, adversaries or red-teamers can abuse this registry key to collect data about the installed internet browsers and their associated settings. This information can be used to steal sensitive data such as login credentials, browsing history, and saved passwords. We observed noise that needs to be filter out so we add several known path of Windows Application to make this detection more stable.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious process accessing default internet browsers registry entry. This registry is used by Windows to store information about default internet browsers installed on a system. Malware, adversaries or red-teamers can abuse this registry key to collect data about the installed internet browsers and their associated settings. This information can be used to steal sensitive data such as login credentials, browsing history, and saved passwords. We observed noise that needs to be filter out so we add several known path of Windows Application to make this detection more stable.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = uninstall application may access this registry to remove the entry of the target application. filter is needed.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Query Registry Browser List Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A suspicious process accessing installed default browser registry on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Query Registry Browser List Application - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 object_file_path IN ("*\\SOFTWARE\\Clients\\StartMenuInternet\\*", "*\\SOFTWARE\\Clients\\StartMenuInternet\\*") AND NOT (process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_browser_list_application_filter`

[ESCU - Windows Query Registry Reg Save - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of reg.exe with "save" parameter. This reg.exe parameter is commonly being abused by threat actors, adversaries and red-teamers to dump credentials or to check the registry modification capabilities of certain users or administrators in targeted hosts. This approach was seen in post-exploitation tool like winpeas where it uses "reg save" and "reg restore" to check registry modification restriction in targeted host after gaining access to it.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of reg.exe with "save" parameter. This reg.exe parameter is commonly being abused by threat actors, adversaries and red-teamers to dump credentials or to check the registry modification capabilities of certain users or administrators in targeted hosts. This approach was seen in post-exploitation tool like winpeas where it uses "reg save" and "reg restore" to check registry modification restriction in targeted host after gaining access to it.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network administrator can use this command tool to backup registry before updates or modifying critical registries.
action.escu.creation_date = 2022-12-12
action.escu.modification_date = 2022-12-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Query Registry Reg Save - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Query Registry Reg Save - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process = "* save *" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_reg_save_filter`

[ESCU - Windows Query Registry UnInstall Program List - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a suspicious query on uninstall application list in Windows OS registry. This registry  is commonly used by legitimate software to store information about installed applications on a Windows system, such as their name, version, publisher, and installation path. However, malware, adversaries or even red-teamers can abuse this registry key to retrieve information stored in the "Uninstall" key to gather data about installed applications in the target host. This Anomaly detection can be a good pivot to detect a possible suspicious process accessing this registry which is not commonly accessed by a normal user.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a suspicious query on uninstall application list in Windows OS registry. This registry  is commonly used by legitimate software to store information about installed applications on a Windows system, such as their name, version, publisher, and installation path. However, malware, adversaries or even red-teamers can abuse this registry key to retrieve information stored in the "Uninstall" key to gather data about installed applications in the target host. This Anomaly detection can be a good pivot to detect a possible suspicious process accessing this registry which is not commonly accessed by a normal user.
action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
action.escu.known_false_positives = Uninstall application may access this registry to remove the entry of the target application. Filter is needed.
action.escu.creation_date = 2023-04-25
action.escu.modification_date = 2023-04-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Query Registry UnInstall Program List - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = A suspicious process $process_name$ accessing uninstall registry on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Query Registry UnInstall Program List - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1012"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4663 object_file_path="\\REGISTRY\\MACHINE\\SOFTWARE\\WOW6432Node\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*" | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_uninstall_program_list_filter`

[ESCU - Windows Raccine Scheduled Task Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Raccine Rules Updater scheduled task being deleted. Adversaries may attempt to remove this task in order to prevent the update of Raccine.  Raccine is a "ransomware vaccine" created by security researcher Florian Roth, designed to intercept and prevent precursors and active ransomware behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Raccine Rules Updater scheduled task being deleted. Adversaries may attempt to remove this task in order to prevent the update of Raccine.  Raccine is a "ransomware vaccine" created by security researcher Florian Roth, designed to intercept and prevent precursors and active ransomware behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, however filter as needed.
action.escu.creation_date = 2021-12-07
action.escu.modification_date = 2021-12-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Raccine Scheduled Task Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user user$ attempting to disable Raccines scheduled task.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Raccine Scheduled Task Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Raccine Rules Updater scheduled task being deleted. Adversaries may attempt to remove this task in order to prevent the update of Raccine.  Raccine is a "ransomware vaccine" created by security researcher Florian Roth, designed to intercept and prevent precursors and active ransomware behavior.
action.notable.param.rule_title = Windows Raccine Scheduled Task Deletion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*delete*" AND Processes.process="*Raccine*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raccine_scheduled_task_deletion_filter`

[ESCU - Windows Rapid Authentication On Multiple Hosts - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event ID 4624 to identify a source computer authenticating to a large number of remote endpoints within an Active Directory network. Specifically, the logic will trigger when a source endpoint authenticates to 30 or more target computers within a 5 minute timespan. This behavior could represent an adversary who is moving laterally across the environment or enumerating network shares in the search for sensitive files. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event ID 4624 to identify a source computer authenticating to a large number of remote endpoints within an Active Directory network. Specifically, the logic will trigger when a source endpoint authenticates to 30 or more target computers within a 5 minute timespan. This behavior could represent an adversary who is moving laterally across the environment or enumerating network shares in the search for sensitive files. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed.
action.escu.creation_date = 2023-03-23
action.escu.modification_date = 2023-03-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Rapid Authentication On Multiple Hosts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = The source computer with ip address $IpAddress$ authenticated to a large number of remote endpoints within 5 minutes.
action.risk.param._risk = [{"risk_object_field": "host_targets", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Rapid Authentication On Multiple Hosts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event ID 4624 to identify a source computer authenticating to a large number of remote endpoints within an Active Directory network. Specifically, the logic will trigger when a source endpoint authenticates to 30 or more target computers within a 5 minute timespan. This behavior could represent an adversary who is moving laterally across the environment or enumerating network shares in the search for sensitive files. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.notable.param.rule_title = Windows Rapid Authentication On Multiple Hosts
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =   `wineventlog_security` EventCode=4624 LogonType=3 TargetUserName!="ANONYMOUS LOGON" TargetUserName!="*$" | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets by _time, IpAddress, TargetUserName | where unique_targets > 30 | `windows_rapid_authentication_on_multiple_hosts_filter`

[ESCU - Windows Rasautou DLL Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows Windows Remote Auto Dialer, rasautou.exe executing an arbitrary DLL. This technique is used to execute arbitrary shellcode or DLLs via the rasautou.exe LOLBin capability. During triage, review parent and child process behavior including file and image loads.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055.001", "T1218", "T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Windows Windows Remote Auto Dialer, rasautou.exe executing an arbitrary DLL. This technique is used to execute arbitrary shellcode or DLLs via the rasautou.exe LOLBin capability. During triage, review parent and child process behavior including file and image loads.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be limited to applications that require Rasautou.exe to load a DLL from disk. Filter as needed.
action.escu.creation_date = 2022-02-15
action.escu.modification_date = 2022-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Rasautou DLL Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to load a DLL in a suspicious manner.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Rasautou DLL Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1055.001", "T1218", "T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows Windows Remote Auto Dialer, rasautou.exe executing an arbitrary DLL. This technique is used to execute arbitrary shellcode or DLLs via the rasautou.exe LOLBin capability. During triage, review parent and child process behavior including file and image loads.
action.notable.param.rule_title = Windows Rasautou DLL Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rasautou.exe Processes.process="* -d *"AND Processes.process="* -p *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rasautou_dll_execution_filter`

[ESCU - Windows Raw Access To Disk Volume Partition - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for suspicious raw access read to device disk partition of the host machine. This technique was seen in several attacks by adversaries or threat actor to wipe, encrypt or overwrite the boot sector of each partition as part of their impact payload for example the "hermeticwiper" malware. This detection is a good indicator that there is a process try to read or write on boot sector.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1561.002", "T1561"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to look for suspicious raw access read to device disk partition of the host machine. This technique was seen in several attacks by adversaries or threat actor to wipe, encrypt or overwrite the boot sector of each partition as part of their impact payload for example the "hermeticwiper" malware. This detection is a good indicator that there is a process try to read or write on boot sector.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the raw access read event (like sysmon eventcode 9), process name and process guid from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = This event is really notable but we found minimal number of normal application from system32 folder like svchost.exe accessing it too. In this case we used 'system32' and 'syswow64' path as a filter for this detection.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Raw Access To Disk Volume Partition - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["CISA AA22-264A", "Graceful Wipe Out Attack", "Data Destruction", "Hermetic Wiper", "Caddy Wiper", "BlackByte Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Process accessing disk partition $Device$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Raw Access To Disk Volume Partition - Rule
action.correlationsearch.annotations = {"analytic_story": ["CISA AA22-264A", "Graceful Wipe Out Attack", "Data Destruction", "Hermetic Wiper", "Caddy Wiper", "BlackByte Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1561.002", "T1561"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=9 Device = \\Device\\HarddiskVolume* NOT (Image IN("*\\Windows\\System32\\*", "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id process_guid process_name process_path Device | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_disk_volume_partition_filter`

[ESCU - Windows Raw Access To Master Boot Record Drive - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for suspicious raw access read to drive where the master boot record is placed. This technique was seen in several attacks by adversaries or threat actor to wipe, encrypt or overwrite the master boot record code as part of their impact payload. This detection is a good indicator that there is a process try to read or write on MBR sector.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1561.002", "T1561"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to look for suspicious raw access read to drive where the master boot record is placed. This technique was seen in several attacks by adversaries or threat actor to wipe, encrypt or overwrite the master boot record code as part of their impact payload. This detection is a good indicator that there is a process try to read or write on MBR sector.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the raw access read event (like sysmon eventcode 9), process name and process guid from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = This event is really notable but we found minimal number of normal application from system32 folder like svchost.exe accessing it too. In this case we used 'system32' and 'syswow64' path as a filter for this detection.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Raw Access To Master Boot Record Drive - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["CISA AA22-264A", "WhisperGate", "Graceful Wipe Out Attack", "Data Destruction", "Hermetic Wiper", "Caddy Wiper", "BlackByte Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = process accessing MBR $Device$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Raw Access To Master Boot Record Drive - Rule
action.correlationsearch.annotations = {"analytic_story": ["CISA AA22-264A", "WhisperGate", "Graceful Wipe Out Attack", "Data Destruction", "Hermetic Wiper", "Caddy Wiper", "BlackByte Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1561.002", "T1561"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is to look for suspicious raw access read to drive where the master boot record is placed. This technique was seen in several attacks by adversaries or threat actor to wipe, encrypt or overwrite the master boot record code as part of their impact payload. This detection is a good indicator that there is a process try to read or write on MBR sector.
action.notable.param.rule_title = Windows Raw Access To Master Boot Record Drive
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=9 Device = \\Device\\Harddisk0\\DR0 NOT (Image IN("*\\Windows\\System32\\*", "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer Image Device ProcessGuid ProcessId EventDescription EventCode | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_master_boot_record_drive_filter`

[ESCU - Windows RDP Connection Successful - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies successful remote desktop connections. Utilize this analytic to hunt for successful attempts. In addition, the query may be modified for EventCode=1148 to potentially identify failed attempts. In testing, 1148 would not generate based on a failed logon attempt. Note this analytic requires enabling and a stanza in a inputs.conf.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1563.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies successful remote desktop connections. Utilize this analytic to hunt for successful attempts. In addition, the query may be modified for EventCode=1148 to potentially identify failed attempts. In testing, 1148 would not generate based on a failed logon attempt. Note this analytic requires enabling and a stanza in a inputs.conf.
action.escu.how_to_implement = The following analyic requires the WIndows TerminalServices RemoteConnectionManager Operational log to be enabled and ingested into Splunk. For the inputs, review https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706.
action.escu.known_false_positives = False positives will be present, filter as needed or restrict to critical assets on the perimeter.
action.escu.creation_date = 2023-04-17
action.escu.modification_date = 2023-04-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows RDP Connection Successful - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Lateral Movement", "BlackByte Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows RDP Connection Successful - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1563.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `remoteconnectionmanager` EventCode=1149 | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, Source_Network_Address, User, Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename ComputerName as dest | `windows_rdp_connection_successful_filter`

[ESCU - Windows Registry BootExecute Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic monitors the BootExecute registry key for any modifications from its default value, which could indicate potential malicious activity. The BootExecute registry key, located at HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Session Manager, manages the list of applications and services that are executed during system boot. By default, the BootExecute value is set to "autocheck autochk *". Attackers might attempt to modify this value to achieve persistence, load malicious code, or tamper with the system's boot process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1542", "T1547.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic monitors the BootExecute registry key for any modifications from its default value, which could indicate potential malicious activity. The BootExecute registry key, located at HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Session Manager, manages the list of applications and services that are executed during system boot. By default, the BootExecute value is set to "autocheck autochk *". Attackers might attempt to modify this value to achieve persistence, load malicious code, or tamper with the system's boot process.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Windows Registry that include the name of the path and key responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be present and will need to be filtered.
action.escu.creation_date = 2023-05-03
action.escu.modification_date = 2023-05-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Registry BootExecute Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows BootKits"]
action.risk = 1
action.risk.param._risk_message = The Registry BootExecute value was modified on $dest$ and should be reviewed immediately.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Registry BootExecute Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows BootKits"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1542", "T1547.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic monitors the BootExecute registry key for any modifications from its default value, which could indicate potential malicious activity. The BootExecute registry key, located at HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Session Manager, manages the list of applications and services that are executed during system boot. By default, the BootExecute value is set to "autocheck autochk *". Attackers might attempt to modify this value to achieve persistence, load malicious code, or tamper with the system's boot process.
action.notable.param.rule_title = Windows Registry BootExecute Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="HKLM\\System\\CurrentControlSet\\Control\\Session Manager\\BootExecute" BY _time span=1h Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid, Registry.action | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_registry_bootexecute_modification_filter`

[ESCU - Windows Registry Certificate Added - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies installation of a root CA certificate by monitoring the registry. The base paths may be found [here](https://gist.github.com/mattifestation/75d6117707bcf8c26845b3cbb6ad2b6b/raw/ae65ef15c706140ffc2e165615204e20f2903028/RootCAInstallationDetection.xml). In short, there are specific certificate registry paths that will be written to (SetValue) when a new certificate is added. The high-fidelity events to pay attention to are SetValue events where the TargetObject property ends with "<THUMBPRINT_VALUE>\Blob" as this indicates the direct installation or modification of a root certificate binary blob. The other high fidelity reference will be which process is making the registry modifications. There are very few processes that modify these day to day, therefore monitoring for all to start (hunting) provides a great beginning.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1553.004", "T1553"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies installation of a root CA certificate by monitoring the registry. The base paths may be found [here](https://gist.github.com/mattifestation/75d6117707bcf8c26845b3cbb6ad2b6b/raw/ae65ef15c706140ffc2e165615204e20f2903028/RootCAInstallationDetection.xml). In short, there are specific certificate registry paths that will be written to (SetValue) when a new certificate is added. The high-fidelity events to pay attention to are SetValue events where the TargetObject property ends with "<THUMBPRINT_VALUE>\Blob" as this indicates the direct installation or modification of a root certificate binary blob. The other high fidelity reference will be which process is making the registry modifications. There are very few processes that modify these day to day, therefore monitoring for all to start (hunting) provides a great beginning.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives will be limited to a legitimate business applicating consistently adding new root certificates to the endpoint. Filter by user, process, or thumbprint.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Registry Certificate Added - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Drivers", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = A root certificate was added on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Registry Certificate Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1553.004", "T1553"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\certificates\\*") AND Registry.registry_value_name="Blob" by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.process_guid Registry.registry_key_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_certificate_added_filter`

[ESCU - Windows Registry Delete Task SD - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process attempting to delete a scheduled task SD (Security Descriptor) from within the registry path of that task. This may occur from a non-standard process running and may not come from reg.exe. This particular behavior will remove the actual Task Name from the Task Scheduler GUI and from the command-line query - schtasks.exe /query. In addition, in order to perform this action, the user context will need to be SYSTEM.\ Identifying the deletion of a scheduled task's Security Descriptor from the registry is significant for a SOC as it may indicate malicious activity attempting to remove evidence of a scheduled task, potentially for defense evasion purposes. If a true positive is detected, it suggests an attacker with privileged access attempting to remove traces of their activities, which can have a significant impact on the security and functionality of affected systems. Immediate investigation and response are required to mitigate further risks and preserve the integrity of the environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process attempting to delete a scheduled task SD (Security Descriptor) from within the registry path of that task. This may occur from a non-standard process running and may not come from reg.exe. This particular behavior will remove the actual Task Name from the Task Scheduler GUI and from the command-line query - schtasks.exe /query. In addition, in order to perform this action, the user context will need to be SYSTEM.\ Identifying the deletion of a scheduled task's Security Descriptor from the registry is significant for a SOC as it may indicate malicious activity attempting to remove evidence of a scheduled task, potentially for defense evasion purposes. If a true positive is detected, it suggests an attacker with privileged access attempting to remove traces of their activities, which can have a significant impact on the security and functionality of affected systems. Immediate investigation and response are required to mitigate further risks and preserve the integrity of the environment.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives should be limited as the activity is not common to delete ONLY the SD from the registry. Filter as needed. Update the analytic Modified or Deleted values based on product that is in the datamodel.
action.escu.creation_date = 2022-04-13
action.escu.modification_date = 2022-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Registry Delete Task SD - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Registry Abuse", "Windows Persistence Techniques", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A scheduled task security descriptor was deleted from the registry on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Registry Delete Task SD - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Registry Abuse", "Windows Persistence Techniques", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1053.005", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Schedule\\TaskCache\\Tree\\*") Registry.user="SYSTEM" Registry.registry_value_name="SD" (Registry.action=Deleted OR Registry.action=modified) by _time  Registry.dest Registry.process_guid Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_key_name Registry.registry_value_data Registry.status Registry.action | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_delete_task_sd_filter`

[ESCU - Windows Registry Modification for Safe Mode Persistence - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a modification or registry add to the safeboot registry as an autostart mechanism. This technique is utilized by adversaries to persist a driver or service into Safe Mode. Two keys are monitored in this analytic,  Minimal and Network. adding values to Minimal will load into Safe Mode and by adding into Network it will provide the service or drive the ability to perform network connections in Safe Mode.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.001", "T1547"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a modification or registry add to the safeboot registry as an autostart mechanism. This technique is utilized by adversaries to persist a driver or service into Safe Mode. Two keys are monitored in this analytic,  Minimal and Network. adding values to Minimal will load into Safe Mode and by adding into Network it will provide the service or drive the ability to perform network connections in Safe Mode.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
action.escu.known_false_positives = updated windows application needed in safe boot may used this registry
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Registry Modification for Safe Mode Persistence - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ransomware", "Windows Registry Abuse", "Windows Drivers"]
action.risk = 1
action.risk.param._risk_message = Safeboot registry $registry_path$ was added or modified with a new value $registry_value_name$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Registry Modification for Safe Mode Persistence - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Registry Abuse", "Windows Drivers"], "cis20": ["CIS 10"], "confidence": 70, "impact": 60, "mitre_attack": ["T1547.001", "T1547"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a modification or registry add to the safeboot registry as an autostart mechanism. This technique is utilized by adversaries to persist a driver or service into Safe Mode. Two keys are monitored in this analytic,  Minimal and Network. adding values to Minimal will load into Safe Mode and by adding into Network it will provide the service or drive the ability to perform network connections in Safe Mode.
action.notable.param.rule_title = Windows Registry Modification for Safe Mode Persistence
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry where Registry.registry_path IN ("*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Minimal\\*","*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Network\\*") by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.process_guid Registry.registry_key_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_modification_for_safe_mode_persistence_filter`

[ESCU - Windows Registry Payload Injection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when suspiciouly long data is written to the registry. This behavior is often associated with certain fileless malware threats or persistence techniques used by threat actors. Data stored in the registy is considered fileless since it does not get written to disk and is traditionally not well defended since normal users can modify thier own registry.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027", "T1027.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies when suspiciouly long data is written to the registry. This behavior is often associated with certain fileless malware threats or persistence techniques used by threat actors. Data stored in the registy is considered fileless since it does not get written to disk and is traditionally not well defended since normal users can modify thier own registry.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown, possible custom scripting.
action.escu.creation_date = 2023-06-15
action.escu.modification_date = 2023-06-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Registry Payload Injection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = The process $process_name$ added a suspicious length of registry data on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 60}, {"threat_object_field": "process", "threat_object_type": "process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Registry Payload Injection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 60, "impact": 100, "mitre_attack": ["T1027", "T1027.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when suspiciouly long data is written to the registry. This behavior is often associated with certain fileless malware threats or persistence techniques used by threat actors. Data stored in the registy is considered fileless since it does not get written to disk and is traditionally not well defended since normal users can modify thier own registry.
action.notable.param.rule_title = Windows Registry Payload Injection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid| `drop_dm_object_name(Processes)` | join max=0 dest process_guid [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry where Registry.registry_value_data=* by _time span=1h Registry.dest Registry.registry_path Registry.registry_value_name Registry.process_guid Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | eval reg_data_len = len(registry_value_data) | where reg_data_len > 512] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_registry_payload_injection_filter`

[ESCU - Windows Registry SIP Provider Modification - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects modifications to the Windows Registry SIP Provider. It identifies this behavior by monitoring Sysmon Event ID 7, which logs registry modification events. The analytic specifically looks for changes in registry paths and values associated with Cryptography Providers and OID Encoding Types. This behavior is worth identifying as it may indicate an attempt to subvert trust controls, a technique often used by adversaries to bypass security measures and maintain persistence in an environment. If a true positive is found, it suggests an attacker is trying to manipulate the system's cryptographic functions, potentially leading to unauthorized access, data theft, or other damaging outcomes. Upon triage, review the registry paths and values modified, and look for concurrent processes to identify the attack source. Review the path of the SIP being added. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1553.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects modifications to the Windows Registry SIP Provider. It identifies this behavior by monitoring Sysmon Event ID 7, which logs registry modification events. The analytic specifically looks for changes in registry paths and values associated with Cryptography Providers and OID Encoding Types. This behavior is worth identifying as it may indicate an attempt to subvert trust controls, a technique often used by adversaries to bypass security measures and maintain persistence in an environment. If a true positive is found, it suggests an attacker is trying to manipulate the system's cryptographic functions, potentially leading to unauthorized access, data theft, or other damaging outcomes. Upon triage, review the registry paths and values modified, and look for concurrent processes to identify the attack source. Review the path of the SIP being added. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = Be aware of potential false positives - legitimate applications may cause benign activities to be flagged.
action.escu.creation_date = 2023-10-10
action.escu.modification_date = 2023-10-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Registry SIP Provider Modification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Subvert Trust Controls SIP and Trust Provider Hijacking"]
action.risk = 1
action.risk.param._risk_message = Windows Registry SIP Provider Modification detected on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Registry SIP Provider Modification - Rule
action.correlationsearch.annotations = {"analytic_story": ["Subvert Trust Controls SIP and Trust Provider Hijacking"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1553.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects modifications to the Windows Registry SIP Provider. It identifies this behavior by monitoring Sysmon Event ID 7, which logs registry modification events. The analytic specifically looks for changes in registry paths and values associated with Cryptography Providers and OID Encoding Types. This behavior is worth identifying as it may indicate an attempt to subvert trust controls, a technique often used by adversaries to bypass security measures and maintain persistence in an environment. If a true positive is found, it suggests an attacker is trying to manipulate the system's cryptographic functions, potentially leading to unauthorized access, data theft, or other damaging outcomes. Upon triage, review the registry paths and values modified, and look for concurrent processes to identify the attack source. Review the path of the SIP being added. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.notable.param.rule_title = Windows Registry SIP Provider Modification
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\Microsoft\\Cryptography\\OID\\EncodingType*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\OID\\EncodingType*") Registry.registry_value_name IN ("Dll","$DLL") by Registry.dest , Registry.user Registry.registry_value_name, Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`| `windows_registry_sip_provider_modification_filter`

[ESCU - Windows Regsvr32 Renamed Binary - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies renamed instances of regsv32.exe executing. regsv32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate if it is the legitimate regsv32.exe executing and what dll module content it is loading. This query relies on the original filename or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.010", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies renamed instances of regsv32.exe executing. regsv32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate if it is the legitimate regsv32.exe executing and what dll module content it is loading. This query relies on the original filename or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-10-27
action.escu.modification_date = 2022-10-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Regsvr32 Renamed Binary - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = regsvr32 was renamed as $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Regsvr32 Renamed Binary - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1218.010", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following hunting analytic identifies renamed instances of regsv32.exe executing. regsv32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate if it is the legitimate regsv32.exe executing and what dll module content it is loading. This query relies on the original filename or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.
action.notable.param.rule_title = Windows Regsvr32 Renamed Binary
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != regsvr32.exe AND Processes.original_file_name=regsvr32.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_regsvr32_renamed_binary_filter`

[ESCU - Windows Remote Access Software BRC4 Loaded Dll - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following anomaly detection identifies the behavior related to 4 native Windows DLLs being loaded by a non-standard process. Identified by MDSec during their research into Brute Ratel, MDSec identified a high signal analytic by calling out these 4 DLLs being loaded into a process. LogonCLI.dll is the Net Logon Client DLL and is related to users and other domain services to get authenticated. Credui.dll is Credential Manager User Interface. Credential managers receive notifications when authentication information changes. For example, credential managers are notified when a user logs on or an account password changes. Samcli.dll is the Security Accounts Manager Client DLL. Adversaries may attempt to extract credential material from the Security Account Manager (SAM) database either through in-memory techniques or through the Windows Registry where the SAM database is stored. Dbghelp.dll is Windows Image Helper. Windows Image Helper is commonly seen in credential dumping due to native functions. All of these modules are important to monitor and track and combined may lead to credentail access or dumping.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1219", "T1003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following anomaly detection identifies the behavior related to 4 native Windows DLLs being loaded by a non-standard process. Identified by MDSec during their research into Brute Ratel, MDSec identified a high signal analytic by calling out these 4 DLLs being loaded into a process. LogonCLI.dll is the Net Logon Client DLL and is related to users and other domain services to get authenticated. Credui.dll is Credential Manager User Interface. Credential managers receive notifications when authentication information changes. For example, credential managers are notified when a user logs on or an account password changes. Samcli.dll is the Security Accounts Manager Client DLL. Adversaries may attempt to extract credential material from the Security Account Manager (SAM) database either through in-memory techniques or through the Windows Registry where the SAM database is stored. Dbghelp.dll is Windows Image Helper. Windows Image Helper is commonly seen in credential dumping due to native functions. All of these modules are important to monitor and track and combined may lead to credentail access or dumping.
action.escu.how_to_implement = The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
action.escu.known_false_positives = This module can be loaded by a third party application. Filter is needed.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Access Software BRC4 Loaded Dll - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brute Ratel C4"]
action.risk = 1
action.risk.param._risk_message = a process $Image$ loaded several modules $ImageLoaded$ that might related to credential access on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Access Software BRC4 Loaded Dll - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1219", "T1003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 |bin _time span=30s | eval BRC4_AnomalyLoadedDll=case(OriginalFileName=="credui.dll", 1, OriginalFileName=="DBGHELP.DLL", 1, OriginalFileName=="SAMCLI.DLL", 1, OriginalFileName=="winhttp.dll", 1, 1=1, 0) | eval BRC4_LoadedDllPath=case(match(ImageLoaded, "credui.dll"), 1, match(ImageLoaded, "dbghelp.dll"), 1, match(ImageLoaded, "samcli.dll"), 1, match(ImageLoaded, "winhttp.dll"), 1, 1=1, 0) | stats count min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as ImageLoaded values(OriginalFileName) as OriginalFileName dc(ImageLoaded) as ImageLoadedCount by Image  BRC4_LoadedDllPath BRC4_AnomalyLoadedDll dest EventCode Signed | where  ImageLoadedCount == 4 AND (BRC4_LoadedDllPath == 1 OR BRC4_AnomalyLoadedDll == 1) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_access_software_brc4_loaded_dll_filter`

[ESCU - Windows Remote Access Software Hunt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic is meant to help organizations understand what remote access software is being used in the environment. When reviewing this hunt, confirm the software identified is authorized to be utilized. Based on fidelity, create a new analytic for specific utilities banned within the organization. Adversaries use these utilities to retain remote access capabilities to the environment. Utilities in the lookup include AnyDesk, GoToMyPC, LogMeIn, TeamViewer and much more. Review the lookup for the entire list and add any others.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1219"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic is meant to help organizations understand what remote access software is being used in the environment. When reviewing this hunt, confirm the software identified is authorized to be utilized. Based on fidelity, create a new analytic for specific utilities banned within the organization. Adversaries use these utilities to retain remote access capabilities to the environment. Utilities in the lookup include AnyDesk, GoToMyPC, LogMeIn, TeamViewer and much more. Review the lookup for the entire list and add any others.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be found. Filter as needed and create higher fidelity analytics based off banned remote access software.
action.escu.creation_date = 2022-08-22
action.escu.modification_date = 2022-08-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Access Software Hunt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Insider Threat", "Command And Control", "Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Access Software Hunt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Insider Threat", "Command And Control", "Ransomware"], "cis20": ["CIS 10"], "confidence": 10, "impact": 10, "mitre_attack": ["T1219"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown by Processes.dest Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup remote_access_software remote_utility AS process_name OUTPUT isutility | search isutility = True | `windows_remote_access_software_hunt_filter`

[ESCU - Windows Remote Access Software RMS Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification or creation of Windows registry related to the Remote Manipulator System (RMS) Remote Admin tool. RMS is a legitimate tool developed by russian organization TektonIT and has been observed being abused by adversaries to gain remote access to the targeted host. Azorult malware utilized RMS to gain remote access.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1219"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification or creation of Windows registry related to the Remote Manipulator System (RMS) Remote Admin tool. RMS is a legitimate tool developed by russian organization TektonIT and has been observed being abused by adversaries to gain remote access to the targeted host. Azorult malware utilized RMS to gain remote access.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-22
action.escu.modification_date = 2022-06-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Access Software RMS Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry related to RMS tool is created in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Access Software RMS Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1219"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is to identify a modification or creation of Windows registry related to the Remote Manipulator System (RMS) Remote Admin tool. RMS is a legitimate tool developed by russian organization TektonIT and has been observed being abused by adversaries to gain remote access to the targeted host. Azorult malware utilized RMS to gain remote access.
action.notable.param.rule_title = Windows Remote Access Software RMS Registry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\Remote Manipulator System*" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_access_software_rms_registry_filter`

[ESCU - Windows Remote Assistance Spawning Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of Microsoft Remote Assistance, msra.exe, spawning PowerShell.exe or cmd.exe as a child process. Msra.exe by default has no command-line arguments and typically spawns itself. It will generate a network connection to the remote system that is connected. This behavior is indicative of another process injected into msra.exe. Review the parent process or cross process events to identify source.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of Microsoft Remote Assistance, msra.exe, spawning PowerShell.exe or cmd.exe as a child process. Msra.exe by default has no command-line arguments and typically spawns itself. It will generate a network connection to the remote system that is connected. This behavior is indicative of another process injected into msra.exe. Review the parent process or cross process events to identify source.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, filter as needed. Add additional shells as needed.
action.escu.creation_date = 2022-02-07
action.escu.modification_date = 2022-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Assistance Spawning Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$, generating behavior not common with msra.exe.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Assistance Spawning Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the use of Microsoft Remote Assistance, msra.exe, spawning PowerShell.exe or cmd.exe as a child process. Msra.exe by default has no command-line arguments and typically spawns itself. It will generate a network connection to the remote system that is connected. This behavior is indicative of another process injected into msra.exe. Review the parent process or cross process events to identify source.
action.notable.param.rule_title = Windows Remote Assistance Spawning Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msra.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_assistance_spawning_process_filter`

[ESCU - Windows Remote Create Service - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies an endpoint that remotely connects to another endpoint to create a new service using sc.exe. On the remote endpoint, the new service will be created and this action will trigger the creation of EventCode 7045 along with all the resulting service information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies an endpoint that remotely connects to another endpoint to create a new service using sc.exe. On the remote endpoint, the new service will be created and this action will trigger the creation of EventCode 7045 along with all the resulting service information.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Note that false positives may occur, and filtering may be necessary, especially when it comes to remote service creation by administrators or software management utilities.
action.escu.creation_date = 2023-03-20
action.escu.modification_date = 2023-03-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Create Service - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a remote service.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 25}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Create Service - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process IN ("*create*")  Processes.process="*\\\\*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_create_service_filter`

[ESCU - Windows Remote Service Rdpwinst Tool Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies RDPWInst.exe tool, which is a RDP wrapper library tool designed to enable remote desktop host support and concurrent RDP session on reduced functionality system. Unfortunately, this open project was abused by adversaries to enable RDP connection to the targeted host for remote access and potentially be for lateral movement.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies RDPWInst.exe tool, which is a RDP wrapper library tool designed to enable remote desktop host support and concurrent RDP session on reduced functionality system. Unfortunately, this open project was abused by adversaries to enable RDP connection to the targeted host for remote access and potentially be for lateral movement.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This tool was designed for home usage and not commonly seen in production environment. Filter as needed.
action.escu.creation_date = 2022-06-24
action.escu.modification_date = 2022-06-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Service Rdpwinst Tool Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = Rdpwinst.exe executed on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Service Rdpwinst Tool Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies RDPWInst.exe tool, which is a RDP wrapper library tool designed to enable remote desktop host support and concurrent RDP session on reduced functionality system. Unfortunately, this open project was abused by adversaries to enable RDP connection to the targeted host for remote access and potentially be for lateral movement.
action.notable.param.rule_title = Windows Remote Service Rdpwinst Tool Execution
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="RDPWInst.exe" OR Processes.original_file_name="RDPWInst.exe") AND Processes.process IN ("* -i*", "* -s*", "* -o*", "* -w*", "* -r*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_service_rdpwinst_tool_execution_filter`

[ESCU - Windows Remote Services Allow Rdp In Firewall - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows firewall to enable remote desktop protocol on a targeted machine. This technique was seen in several adversaries, malware or red teamer to remotely access the compromised or targeted host by allowing this protocol in firewall. Even this protocol might be allowed in some production environment, This TTP behavior is a good pivot to check who and why the user want to enable this feature through firewall which is also common traits of attack to start lateral movement.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows firewall to enable remote desktop protocol on a targeted machine. This technique was seen in several adversaries, malware or red teamer to remotely access the compromised or targeted host by allowing this protocol in firewall. Even this protocol might be allowed in some production environment, This TTP behavior is a good pivot to check who and why the user want to enable this feature through firewall which is also common traits of attack to start lateral movement.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-21
action.escu.modification_date = 2022-06-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Services Allow Rdp In Firewall - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = new firewall rules was added to allow rdp connection to $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Services Allow Rdp In Firewall - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "netsh.exe" OR Processes.original_file_name= "netsh.exe") AND Processes.process = "*firewall*" AND Processes.process = "*add*" AND Processes.process = "*protocol=TCP*" AND Processes.process = "*localport=3389*" AND Processes.process = "*action=allow*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_rdp_in_firewall_filter`

[ESCU - Windows Remote Services Allow Remote Assistance - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows registry to enable remote desktop assistance on a targeted machine. This technique was seen in several adversaries, malware or red teamer like azorult to remotely access the compromised or targeted host by enabling this protocol in registry. Even this protocol might be allowed in some production environment, This Anomaly behavior is a good pivot to check who and why the user want to enable this feature through registry which is un-common. And as per stated in microsoft documentation the default value of this registry is false that makes this a good indicator of suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows registry to enable remote desktop assistance on a targeted machine. This technique was seen in several adversaries, malware or red teamer like azorult to remotely access the compromised or targeted host by enabling this protocol in registry. Even this protocol might be allowed in some production environment, This Anomaly behavior is a good pivot to check who and why the user want to enable this feature through registry which is un-common. And as per stated in microsoft documentation the default value of this registry is false that makes this a good indicator of suspicious behavior.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-21
action.escu.modification_date = 2022-06-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Services Allow Remote Assistance - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry for rdp protocol was modified to enable in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Services Allow Remote Assistance - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fAllowToGetHelp*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_remote_assistance_filter`

[ESCU - Windows Remote Services Rdp Enable - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is to identify a modification in the Windows registry to enable remote desktop protocol on a targeted machine. This technique was seen in several adversaries, malware or red teamer to remotely access the compromised or targeted host by enabling this protocol in registry. Even this protocol might be allowed in some production environment, This TTP behavior is a good pivot to check who and why the user want to enable this feature through registry which is un-common.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is to identify a modification in the Windows registry to enable remote desktop protocol on a targeted machine. This technique was seen in several adversaries, malware or red teamer to remotely access the compromised or targeted host by enabling this protocol in registry. Even this protocol might be allowed in some production environment, This TTP behavior is a good pivot to check who and why the user want to enable this feature through registry which is un-common.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = administrators may enable or disable this feature that may cause some false positive.
action.escu.creation_date = 2022-06-21
action.escu.modification_date = 2022-06-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Remote Services Rdp Enable - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = the registry for rdp protocol was modified to enable in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Remote Services Rdp Enable - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is to identify a modification in the Windows registry to enable remote desktop protocol on a targeted machine. This technique was seen in several adversaries, malware or red teamer to remotely access the compromised or targeted host by enabling this protocol in registry. Even this protocol might be allowed in some production environment, This TTP behavior is a good pivot to check who and why the user want to enable this feature through registry which is un-common.
action.notable.param.rule_title = Windows Remote Services Rdp Enable
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_rdp_enable_filter`

[ESCU - Windows Replication Through Removable Media - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is developed to detect suspicious executable or script files created or dropped in the root drive of a targeted host. This technique is commonly used by threat actors, adversaries or even red teamers to replicate or spread in possible removable drives. Back then, WORM malware was popular for this technique where it would drop a copy of itself in the root drive to be able to spread or to have a lateral movement in other network machines. Nowadays, Ransomware like CHAOS ransomware also use this technique to spread its malicious code in possible removable drives. This TTP detection can be a good indicator that a process might create a persistence technique or lateral movement of a targeted machine. We suggest checking the process name that creates this event, the file created, user type, and the reason why that executable or scripts are dropped in the root drive.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1091"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is developed to detect suspicious executable or script files created or dropped in the root drive of a targeted host. This technique is commonly used by threat actors, adversaries or even red teamers to replicate or spread in possible removable drives. Back then, WORM malware was popular for this technique where it would drop a copy of itself in the root drive to be able to spread or to have a lateral movement in other network machines. Nowadays, Ransomware like CHAOS ransomware also use this technique to spread its malicious code in possible removable drives. This TTP detection can be a good indicator that a process might create a persistence technique or lateral movement of a targeted machine. We suggest checking the process name that creates this event, the file created, user type, and the reason why that executable or scripts are dropped in the root drive.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = Administrators may allow creation of script or exe in the paths specified. Filter as needed.
action.escu.creation_date = 2023-09-07
action.escu.modification_date = 2023-09-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Replication Through Removable Media - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Chaos Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = executable or script $file_path$ was dropped in root drive $root_drive$ in $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Replication Through Removable Media - Rule
action.correlationsearch.annotations = {"analytic_story": ["Chaos Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1091"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is developed to detect suspicious executable or script files created or dropped in the root drive of a targeted host. This technique is commonly used by threat actors, adversaries or even red teamers to replicate or spread in possible removable drives. Back then, WORM malware was popular for this technique where it would drop a copy of itself in the root drive to be able to spread or to have a lateral movement in other network machines. Nowadays, Ransomware like CHAOS ransomware also use this technique to spread its malicious code in possible removable drives. This TTP detection can be a good indicator that a process might create a persistence technique or lateral movement of a targeted machine. We suggest checking the process name that creates this event, the file created, user type, and the reason why that executable or scripts are dropped in the root drive.
action.notable.param.rule_title = Windows Replication Through Removable Media
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name = *.js  OR Filesystem.file_name= *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.user Filesystem.dest | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | where LIKE(root_drive, "%:") AND dropped_file_path_split_count = 2  AND root_drive!= "C:" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_replication_through_removable_media_filter`

[ESCU - Windows Root Domain linked policies Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate root domain linked policies for situational awareness and Active Directory Discovery.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the `[Adsisearcher]` type accelerator being used to query Active Directory for domain groups. Red Teams and adversaries may leverage `[Adsisearcher]` to enumerate root domain linked policies for situational awareness and Active Directory Discovery.
action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Root Domain linked policies Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Data Destruction", "Active Directory Discovery", "Industroyer2"]
action.risk = 1
action.risk.param._risk_message = Windows PowerShell [Adsisearcher] was used user enumeration on endpoint $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Root Domain linked policies Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Active Directory Discovery", "Industroyer2"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087.002", "T1087"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*.SearchRooT*" ScriptBlockText = "*.gplink*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_root_domain_linked_policies_discovery_filter`

[ESCU - Windows Rundll32 WebDAV Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe with the commandline arguments loading davclnt.dll function - davsetcookie - to be used to access a remote WebDav instance. This particular behavior was recently showcased in CVE-2023-23397.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies rundll32.exe with the commandline arguments loading davclnt.dll function - davsetcookie - to be used to access a remote WebDav instance. This particular behavior was recently showcased in CVE-2023-23397.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present based on legitimate software, filtering may need to occur.
action.escu.creation_date = 2023-03-15
action.escu.modification_date = 2023-03-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Rundll32 WebDAV Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["CVE-2023-23397 Outlook Elevation of Privilege"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 48}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Rundll32 WebDAV Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["CVE-2023-23397 Outlook Elevation of Privilege"], "cis20": ["CIS 10"], "confidence": 60, "cve": ["CVE-2023-23397"], "impact": 80, "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe with the commandline arguments loading davclnt.dll function - davsetcookie - to be used to access a remote WebDav instance. This particular behavior was recently showcased in CVE-2023-23397.
action.notable.param.rule_title = Windows Rundll32 WebDAV Request
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*","*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rundll32_webdav_request_filter`

[ESCU - Windows Rundll32 WebDav With Network Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies rundll32.exe with the commandline arguments loading davclnt.dll function - davsetcookie - to be used to access a remote WebDav instance. The analytic attempts to use join from Processes and All_Traffic to identify the network connection. This particular behavior was recently showcased in CVE-2023-23397.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic identifies rundll32.exe with the commandline arguments loading davclnt.dll function - davsetcookie - to be used to access a remote WebDav instance. The analytic attempts to use join from Processes and All_Traffic to identify the network connection. This particular behavior was recently showcased in CVE-2023-23397.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present based on legitimate software, filtering may need to occur.
action.escu.creation_date = 2023-03-16
action.escu.modification_date = 2023-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Rundll32 WebDav With Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["CVE-2023-23397 Outlook Elevation of Privilege"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 48}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Rundll32 WebDav With Network Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["CVE-2023-23397 Outlook Elevation of Privilege"], "cis20": ["CIS 10"], "confidence": 60, "cve": ["CVE-2023-23397"], "impact": 80, "mitre_attack": ["T1048.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies rundll32.exe with the commandline arguments loading davclnt.dll function - davsetcookie - to be used to access a remote WebDav instance. The analytic attempts to use join from Processes and All_Traffic to identify the network connection. This particular behavior was recently showcased in CVE-2023-23397.
action.notable.param.rule_title = Windows Rundll32 WebDav With Network Connection
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name=svchost.exe `process_rundll32` Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*", "*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename dest as src | join host process_id [ | tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port!=0 NOT (All_Traffic.dest_ip IN (10.0.0.0/12, 172.16.0.0/12, 192.168.0.0/16))  by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `windows_rundll32_webdav_with_network_connection_filter`

[ESCU - Windows Scheduled Task Created Via XML - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of suspicious scheduled tasks in Windows, specifically tasks created using schtasks.exe with the -create flag and an XML parameter in the command-line. This technique is commonly employed by threat actors, adversaries, and red teamers to establish persistence or achieve privilege escalation on targeted hosts. Notably, malware like Trickbot and Winter-Vivern have been observed using XML files to create scheduled tasks. Monitoring and investigating this activity is crucial to mitigate potential security risks. It is important to be aware that scripts or administrators may trigger this analytic, leading to potential false positives. To minimize false positives, adjust the filter based on the parent process or application.\
When a true positive is detected, it suggests an attacker's attempt to gain persistence or execute additional malicious payloads, potentially resulting in data theft, ransomware, or other damaging outcomes. During triage, review the source of the scheduled task, the command to be executed, and capture any relevant on-disk artifacts. Analyze concurrent processes to identify the source of the attack. This analytic enables analysts to detect and respond to potential threats early, mitigating the associated risks effectively.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of suspicious scheduled tasks in Windows, specifically tasks created using schtasks.exe with the -create flag and an XML parameter in the command-line. This technique is commonly employed by threat actors, adversaries, and red teamers to establish persistence or achieve privilege escalation on targeted hosts. Notably, malware like Trickbot and Winter-Vivern have been observed using XML files to create scheduled tasks. Monitoring and investigating this activity is crucial to mitigate potential security risks. It is important to be aware that scripts or administrators may trigger this analytic, leading to potential false positives. To minimize false positives, adjust the filter based on the parent process or application.\
When a true positive is detected, it suggests an attacker's attempt to gain persistence or execute additional malicious payloads, potentially resulting in data theft, ransomware, or other damaging outcomes. During triage, review the source of the scheduled task, the command to be executed, and capture any relevant on-disk artifacts. Analyze concurrent processes to identify the source of the attack. This analytic enables analysts to detect and respond to potential threats early, mitigating the associated risks effectively.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible scripts or administrators may trigger this analytic. Filter as needed based on parent process, application.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Scheduled Task Created Via XML - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Winter Vivern", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A scheduled task process, $process_name$, with 'create' or 'delete' commands present in the command line.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Scheduled Task Created Via XML - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of suspicious scheduled tasks in Windows, specifically tasks created using schtasks.exe with the -create flag and an XML parameter in the command-line. This technique is commonly employed by threat actors, adversaries, and red teamers to establish persistence or achieve privilege escalation on targeted hosts. Notably, malware like Trickbot and Winter-Vivern have been observed using XML files to create scheduled tasks. Monitoring and investigating this activity is crucial to mitigate potential security risks. It is important to be aware that scripts or administrators may trigger this analytic, leading to potential false positives. To minimize false positives, adjust the filter based on the parent process or application.\
When a true positive is detected, it suggests an attacker's attempt to gain persistence or execute additional malicious payloads, potentially resulting in data theft, ransomware, or other damaging outcomes. During triage, review the source of the scheduled task, the command to be executed, and capture any relevant on-disk artifacts. Analyze concurrent processes to identify the source of the attack. This analytic enables analysts to detect and respond to potential threats early, mitigating the associated risks effectively.
action.notable.param.rule_title = Windows Scheduled Task Created Via XML
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process=*create* Processes.process="* /xml *" by Processes.user  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.process_id Processes.parent_process_guid Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_created_via_xml_filter`

[ESCU - Windows Scheduled Task Service Spawned Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when the Task Scheduler service "svchost.exe -k netsvcs -p -s Schedule" is the parent process to common command line, scripting, or shell execution binaries. Attackers often abuse the task scheduler service with these binaries as an execution and persistence mechanism in order to blend in with normal Windows operations. This TTP is also commonly seen for legitimate purposes such as business scripts or application updates.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies when the Task Scheduler service "svchost.exe -k netsvcs -p -s Schedule" is the parent process to common command line, scripting, or shell execution binaries. Attackers often abuse the task scheduler service with these binaries as an execution and persistence mechanism in order to blend in with normal Windows operations. This TTP is also commonly seen for legitimate purposes such as business scripts or application updates.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown, possible custom scripting.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Scheduled Task Service Spawned Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A windows scheduled task spawned the shell application $process_name$ on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 20}, {"threat_object_field": "process", "threat_object_type": "process name"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Scheduled Task Service Spawned Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 10"], "confidence": 25, "impact": 80, "mitre_attack": ["T1053.005", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when the Task Scheduler service "svchost.exe -k netsvcs -p -s Schedule" is the parent process to common command line, scripting, or shell execution binaries. Attackers often abuse the task scheduler service with these binaries as an execution and persistence mechanism in order to blend in with normal Windows operations. This TTP is also commonly seen for legitimate purposes such as business scripts or application updates.
action.notable.param.rule_title = Windows Scheduled Task Service Spawned Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*\\system32\\svchost.exe*" AND Processes.parent_process="*-k*" AND Processes.parent_process= "*netsvcs*" AND Processes.parent_process="*-p*" AND Processes.parent_process="*-s*" AND Processes.parent_process="*Schedule*" Processes.process_name IN("powershell.exe", "wscript.exe", "cscript.exe", "cmd.exe", "sh.exe", "ksh.exe", "zsh.exe", "bash.exe", "scrcons.exe","pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_service_spawned_shell_filter`

[ESCU - Windows Scheduled Task with Highest Privileges - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of a new task with the highest execution privilege via Schtasks.exe. This tactic is often observed in AsyncRAT attacks, where the scheduled task is used for persistence and privilege escalation. AsyncRAT sets up a scheduled task with parameters '/rl' and 'highest', triggering this technique. It's a strong indicator of potential malware or adversaries seeking to establish persistence and escalate privileges through scheduled tasks. This is crucial for a Security Operations Center (SOC) as it can prevent unauthorized system access and potential data breaches.\
The analytic works by monitoring logs for process name, parent process, and command-line executions. In the presence of the '*/rl ' and ' highest *' commands in a schtasks.exe process, an alert is triggered.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the creation of a new task with the highest execution privilege via Schtasks.exe. This tactic is often observed in AsyncRAT attacks, where the scheduled task is used for persistence and privilege escalation. AsyncRAT sets up a scheduled task with parameters '/rl' and 'highest', triggering this technique. It's a strong indicator of potential malware or adversaries seeking to establish persistence and escalate privileges through scheduled tasks. This is crucial for a Security Operations Center (SOC) as it can prevent unauthorized system access and potential data breaches.\
The analytic works by monitoring logs for process name, parent process, and command-line executions. In the presence of the '*/rl ' and ' highest *' commands in a schtasks.exe process, an alert is triggered.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may arise from legitimate applications that create tasks to run as SYSTEM. Therefore, it's recommended to adjust filters based on parent process or modify the query to include world writable paths for restriction.
action.escu.creation_date = 2023-01-26
action.escu.modification_date = 2023-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Scheduled Task with Highest Privileges - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["AsyncRAT", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = a $process_name$ creating a schedule task $process$ with highest run level privilege in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Scheduled Task with Highest Privileges - Rule
action.correlationsearch.annotations = {"analytic_story": ["AsyncRAT", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1053", "T1053.005"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of a new task with the highest execution privilege via Schtasks.exe. This tactic is often observed in AsyncRAT attacks, where the scheduled task is used for persistence and privilege escalation. AsyncRAT sets up a scheduled task with parameters '/rl' and 'highest', triggering this technique. It's a strong indicator of potential malware or adversaries seeking to establish persistence and escalate privileges through scheduled tasks. This is crucial for a Security Operations Center (SOC) as it can prevent unauthorized system access and potential data breaches.\
The analytic works by monitoring logs for process name, parent process, and command-line executions. In the presence of the '*/rl ' and ' highest *' commands in a schtasks.exe process, an alert is triggered.
action.notable.param.rule_title = Windows Scheduled Task with Highest Privileges
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/rl *" Processes.process = "* highest *" by Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_with_highest_privileges_filter`

[ESCU - Windows Schtasks Create Run As System - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the creation of a new task to start and run as an elevated user - SYSTEM using Schtasks.exe. This behavior is commonly used by adversaries to spawn a process in an elevated state. If a true positive is found, it suggests an attacker is attempting to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. Upon triage, review the scheduled task's source and the command to be executed. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the creation of a new task to start and run as an elevated user - SYSTEM using Schtasks.exe. This behavior is commonly used by adversaries to spawn a process in an elevated state. If a true positive is found, it suggests an attacker is attempting to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. Upon triage, review the scheduled task's source and the command to be executed. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be limited to legitimate applications creating a task to run as SYSTEM. Filter as needed based on parent process, or modify the query to have world writeable paths to restrict it.
action.escu.creation_date = 2022-02-07
action.escu.modification_date = 2022-02-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Schtasks Create Run As System - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Persistence Techniques", "Qakbot", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = An $process_name$ was created on endpoint $dest$ attempting to spawn as SYSTEM.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Schtasks Create Run As System - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Qakbot", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 60, "impact": 80, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the creation of a new task to start and run as an elevated user - SYSTEM using Schtasks.exe. This behavior is commonly used by adversaries to spawn a process in an elevated state. If a true positive is found, it suggests an attacker is attempting to persist within the environment or potentially deliver additional malicious payloads, leading to data theft, ransomware, or other damaging outcomes. Upon triage, review the scheduled task's source and the command to be executed. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.notable.param.rule_title = Windows Schtasks Create Run As System
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_schtasks` Processes.process="*/create *" AND Processes.process="*/ru *" AND Processes.process="*system*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_schtasks_create_run_as_system_filter`

[ESCU - Windows Screen Capture Via Powershell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a potential PowerShell script that captures screen images on compromised or targeted hosts. This technique was observed in the Winter-Vivern malware, which attempts to capture desktop screens using a PowerShell script and send the images to its C2 server as part of its exfiltration strategy. This TTP serves as a useful indicator that a PowerShell process may be gathering desktop screenshots from a host system, potentially signaling malicious activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a potential PowerShell script that captures screen images on compromised or targeted hosts. This technique was observed in the Winter-Vivern malware, which attempts to capture desktop screens using a PowerShell script and send the images to its C2 server as part of its exfiltration strategy. This TTP serves as a useful indicator that a PowerShell process may be gathering desktop screenshots from a host system, potentially signaling malicious activity.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Screen Capture Via Powershell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Winter Vivern"]
action.risk = 1
action.risk.param._risk_message = A PowerShell script was identified possibly performing screen captures on $Computer$.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Screen Capture Via Powershell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Winter Vivern"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1113"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a potential PowerShell script that captures screen images on compromised or targeted hosts. This technique was observed in the Winter-Vivern malware, which attempts to capture desktop screens using a PowerShell script and send the images to its C2 server as part of its exfiltration strategy. This TTP serves as a useful indicator that a PowerShell process may be gathering desktop screenshots from a host system, potentially signaling malicious activity.
action.notable.param.rule_title = Windows Screen Capture Via Powershell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText = "*[Drawing.Graphics]::FromImage(*" AND ScriptBlockText = "*New-Object Drawing.Bitmap*" AND ScriptBlockText = "*.CopyFromScreen*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_screen_capture_via_powershell_filter`

[ESCU - Windows Security Account Manager Stopped - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified.
action.escu.creation_date = 2020-11-06
action.escu.modification_date = 2020-11-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Security Account Manager Stopped - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Ryuk Ransomware"]
action.risk = 1
action.risk.param._risk_message = The Windows Security Account Manager (SAM) was stopped via cli by $user$ on $dest$ by this command: $process$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}, {"risk_object_field": "process", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Security Account Manager Stopped - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.
action.notable.param.rule_title = Windows Security Account Manager Stopped
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter`

[ESCU - Windows Security Support Provider Reg Query - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line related to the discovery of possible Security Support Providers in the registry. This technique is being abused by adversaries or post exploitation tools like winpeas to gather LSA protection and configuration in the registry in the targeted host. This registry entry can contain several information related to LSA that validates users for local and remote sign-ins and enforces local security policies. Understanding LSA protection may give a good information in accessing LSA content in memory which is commonly attack by adversaries and tool like mimikatz to scrape password hashes or clear plain text passwords.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.005", "T1547"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line related to the discovery of possible Security Support Providers in the registry. This technique is being abused by adversaries or post exploitation tools like winpeas to gather LSA protection and configuration in the registry in the targeted host. This registry entry can contain several information related to LSA that validates users for local and remote sign-ins and enforces local security policies. Understanding LSA protection may give a good information in accessing LSA content in memory which is commonly attack by adversaries and tool like mimikatz to scrape password hashes or clear plain text passwords.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Security Support Provider Reg Query - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware", "Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = process with reg query command line $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Security Support Provider Reg Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware", "Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1547.005", "T1547"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process = "*\\SYSTEM\\CurrentControlSet\\Control\\LSA*" Processes.process IN ("*RunAsPPL*" , "*LsaCfgFlags*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_security_support_provider_reg_query_filter`

[ESCU - Windows Server Software Component GACUtil Install to GAC - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the Windows SDK utility - GACUtil.exe, being utilized to add a DLL into the Global Assembly Cache (GAC). Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. As outlined by Microsoft in their blog, it is not common to see this spawning from W3WP.exe, however, in a non-development environment it may not be common at all. Note that in order to utilize GACutil.exe, The Windows SDK must be installed, this is not a native binary.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the Windows SDK utility - GACUtil.exe, being utilized to add a DLL into the Global Assembly Cache (GAC). Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. As outlined by Microsoft in their blog, it is not common to see this spawning from W3WP.exe, however, in a non-development environment it may not be common at all. Note that in order to utilize GACutil.exe, The Windows SDK must be installed, this is not a native binary.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present if gacutil.exe is utilized day to day by developers. Filter as needed.
action.escu.creation_date = 2023-01-17
action.escu.modification_date = 2023-01-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Server Software Component GACUtil Install to GAC - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IIS Components"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a module to the global assembly cache.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Server Software Component GACUtil Install to GAC - Rule
action.correlationsearch.annotations = {"analytic_story": ["IIS Components"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1505", "T1505.004"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the Windows SDK utility - GACUtil.exe, being utilized to add a DLL into the Global Assembly Cache (GAC). Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. As outlined by Microsoft in their blog, it is not common to see this spawning from W3WP.exe, however, in a non-development environment it may not be common at all. Note that in order to utilize GACutil.exe, The Windows SDK must be installed, this is not a native binary.
action.notable.param.rule_title = Windows Server Software Component GACUtil Install to GAC
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=gacutil.exe Processes.process IN ("*-i *","*/i *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_server_software_component_gacutil_install_to_gac_filter`

[ESCU - Windows Service Create Kernel Mode Driver - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifes a new kernel driver being added to Windows using sc.exe. Adding a Kernel driver is not common day to day and should be investigated to further understand the source.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003", "T1543", "T1068"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifes a new kernel driver being added to Windows using sc.exe. Adding a Kernel driver is not common day to day and should be investigated to further understand the source.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present based on common applications adding new drivers, however, filter as needed.
action.escu.creation_date = 2022-05-05
action.escu.modification_date = 2022-05-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Create Kernel Mode Driver - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Drivers", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = Service control, $process_name$, loaded a new kernel mode driver on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 48}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Create Kernel Mode Driver - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 80, "impact": 60, "mitre_attack": ["T1543.003", "T1543", "T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifes a new kernel driver being added to Windows using sc.exe. Adding a Kernel driver is not common day to day and should be investigated to further understand the source.
action.notable.param.rule_title = Windows Service Create Kernel Mode Driver
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process="*kernel*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_kernel_mode_driver_filter`

[ESCU - Windows Service Create RemComSvc - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies RemComSvc installed on an endpoint. This typically occurs when someone is moving laterally with RemCom.exe.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies RemComSvc installed on an endpoint. This typically occurs when someone is moving laterally with RemCom.exe.
action.escu.how_to_implement = To implement this analytic, the Windows EventCode 7045 will need to be logged. The Windows TA for Splunk is also recommended.
action.escu.known_false_positives = False positives may be present, filter as needed based on administrative activity.
action.escu.creation_date = 2023-03-20
action.escu.modification_date = 2023-03-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Create RemComSvc - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = A new service was created related to RemCom on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 32}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Create RemComSvc - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 80, "impact": 40, "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045 ServiceName="RemCom Service" | stats count min(_time) as firstTime max(_time) as lastTime by dest ImagePath ServiceName ServiceType   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_remcomsvc_filter`

[ESCU - Windows Service Create SliverC2 - Rule]
action.escu = 0
action.escu.enabled = 1
description = When an adversary utilizes SliverC2 to laterally move with the Psexec module, it will create a service with the name and description of "Sliver" and "Sliver Implant". Note that these may be easily changed and are specific to only SliverC2. We have also created the same regex as Microsoft has outlined to attempt to capture the suspicious service path (regex101 reference).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = When an adversary utilizes SliverC2 to laterally move with the Psexec module, it will create a service with the name and description of "Sliver" and "Sliver Implant". Note that these may be easily changed and are specific to only SliverC2. We have also created the same regex as Microsoft has outlined to attempt to capture the suspicious service path (regex101 reference).
action.escu.how_to_implement = To implement this analytic, the Windows EventCode 7045 will need to be logged from the System Event log. The Windows TA for Splunk is also recommended.
action.escu.known_false_positives = False positives should be limited, but if another service out there is named Sliver, filtering may be needed.
action.escu.creation_date = 2023-03-03
action.escu.modification_date = 2023-03-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Create SliverC2 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["BishopFox Sliver Adversary Emulation Framework"]
action.risk = 1
action.risk.param._risk_message = A user mode service was created on $dest$ related to SliverC2.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Create SliverC2 - Rule
action.correlationsearch.annotations = {"analytic_story": ["BishopFox Sliver Adversary Emulation Framework"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = When an adversary utilizes SliverC2 to laterally move with the Psexec module, it will create a service with the name and description of "Sliver" and "Sliver Implant". Note that these may be easily changed and are specific to only SliverC2. We have also created the same regex as Microsoft has outlined to attempt to capture the suspicious service path (regex101 reference).
action.notable.param.rule_title = Windows Service Create SliverC2
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045 ServiceName="sliver" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_sliverc2_filter`

[ESCU - Windows Service Create with Tscon - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential RDP Hijacking attempts by monitoring a series of actions taken by an attacker to gain unauthorized access to a remote system. The attacker first runs the quser command to query the remote host for disconnected user sessions. Upon identifying a disconnected session, they use the sc.exe command to create a new Windows service with a binary path that launches tscon.exe. By specifying the disconnected session ID and a destination ID, the attacker can transfer the disconnected session to a new RDP session, effectively hijacking the user's session. This analytic allows security teams to detect and respond to RDP Hijacking attempts, mitigating potential risks and impacts on targeted systems.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1563.002", "T1563", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects potential RDP Hijacking attempts by monitoring a series of actions taken by an attacker to gain unauthorized access to a remote system. The attacker first runs the quser command to query the remote host for disconnected user sessions. Upon identifying a disconnected session, they use the sc.exe command to create a new Windows service with a binary path that launches tscon.exe. By specifying the disconnected session ID and a destination ID, the attacker can transfer the disconnected session to a new RDP session, effectively hijacking the user's session. This analytic allows security teams to detect and respond to RDP Hijacking attempts, mitigating potential risks and impacts on targeted systems.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may arise in the RDP Hijacking analytic when legitimate administrators access remote sessions for maintenance or troubleshooting purposes. These activities might resemble an attacker''s attempt to hijack a disconnected session, leading to false alarms. To mitigate the risk of false positives and improve the overall security posture, organizations can implement Group Policy to automatically disconnect RDP sessions when they are complete. By enforcing this policy, administrators ensure that disconnected sessions are promptly terminated, reducing the window of opportunity for an attacker to hijack a session. Additionally, organizations can also implement access control mechanisms and monitor the behavior of privileged accounts to further enhance security and reduce the chances of false positives in RDP Hijacking detection.
action.escu.creation_date = 2023-03-29
action.escu.modification_date = 2023-03-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Create with Tscon - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to hijack a RDP session.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 64}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Create with Tscon - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1563.002", "T1563", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects potential RDP Hijacking attempts by monitoring a series of actions taken by an attacker to gain unauthorized access to a remote system. The attacker first runs the quser command to query the remote host for disconnected user sessions. Upon identifying a disconnected session, they use the sc.exe command to create a new Windows service with a binary path that launches tscon.exe. By specifying the disconnected session ID and a destination ID, the attacker can transfer the disconnected session to a new RDP session, effectively hijacking the user's session. This analytic allows security teams to detect and respond to RDP Hijacking attempts, mitigating potential risks and impacts on targeted systems.
action.notable.param.rule_title = Windows Service Create with Tscon
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process="*/dest:rdp-tcp*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_with_tscon_filter`

[ESCU - Windows Service Created with Suspicious Service Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytics uses Windows Event Id 7045, `New Service Was Installed`, to identify the creation of a Windows Service where the service binary path path is located in a non-common Service folder in Windows. Red Teams and adversaries alike may create malicious Services for lateral movement or remote code execution as well as persistence and execution. The Clop ransomware has also been seen in the wild abusing Windows services.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytics uses Windows Event Id 7045, `New Service Was Installed`, to identify the creation of a Windows Service where the service binary path path is located in a non-common Service folder in Windows. Red Teams and adversaries alike may create malicious Services for lateral movement or remote code execution as well as persistence and execution. The Clop ransomware has also been seen in the wild abusing Windows services.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = Legitimate applications may install services with uncommon services paths.
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Created with Suspicious Service Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Clop Ransomware", "Active Directory Lateral Movement", "Brute Ratel C4", "Qakbot", "Snake Malware", "Flax Typhoon"]
action.risk = 1
action.risk.param._risk_message = A service $Service_File_Name$ was created from a non-standard path using $Service_Name$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "Service_File_Name", "risk_object_type": "other", "risk_score": 56}, {"risk_object_field": "Service_Name", "risk_object_type": "other", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Created with Suspicious Service Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware", "Active Directory Lateral Movement", "Brute Ratel C4", "Qakbot", "Snake Malware", "Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1569", "T1569.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytics uses Windows Event Id 7045, `New Service Was Installed`, to identify the creation of a Windows Service where the service binary path path is located in a non-common Service folder in Windows. Red Teams and adversaries alike may create malicious Services for lateral movement or remote code execution as well as persistence and execution. The Clop ransomware has also been seen in the wild abusing Windows services.
action.notable.param.rule_title = Windows Service Created with Suspicious Service Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_system` EventCode=7045  Service_File_Name = "*.exe" NOT (Service_File_Name IN ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_created_with_suspicious_service_path_filter`

[ESCU - Windows Service Created Within Public Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytc uses Windows Event Id 7045, `New Service Was Installed`, to identify the creation of a Windows Service where the service binary path is located in public paths. This behavior could represent the installation of a malicious service. Red Teams and adversaries alike may create malicious Services for lateral movement or remote code execution
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytc uses Windows Event Id 7045, `New Service Was Installed`, to identify the creation of a Windows Service where the service binary path is located in public paths. This behavior could represent the installation of a malicious service. Red Teams and adversaries alike may create malicious Services for lateral movement or remote code execution
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = Legitimate applications may install services with uncommon services paths.
action.escu.creation_date = 2021-11-22
action.escu.modification_date = 2021-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Created Within Public Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Lateral Movement", "Snake Malware"]
action.risk = 1
action.risk.param._risk_message = A Windows Service $Service_File_Name$ with a public path was created on $ComputerName
action.risk.param._risk = [{"risk_object_field": "Service_File_Name", "risk_object_type": "other", "risk_score": 54}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Created Within Public Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Snake Malware"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytc uses Windows Event Id 7045, `New Service Was Installed`, to identify the creation of a Windows Service where the service binary path is located in public paths. This behavior could represent the installation of a malicious service. Red Teams and adversaries alike may create malicious Services for lateral movement or remote code execution
action.notable.param.rule_title = Windows Service Created Within Public Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045  Service_File_Name = "*\.exe" NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_created_within_public_path_filter`

[ESCU - Windows Service Creation on Remote Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `sc.exe` with command-line arguments utilized to create a Windows Service on a remote endpoint. Red Teams and adversaries alike may abuse the Service Control Manager for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `sc.exe` with command-line arguments utilized to create a Windows Service on a remote endpoint. Red Teams and adversaries alike may abuse the Service Control Manager for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may create Windows Services on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-10
action.escu.modification_date = 2021-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Creation on Remote Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A Windows Service was created on a remote endpoint from $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Creation on Remote Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `sc.exe` with command-line arguments utilized to create a Windows Service on a remote endpoint. Red Teams and adversaries alike may abuse the Service Control Manager for lateral movement and remote code execution.
action.notable.param.rule_title = Windows Service Creation on Remote Endpoint
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*create* AND Processes.process=*binpath*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_creation_on_remote_endpoint_filter`

[ESCU - Windows Service Creation Using Registry Entry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects when reg.exe modify registry keys that define Windows services and their configurations in Windows to detect potential threats earlier and mitigate the risks. This detection is made by a Splunk query that searches for specific keywords in the process name, parent process name, user, and process ID. This detection is important because it suggests that an attacker has modified the registry keys that define Windows services and their configurations, which can allow them to maintain access to the system and potentially move laterally within the network. It is a common technique used by attackers to gain persistence on a compromised system and its impact can lead to data theft, ransomware, or other damaging outcomes. False positives can occur since legitimate uses of reg.exe to modify registry keys for Windows services can also trigger this alert. Next steps include reviewing the process and user context of the reg.exe activity and identify any other concurrent processes that might be associated with the attack upon triage.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.011"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects when reg.exe modify registry keys that define Windows services and their configurations in Windows to detect potential threats earlier and mitigate the risks. This detection is made by a Splunk query that searches for specific keywords in the process name, parent process name, user, and process ID. This detection is important because it suggests that an attacker has modified the registry keys that define Windows services and their configurations, which can allow them to maintain access to the system and potentially move laterally within the network. It is a common technique used by attackers to gain persistence on a compromised system and its impact can lead to data theft, ransomware, or other damaging outcomes. False positives can occur since legitimate uses of reg.exe to modify registry keys for Windows services can also trigger this alert. Next steps include reviewing the process and user context of the reg.exe activity and identify any other concurrent processes that might be associated with the attack upon triage.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
action.escu.known_false_positives = Third party tools may used this technique to create services but not so common.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Creation Using Registry Entry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Registry Abuse", "Brute Ratel C4"]
action.risk = 1
action.risk.param._risk_message = A Windows Service was created on a endpoint from $dest$ using a registry entry
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Creation Using Registry Entry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Registry Abuse", "Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1574.011"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects when reg.exe modify registry keys that define Windows services and their configurations in Windows to detect potential threats earlier and mitigate the risks. This detection is made by a Splunk query that searches for specific keywords in the process name, parent process name, user, and process ID. This detection is important because it suggests that an attacker has modified the registry keys that define Windows services and their configurations, which can allow them to maintain access to the system and potentially move laterally within the network. It is a common technique used by attackers to gain persistence on a compromised system and its impact can lead to data theft, ransomware, or other damaging outcomes. False positives can occur since legitimate uses of reg.exe to modify registry keys for Windows services can also trigger this alert. Next steps include reviewing the process and user context of the reg.exe activity and identify any other concurrent processes that might be associated with the attack upon triage.
action.notable.param.rule_title = Windows Service Creation Using Registry Entry
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services*" Registry.registry_value_name = ImagePath) BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_creation_using_registry_entry_filter`

[ESCU - Windows Service Deletion In Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a service being deleted from the Windows Registry under CurrentControlSet\Services. Adversaries may delete a service as part of defense evasion.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a service being deleted from the Windows Registry under CurrentControlSet\Services. Adversaries may delete a service as part of defense evasion.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
action.escu.known_false_positives = This event can be seen when administrator delete a service or uninstall/reinstall a software that creates service entry, but it is still recommended to check this alert with high priority.
action.escu.creation_date = 2022-08-24
action.escu.modification_date = 2022-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Deletion In Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Brute Ratel C4"]
action.risk = 1
action.risk.param._risk_message = A service was deleted on $dest$ within the Windows registry.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Deletion In Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brute Ratel C4"], "cis20": ["CIS 10"], "confidence": 30, "impact": 60, "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\CurrentControlSet\\Services*" AND (Registry.action = deleted OR (Registry.registry_value_name = DeleteFlag AND Registry.registry_value_data = 0x00000001 AND Registry.action=modified)) by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_value_name Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_deletion_in_registry_filter`

[ESCU - Windows Service Initiation on Remote Endpoint - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the execution of `sc.exe` with command-line arguments utilized to start a Windows Service on a remote endpoint. Red Teams and adversaries alike may abuse the Service Control Manager for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `sc.exe` with command-line arguments utilized to start a Windows Service on a remote endpoint. Red Teams and adversaries alike may abuse the Service Control Manager for lateral movement and remote code execution.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may start Windows Services on remote systems, but this activity is usually limited to a small set of hosts or users.
action.escu.creation_date = 2021-11-10
action.escu.modification_date = 2021-11-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Initiation on Remote Endpoint - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A Windows Service was started on a remote endpoint from $dest
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Initiation on Remote Endpoint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1543", "T1543.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the execution of `sc.exe` with command-line arguments utilized to start a Windows Service on a remote endpoint. Red Teams and adversaries alike may abuse the Service Control Manager for lateral movement and remote code execution.
action.notable.param.rule_title = Windows Service Initiation on Remote Endpoint
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*start*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_initiation_on_remote_endpoint_filter`

[ESCU - Windows Service Stop By Deletion - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies Windows Service Control, `sc.exe`, attempting to delete a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique to terminate security services or other related services to continue there objective and evade detections.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies Windows Service Control, `sc.exe`, attempting to delete a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique to terminate security services or other related services to continue there objective and evade detections.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = It is possible administrative scripts may start/stop/delete services. Filter as needed.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Stop By Deletion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to delete a service.
action.risk.param._risk = [{"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Stop By Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies Windows Service Control, `sc.exe`, attempting to delete a service. This is typically identified in parallel with other instances of service enumeration of attempts to stop a service and then delete it. Adversaries utilize this technique to terminate security services or other related services to continue there objective and evade detections.
action.notable.param.rule_title = Windows Service Stop By Deletion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = sc.exe OR Processes.original_file_name = sc.exe) Processes.process="* delete *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_by_deletion_filter`

[ESCU - Windows Service Stop Via Net  and SC Application - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies suspicious attempts to stop services on a system using either `net.exe` or `sc.exe`. This technique is used by adversaries to terminate security services or other related services to continue their objective and evade detections. This technique is also commonly used by ransomware threat actors to successfully encrypt databases or files being processed or used by Windows OS Services.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies suspicious attempts to stop services on a system using either `net.exe` or `sc.exe`. This technique is used by adversaries to terminate security services or other related services to continue their objective and evade detections. This technique is also commonly used by ransomware threat actors to successfully encrypt databases or files being processed or used by Windows OS Services.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Windows OS or software may stop and restart services due to some critical update.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Stop Via Net  and SC Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Prestige Ransomware", "Graceful Wipe Out Attack"]
action.risk = 1
action.risk.param._risk_message = $process$ was executed on $dest$ attempting to stop service.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Stop Via Net  and SC Application - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prestige Ransomware", "Graceful Wipe Out Attack"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name = "sc.exe" OR Processes.original_file_name= "sc.exe" AND Processes.process="*stop*" by  Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_via_net__and_sc_application_filter`

[ESCU - Windows Service Stop Win Updates - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a windows update service being disabled in Windows OS. This technique is being abused by adversaries or threat actors to add defense mechanisms to their malware implant in the targeted host. Disabling windows update will put the compromised host vulnerable in some zero day exploit or even some update features against threats. RedLine Stealer kills this service as part of its defense evasion mechanism.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a windows update service being disabled in Windows OS. This technique is being abused by adversaries or threat actors to add defense mechanisms to their malware implant in the targeted host. Disabling windows update will put the compromised host vulnerable in some zero day exploit or even some update features against threats. RedLine Stealer kills this service as part of its defense evasion mechanism.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints (like Windows system.log Event ID 7040)
action.escu.known_false_positives = Network administrator may disable this services as part of its audit process within the network. Filter is needed.
action.escu.creation_date = 2023-04-21
action.escu.modification_date = 2023-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Service Stop Win Updates - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["RedLine Stealer"]
action.risk = 1
action.risk.param._risk_message = Windows update services $service_name$ was being disabled on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Service Stop Win Updates - Rule
action.correlationsearch.annotations = {"analytic_story": ["RedLine Stealer"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1489"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7040 (service_name IN ("Update Orchestrator Service for Windows Update", "WaaSMedicSvc", "Windows Update") OR param1 IN ("UsoSvc", "WaaSMedicSvc", "wuauserv")) AND (param3=disabled OR start_mode = disabled) | stats count min(_time) as firstTime max(_time) as lastTime by Computer Error_Code service_name start_mode param1 param2 param3 param4 | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_win_updates_filter`

[ESCU - Windows SIP Provider Inventory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following inventory analytic is used with a PowerShell scripted inputs to capture all SIP providers on a Windows system. This analytic is used to identify potential malicious SIP providers that may be used to subvert trust controls. Upon review, look for new and non-standard paths for SIP providers.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1553.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following inventory analytic is used with a PowerShell scripted inputs to capture all SIP providers on a Windows system. This analytic is used to identify potential malicious SIP providers that may be used to subvert trust controls. Upon review, look for new and non-standard paths for SIP providers.
action.escu.how_to_implement = To implement this analytic, one must first perform inventory using a scripted inputs. Review the following Gist - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1
action.escu.known_false_positives = False positives are limited as this is a hunting query for inventory.
action.escu.creation_date = 2023-10-10
action.escu.modification_date = 2023-10-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows SIP Provider Inventory - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Subvert Trust Controls SIP and Trust Provider Hijacking"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows SIP Provider Inventory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Subvert Trust Controls SIP and Trust Provider Hijacking"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1553.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `subjectinterfacepackage` Dll=*\\*.dll | stats count min(_time) as firstTime max(_time) as lastTime values(Dll) by Path host| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_sip_provider_inventory_filter`

[ESCU - Windows SIP WinVerifyTrust Failed Trust Validation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes a Windows Event Log - CAPI2 - or CryptoAPI 2, to identify failed trust validation. Typically, this event log is meant for diagnosing PKI issues, however is a great source to identify failed trust validation. Note that this event log is noisy as it captures common PKI requests from many different processes. EventID 81 is generated anytime a trust validation fails. The description for EventID 81 is "The digital signature of the object did not verify." STRT tested this analytic using Mimikatz binary.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1553.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes a Windows Event Log - CAPI2 - or CryptoAPI 2, to identify failed trust validation. Typically, this event log is meant for diagnosing PKI issues, however is a great source to identify failed trust validation. Note that this event log is noisy as it captures common PKI requests from many different processes. EventID 81 is generated anytime a trust validation fails. The description for EventID 81 is "The digital signature of the object did not verify." STRT tested this analytic using Mimikatz binary.
action.escu.how_to_implement = To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational log within the Windows Event Log. Note this is a debug log for many purposes, and the analytic only focuses in on EventID 81. Review the following gist for additional enabling information.
action.escu.known_false_positives = False positives may be present in some instances of legitimate binaries with invalid signatures. Filter as needed.
action.escu.creation_date = 2023-10-10
action.escu.modification_date = 2023-10-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows SIP WinVerifyTrust Failed Trust Validation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Subvert Trust Controls SIP and Trust Provider Hijacking"]
action.risk = 1
action.risk.param._risk_message = Failed trust validation via the CryptoAPI 2 on $dest$ for a binary.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows SIP WinVerifyTrust Failed Trust Validation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Subvert Trust Controls SIP and Trust Provider Hijacking"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1553.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `capi2_operational` EventID=81 "The digital signature of the object did not verify." | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as lastTime by Computer, UserData_Xml | rename Computer as dest | `windows_sip_winverifytrust_failed_trust_validation_filter`

[ESCU - Windows Snake Malware File Modification Crmlog - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identfies a .crmlog written to windows\registration. Per the report, typically, this file has been found within the %windows%\Registration directory with the format of <RANDOM_GUID>.<RANDOM_GUID>.crmlog and is decrypted by Snake's kernel driver.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1027"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identfies a .crmlog written to windows\registration. Per the report, typically, this file has been found within the %windows%\Registration directory with the format of <RANDOM_GUID>.<RANDOM_GUID>.crmlog and is decrypted by Snake's kernel driver.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be present as the file pattern does match legitimate files on disk. It is possible other native tools write the same file name scheme.
action.escu.creation_date = 2023-05-10
action.escu.modification_date = 2023-05-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Snake Malware File Modification Crmlog - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Snake Malware"]
action.risk = 1
action.risk.param._risk_message = A file related to Snake Malware has been identified on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Snake Malware File Modification Crmlog - Rule
action.correlationsearch.annotations = {"analytic_story": ["Snake Malware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1027"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identfies a .crmlog written to windows\registration. Per the report, typically, this file has been found within the %windows%\Registration directory with the format of <RANDOM_GUID>.<RANDOM_GUID>.crmlog and is decrypted by Snake's kernel driver.
action.notable.param.rule_title = Windows Snake Malware File Modification Crmlog
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\registration\\*" AND  Filesystem.file_name="*.crmlog"  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_snake_malware_file_modification_crmlog_filter`

[ESCU - Windows Snake Malware Kernel Driver Comadmin - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the comadmin.dat file written to disk, which is related to Snake Malware. From the report, Snakes installer drops the kernel driver and a custom DLL which is used to load the driver into a single AES encrypted file on disk. Typically, this file is named comadmin.dat and is stored in the %windows%\system32\Com directory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.006"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the comadmin.dat file written to disk, which is related to Snake Malware. From the report, Snakes installer drops the kernel driver and a custom DLL which is used to load the driver into a single AES encrypted file on disk. Typically, this file is named comadmin.dat and is stored in the %windows%\system32\Com directory.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2023-05-11
action.escu.modification_date = 2023-05-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Snake Malware Kernel Driver Comadmin - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Snake Malware"]
action.risk = 1
action.risk.param._risk_message = A kernel driver comadmin.dat related to Snake Malware was written to disk on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Snake Malware Kernel Driver Comadmin - Rule
action.correlationsearch.annotations = {"analytic_story": ["Snake Malware"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1547.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the comadmin.dat file written to disk, which is related to Snake Malware. From the report, Snakes installer drops the kernel driver and a custom DLL which is used to load the driver into a single AES encrypted file on disk. Typically, this file is named comadmin.dat and is stored in the %windows%\system32\Com directory.
action.notable.param.rule_title = Windows Snake Malware Kernel Driver Comadmin
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\system32\\com\\*" AND Filesystem.file_name="comadmin.dat"  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_kernel_driver_comadmin_filter`

[ESCU - Windows Snake Malware Registry Modification wav OpenWithProgIds - Rule]
action.escu = 0
action.escu.enabled = 1
description = The follow analytic identifies the registry being modified at .wav\\OpenWithProgIds\, which is related to the Snake Malware campaign. Upon execution, Snake's WerFault.exe will attempt to decrypt an encrypted blob within the Windows registry that is typically found at HKLM:\SOFTWARE\Classes\.wav\OpenWithProgIds. The encrypted data includes the AES key, IV, and path that is used to find and decrypt the file containing Snake's kernel driver and kernel driver loader.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The follow analytic identifies the registry being modified at .wav\\OpenWithProgIds\, which is related to the Snake Malware campaign. Upon execution, Snake's WerFault.exe will attempt to decrypt an encrypted blob within the Windows registry that is typically found at HKLM:\SOFTWARE\Classes\.wav\OpenWithProgIds. The encrypted data includes the AES key, IV, and path that is used to find and decrypt the file containing Snake's kernel driver and kernel driver loader.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
action.escu.known_false_positives = False positives may be present and will require tuning based on program Ids in large organizations.
action.escu.creation_date = 2023-05-10
action.escu.modification_date = 2023-05-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Snake Malware Registry Modification wav OpenWithProgIds - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Snake Malware"]
action.risk = 1
action.risk.param._risk_message = A registry modification related to Snake Malware has been identified on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Snake Malware Registry Modification wav OpenWithProgIds - Rule
action.correlationsearch.annotations = {"analytic_story": ["Snake Malware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The follow analytic identifies the registry being modified at .wav\\OpenWithProgIds\, which is related to the Snake Malware campaign. Upon execution, Snake's WerFault.exe will attempt to decrypt an encrypted blob within the Windows registry that is typically found at HKLM:\SOFTWARE\Classes\.wav\OpenWithProgIds. The encrypted data includes the AES key, IV, and path that is used to find and decrypt the file containing Snake's kernel driver and kernel driver loader.
action.notable.param.rule_title = Windows Snake Malware Registry Modification wav OpenWithProgIds
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\.wav\\OpenWithProgIds\\*"  by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_snake_malware_registry_modification_wav_openwithprogids_filter`

[ESCU - Windows Snake Malware Service Create - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a new service WerFaultSvc being created with a binary path located in the windows winsxs path. Per the report, the Snake version primarily discussed in this advisory registers a service to maintain persistence on a system. Typically this service is named WerFaultSvc which we assess was used to blend in with the legitimate Windows service WerSvc. On boot, this service will execute Snakes WerFault.exe, which Snake developers chose to hide among the numerous valid Windows WerFault.exe files in the windows WinSxS directory. Executing WerFault.exe will start the process of decrypting Snakes components and loading them into memory.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1547.006", "T1569.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a new service WerFaultSvc being created with a binary path located in the windows winsxs path. Per the report, the Snake version primarily discussed in this advisory registers a service to maintain persistence on a system. Typically this service is named WerFaultSvc which we assess was used to blend in with the legitimate Windows service WerSvc. On boot, this service will execute Snakes WerFault.exe, which Snake developers chose to hide among the numerous valid Windows WerFault.exe files in the windows WinSxS directory. Executing WerFault.exe will start the process of decrypting Snakes components and loading them into memory.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
action.escu.known_false_positives = False positives should be limited as this is a strict primary indicator used by Snake Malware.
action.escu.creation_date = 2023-05-11
action.escu.modification_date = 2023-05-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Snake Malware Service Create - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Snake Malware"]
action.risk = 1
action.risk.param._risk_message = A service, WerFaultSvc, was created on $dest$ and is related to Snake Malware.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Snake Malware Service Create - Rule
action.correlationsearch.annotations = {"analytic_story": ["Snake Malware"], "cis20": ["CIS 10"], "confidence": 90, "impact": 80, "mitre_attack": ["T1547.006", "T1569.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a new service WerFaultSvc being created with a binary path located in the windows winsxs path. Per the report, the Snake version primarily discussed in this advisory registers a service to maintain persistence on a system. Typically this service is named WerFaultSvc which we assess was used to blend in with the legitimate Windows service WerSvc. On boot, this service will execute Snakes WerFault.exe, which Snake developers chose to hide among the numerous valid Windows WerFault.exe files in the windows WinSxS directory. Executing WerFault.exe will start the process of decrypting Snakes components and loading them into memory.
action.notable.param.rule_title = Windows Snake Malware Service Create
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_system` EventCode=7045  ImagePath="*\\windows\\winSxS\\*" ImagePath="*\Werfault.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_service_create_filter`

[ESCU - Windows Spearphishing Attachment Connect To None MS Office Domain - Rule]
action.escu = 0
action.escu.enabled = 1
description = this detection was designed to identifies suspicious office documents that connect to a website aside from Microsoft Office Domain. This technique was seen in several malicious documents that abuses .rels xml properties of MS office to connect or download malicious files. This hunting query can be a good pivot or guide to check what URL link it tries to connect, what domain, where the documents came from and how the connection happens.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = this detection was designed to identifies suspicious office documents that connect to a website aside from Microsoft Office Domain. This technique was seen in several malicious documents that abuses .rels xml properties of MS office to connect or download malicious files. This hunting query can be a good pivot or guide to check what URL link it tries to connect, what domain, where the documents came from and how the connection happens.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = Windows Office document may contain legitimate url link other than MS office Domain. filter is needed
action.escu.creation_date = 2023-02-15
action.escu.modification_date = 2023-02-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Spearphishing Attachment Connect To None MS Office Domain - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spearphishing Attachments", "AsyncRAT"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Spearphishing Attachment Connect To None MS Office Domain - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=22 Image IN ("*\\winword.exe","*\\excel.exe","*\\powerpnt.exe","*\\mspub.exe","*\\visio.exe","*\\wordpad.exe","*\\wordview.exe","*\\onenote.exe", "*\\onenotem.exe","*\\onenoteviewer.exe","*\\onenoteim.exe", "*\\msaccess.exe") AND NOT(QueryName IN ("*.office.com", "*.office.net")) | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryResults QueryStatus Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_connect_to_none_ms_office_domain_filter`

[ESCU - Windows Spearphishing Attachment Onenote Spawn Mshta - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies the latest behavior utilized by different malware families (including TA551, AsyncRat, Redline and DCRAT). This detection identifies onenote Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies the latest behavior utilized by different malware families (including TA551, AsyncRat, Redline and DCRAT). This detection identifies onenote Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = No false positives known. Filter as needed.
action.escu.creation_date = 2023-01-24
action.escu.modification_date = 2023-01-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Spearphishing Attachment Onenote Spawn Mshta - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "AsyncRAT"]
action.risk = 1
action.risk.param._risk_message = office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Spearphishing Attachment Onenote Spawn Mshta - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "AsyncRAT"], "cis20": ["CIS 10"], "confidence": 90, "impact": 90, "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, AsyncRat, Redline and DCRAT). This detection identifies onenote Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
action.notable.param.rule_title = Windows Spearphishing Attachment Onenote Spawn Mshta
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("onenote.exe", "onenotem.exe") `process_mshta` by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_onenote_spawn_mshta_filter`

[ESCU - Windows Special Privileged Logon On Multiple Hosts - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Event ID 4672 to identify a source user authenticating with special privileges across a large number remote endpoints. Specifically, the logic will trigger when a source user obtains special privileges across 30 or more target computers within a 5 minute timespan. Special privileges are assigned to a new logon session when sensitive privileges like SeDebugPrivilege and SeImpersonatePrivilege are assigned. This behavior could represent an adversary who is moving laterally and executing remote code across the network. It can also be triggered by other behavior like an adversary enumerating network shares. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1021.002", "T1135"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Event ID 4672 to identify a source user authenticating with special privileges across a large number remote endpoints. Specifically, the logic will trigger when a source user obtains special privileges across 30 or more target computers within a 5 minute timespan. Special privileges are assigned to a new logon session when sensitive privileges like SeDebugPrivilege and SeImpersonatePrivilege are assigned. This behavior could represent an adversary who is moving laterally and executing remote code across the network. It can also be triggered by other behavior like an adversary enumerating network shares. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting special logon events. The Advanced Security Audit policy setting `Audit Special Logon` within `Logon/Logoff` need to be enabled.
action.escu.known_false_positives = Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed.
action.escu.creation_date = 2023-03-27
action.escu.modification_date = 2023-03-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Special Privileged Logon On Multiple Hosts - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A source user $Caller_User_Name$ obtained special privileges on a large number of endpoints within 5 minutes.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "Caller_User_Name", "risk_object_type": "user", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Special Privileged Logon On Multiple Hosts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Privilege Escalation", "Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1087", "T1021.002", "T1135"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Event ID 4672 to identify a source user authenticating with special privileges across a large number remote endpoints. Specifically, the logic will trigger when a source user obtains special privileges across 30 or more target computers within a 5 minute timespan. Special privileges are assigned to a new logon session when sensitive privileges like SeDebugPrivilege and SeImpersonatePrivilege are assigned. This behavior could represent an adversary who is moving laterally and executing remote code across the network. It can also be triggered by other behavior like an adversary enumerating network shares. As environments differ across organizations, security teams should customize the thresholds of this detection as needed.
action.notable.param.rule_title = Windows Special Privileged Logon On Multiple Hosts
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4672 AND NOT(Caller_User_Name IN ("DWM-1","DWM-2","DWM-3","LOCAL SERVICE","NETWORK SERVICE","SYSTEM","*$")) | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets values(PrivilegeList) as privileges by _time, Caller_User_Name | where unique_targets > 30 | `windows_special_privileged_logon_on_multiple_hosts_filter`

[ESCU - Windows SQL Spawning CertUtil - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the use of certutil to download software, a behavior exhibited by the threat actor Flax Typhoon. This actor deploys a VPN connection by downloading an executable file for SoftEther VPN from their network infrastructure using one of several LOLBins, including certutil. The actor then uses the Service Control Manager (SCM) to create a Windows service that launches the VPN connection automatically when the system starts. This behavior allows the actor to monitor the availability of the compromised system and establish an RDP connection. This analytic identifies this behavior by monitoring for the use of certutil in conjunction with the downloading of software. This behavior is worth identifying for a SOC as it indicates a potential compromise of the system and the establishment of a persistent threat. If a true positive is found, it suggests an attacker has gained access to the environment and is attempting to maintain that access, potentially leading to further malicious activities such as data theft or ransomware attacks. Be aware of potential false positives - legitimate uses of certutil in your environment may cause benign activities to be flagged. Upon triage, review the command executed and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the use of certutil to download software, a behavior exhibited by the threat actor Flax Typhoon. This actor deploys a VPN connection by downloading an executable file for SoftEther VPN from their network infrastructure using one of several LOLBins, including certutil. The actor then uses the Service Control Manager (SCM) to create a Windows service that launches the VPN connection automatically when the system starts. This behavior allows the actor to monitor the availability of the compromised system and establish an RDP connection. This analytic identifies this behavior by monitoring for the use of certutil in conjunction with the downloading of software. This behavior is worth identifying for a SOC as it indicates a potential compromise of the system and the establishment of a persistent threat. If a true positive is found, it suggests an attacker has gained access to the environment and is attempting to maintain that access, potentially leading to further malicious activities such as data theft or ransomware attacks. Be aware of potential false positives - legitimate uses of certutil in your environment may cause benign activities to be flagged. Upon triage, review the command executed and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = The occurrence of false positives should be minimal, given that the SQL agent does not typically download software using CertUtil.
action.escu.creation_date = 2023-08-25
action.escu.modification_date = 2023-08-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows SQL Spawning CertUtil - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Flax Typhoon"]
action.risk = 1
action.risk.param._risk_message = $process_name$ was launched on $dest$ by $user$. This behavior is uncommon with the SQL process identified.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows SQL Spawning CertUtil - Rule
action.correlationsearch.annotations = {"analytic_story": ["Flax Typhoon"], "cis20": ["CIS 10"], "confidence": 100, "impact": 90, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the use of certutil to download software, a behavior exhibited by the threat actor Flax Typhoon. This actor deploys a VPN connection by downloading an executable file for SoftEther VPN from their network infrastructure using one of several LOLBins, including certutil. The actor then uses the Service Control Manager (SCM) to create a Windows service that launches the VPN connection automatically when the system starts. This behavior allows the actor to monitor the availability of the compromised system and establish an RDP connection. This analytic identifies this behavior by monitoring for the use of certutil in conjunction with the downloading of software. This behavior is worth identifying for a SOC as it indicates a potential compromise of the system and the establishment of a persistent threat. If a true positive is found, it suggests an attacker has gained access to the environment and is attempting to maintain that access, potentially leading to further malicious activities such as data theft or ransomware attacks. Be aware of potential false positives - legitimate uses of certutil in your environment may cause benign activities to be flagged. Upon triage, review the command executed and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.notable.param.rule_title = Windows SQL Spawning CertUtil
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("sqlservr.exe", "sqlagent.exe", "sqlps.exe", "launchpad.exe", "sqldumper.exe") `process_certutil` (Processes.process=*urlcache* Processes.process=*split*) OR Processes.process=*urlcache* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sql_spawning_certutil_filter`

[ESCU - Windows Steal Authentication Certificates - ESC1 Authentication - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when a suspicious certificate is granted using Active Directory Certificate Services (AD CS) with a Subject Alternative Name (SAN) and then immediately used for authentication. This action alone may not be malicious, however improperly configured certificate templates can be abused to permit privilege escalation and environment compromise due to over permissive settings (AD CS ESC1).
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649", "T1550"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when a suspicious certificate is granted using Active Directory Certificate Services (AD CS) with a Subject Alternative Name (SAN) and then immediately used for authentication. This action alone may not be malicious, however improperly configured certificate templates can be abused to permit privilege escalation and environment compromise due to over permissive settings (AD CS ESC1).
action.escu.how_to_implement = To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. Recommend throttle correlation by RequestId/ssl_serial at minimum.
action.escu.known_false_positives = False positives may be generated in environments where administrative users or processes are allowed to generate certificates with Subject Alternative Names for authentication. Sources or templates used in these processes may need to be tuned out for accurate function.
action.escu.creation_date = 2023-05-25
action.escu.modification_date = 2023-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates - ESC1 Authentication - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = Possible AD CS ESC1 authentication on $dest$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"threat_object_field": "ssl_hash", "threat_object_type": "other"}, {"threat_object_field": "ssl_serial", "threat_object_type": "other"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates - ESC1 Authentication - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1649", "T1550"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when a suspicious certificate is granted using Active Directory Certificate Services (AD CS) with a Subject Alternative Name (SAN) and then immediately used for authentication. This action alone may not be malicious, however improperly configured certificate templates can be abused to permit privilege escalation and environment compromise due to over permissive settings (AD CS ESC1).
action.notable.param.rule_title = Windows Steal Authentication Certificates - ESC1 Authentication
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode IN (4887) Attributes="*SAN:*upn*" Attributes="*CertificateTemplate:*" | stats count min(_time) as firstTime max(_time) as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | rex field=Attributes "(?i)CertificateTemplate:(?<object>[^\r\n]+)" | rex field=Attributes "(?i)ccm:(?<req_src>[^\r\n]+)" | rex max_match=10 field=Attributes "(?i)(upn=(?<req_user_1>[^\r\n&]+))" | rex max_match=10 field=Attributes "(?i)(dns=(?<req_dest_1>[^\r\n&]+))" | rex field=Requester "(.+\\\\)?(?<src_user>[^\r\n]+)" | rename Attributes as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, Requester as ssl_subject_common_name | eval user = lower(coalesce(req_user_1,req_user_2))  | join user [ | search `wineventlog_security` EventCode=4768 CertThumbprint=* | rename TargetUserName as user, Computer as auth_dest, IpAddress as auth_src | fields auth_src,auth_dest,user ] | eval src = upper(coalesce(auth_src,req_src)), dest = upper(coalesce(auth_dest,req_dest_1,req_dest_2)), risk_score = 90 | eval flavor_text = case(signature_id=="4887", "User account [".'user'."] authenticated after a suspicious certificate was issued for it by [".'src_user'."] using certificate request ID: ".'ssl_serial') | fields - req_* auth_* | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates___esc1_authentication_filter`

[ESCU - Windows Steal Authentication Certificates Certificate Issued - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when a new certificate is issued against the Certificate Services - AD CS. By its very nature this is not malicious, but should be tracked and correlated with other events related to certificates being issued. When the CA issues the certificate, it creates EID 4887 'Certificate Services approved a certificate request and issued a certificate". The event supplies the requester user context, the DNS hostname of the machine they requested the certificate from, and the time they requested the certificate. The attributes fields in these event commonly has values for CDC, RMD, and CCM which correspond to Client DC, Request Machine DNS name, and Cert Client Machine, respectively.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when a new certificate is issued against the Certificate Services - AD CS. By its very nature this is not malicious, but should be tracked and correlated with other events related to certificates being issued. When the CA issues the certificate, it creates EID 4887 'Certificate Services approved a certificate request and issued a certificate". The event supplies the requester user context, the DNS hostname of the machine they requested the certificate from, and the time they requested the certificate. The attributes fields in these event commonly has values for CDC, RMD, and CCM which correspond to Client DC, Request Machine DNS name, and Cert Client Machine, respectively.
action.escu.how_to_implement = To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference.
action.escu.known_false_positives = False positives will be generated based on normal certificates issued. Leave enabled to generate Risk, as this is meant to be an anomaly analytic.
action.escu.creation_date = 2023-02-06
action.escu.modification_date = 2023-02-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates Certificate Issued - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = A certificate was issued to $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 8}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates Certificate Issued - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 80, "impact": 10, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security`  EventCode=4887 | stats count min(_time) as firstTime max(_time) as lastTime by dest, name, Requester, action, Attributes, Subject | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_steal_authentication_certificates_certificate_issued_filter`

[ESCU - Windows Steal Authentication Certificates Certificate Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when a new certificate is requested against the Certificate Services - AD CS. By its very nature this is not malicious, but should be tracked and correlated with other events related to certificate requests. When an account requests a certificate, the CA generates event ID (EID) 4886 "Certificate Services received a certificate request".
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when a new certificate is requested against the Certificate Services - AD CS. By its very nature this is not malicious, but should be tracked and correlated with other events related to certificate requests. When an account requests a certificate, the CA generates event ID (EID) 4886 "Certificate Services received a certificate request".
action.escu.how_to_implement = To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference.
action.escu.known_false_positives = False positives will be generated based on normal certificate requests. Leave enabled to generate Risk, as this is meant to be an anomaly analytic.
action.escu.creation_date = 2023-02-06
action.escu.modification_date = 2023-02-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates Certificate Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = A certificate was requested by $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 8}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates Certificate Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 80, "impact": 10, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4886 | stats count min(_time) as firstTime max(_time) as lastTime by dest, name, Requester, action, Attributes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certificate_request_filter`

[ESCU - Windows Steal Authentication Certificates CertUtil Backup - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is focused on CertUtil.exe performing a backup of the Certificate Store. Typically, administrators may perform this task to migrate or perform backups of the store, however it may be found uncommon in most organizations.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic is focused on CertUtil.exe performing a backup of the Certificate Store. Typically, administrators may perform this task to migrate or perform backups of the store, however it may be found uncommon in most organizations.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be generated based on normal certificate store backups. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. If CS backups are not normal, enable as TTP.
action.escu.creation_date = 2023-02-06
action.escu.modification_date = 2023-02-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates CertUtil Backup - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to backup the Certificate Store.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 40}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 40}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates CertUtil Backup - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process IN ("*-backupdb *", "*-backup *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certutil_backup_filter`

[ESCU - Windows Steal Authentication Certificates CryptoAPI - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes a Windows Event Log - CAPI2 - or CryptoAPI 2, to identify suspicious certificate extraction. Typically, this event log is meant for diagnosing PKI issues, however is a great source to identify certificate exports. Note that this event log is noisy as it captures common PKI requests from many different processes. EventID 70 is generated anytime a certificate is exported. The description for EventID 70 is "Acquire Certificate Private Key". STRT tested this analytic using Mimikatz binary and the implementation of Mimikatz in Cobalt Strike.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes a Windows Event Log - CAPI2 - or CryptoAPI 2, to identify suspicious certificate extraction. Typically, this event log is meant for diagnosing PKI issues, however is a great source to identify certificate exports. Note that this event log is noisy as it captures common PKI requests from many different processes. EventID 70 is generated anytime a certificate is exported. The description for EventID 70 is "Acquire Certificate Private Key". STRT tested this analytic using Mimikatz binary and the implementation of Mimikatz in Cobalt Strike.
action.escu.how_to_implement = To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational log within the Windows Event Log. Note this is a debug log for many purposes, and the analytic only focuses in on EventID 70. Review the following gist for additional enabling information.
action.escu.known_false_positives = False positives may be present in some instances of legitimate applications requiring to export certificates. Filter as needed.
action.escu.creation_date = 2023-02-08
action.escu.modification_date = 2023-02-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates CryptoAPI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = Certificates were exported via the CryptoAPI 2 on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates CryptoAPI - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 80, "impact": 30, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `capi2_operational` EventCode=70 | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as lastTime by Computer, UserData_Xml | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_cryptoapi_filter`

[ESCU - Windows Steal Authentication Certificates CS Backup - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when the Active Directory Certificate Services store is backed up utilizing Event ID 4876. This event triggers whenever the backup occurs in the UI of CertSrv.msc or via CertUtil.exe -BackupDB occurs.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when the Active Directory Certificate Services store is backed up utilizing Event ID 4876. This event triggers whenever the backup occurs in the UI of CertSrv.msc or via CertUtil.exe -BackupDB occurs.
action.escu.how_to_implement = To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 128 of first reference.
action.escu.known_false_positives = False positives will be generated based on normal certificate store backups. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. If CS backups are not normal, enable as TTP.
action.escu.creation_date = 2023-02-06
action.escu.modification_date = 2023-02-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates CS Backup - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = The Active Directory Certiciate Services was backed up on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates CS Backup - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 80, "impact": 50, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4876| stats count min(_time) as firstTime max(_time) as lastTime by dest, name, action, Caller_Domain ,Caller_User_Name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_cs_backup_filter`

[ESCU - Windows Steal Authentication Certificates - ESC1 Abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies when a new certificate is requested and/or granted against the Active Directory Certificate Services (AD CS) using a Subject Alternative Name (SAN). This action by its self is not malicious, however improperly configured certificate templates can be abused to permit privilege escalation and environment compromise due to over permissive settings (AD CS ESC1)
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies when a new certificate is requested and/or granted against the Active Directory Certificate Services (AD CS) using a Subject Alternative Name (SAN). This action by its self is not malicious, however improperly configured certificate templates can be abused to permit privilege escalation and environment compromise due to over permissive settings (AD CS ESC1)
action.escu.how_to_implement = To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. Recommend throttle correlation by RequestId/ssl_serial at minimum.
action.escu.known_false_positives = False positives may be generated in environments where administrative users or processes are allowed to generate certificates with Subject Alternative Names. Sources or templates used in these processes may need to be tuned out for accurate function.
action.escu.creation_date = 2023-05-25
action.escu.modification_date = 2023-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates - ESC1 Abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = Possible AD CS ESC1 activity by $src_user$ - $flavor_text$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "src_user", "risk_object_type": "user", "risk_score": 60}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 60}, {"threat_object_field": "ssl_hash", "threat_object_type": "other"}, {"threat_object_field": "ssl_serial", "threat_object_type": "other"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates - ESC1 Abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 60, "impact": 100, "mitre_attack": ["T1649"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies when a new certificate is requested and/or granted against the Active Directory Certificate Services (AD CS) using a Subject Alternative Name (SAN). This action by its self is not malicious, however improperly configured certificate templates can be abused to permit privilege escalation and environment compromise due to over permissive settings (AD CS ESC1)
action.notable.param.rule_title = Windows Steal Authentication Certificates - ESC1 Abuse
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode IN (4886,4887) Attributes="*SAN:*upn*" Attributes="*CertificateTemplate:*" | stats count min(_time) as firstTime max(_time) as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | rex field=Attributes "(?i)CertificateTemplate:(?<object>[^\r\n]+)" | rex field=Attributes "(?i)ccm:(?<req_src>[^\r\n]+)" | rex max_match=10 field=Attributes "(?i)(upn=(?<req_user_1>[^\r\n&]+))" | rex max_match=10 field=Attributes "(?i)(dns=(?<req_dest_1>[^\r\n&]+))" | rex field=Requester "(.+\\\\)?(?<src_user>[^\r\n]+)" | eval flavor_text = case(EventCode=="4886","A suspicious certificate was requested using request ID: ".'RequestId',EventCode=="4887", "A suspicious certificate was issued using request ID: ".'RequestId'.". To revoke this certifacte use this request ID or the SSL fingerprint [".'ssl_hash'."]"), dest = upper(coalesce(req_dest_1,req_dest_2)), src = upper(coalesce(req_src,Computer)) | fields - req_* | rename Attributes as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, Requester as ssl_subject_common_name| `windows_steal_authentication_certificates___esc1_abuse_filter`

[ESCU - Windows Steal Authentication Certificates Export Certificate - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of export-certificate, the PowerShell cmdlet, being utilized on the command-line in an attempt to export the certifcate from the local Windows Certificate Store.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of export-certificate, the PowerShell cmdlet, being utilized on the command-line in an attempt to export the certifcate from the local Windows Certificate Store.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Filtering may be requried based on automated utilities and third party applications that may export certificates.
action.escu.creation_date = 2023-02-01
action.escu.modification_date = 2023-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates Export Certificate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export a certificate from the local Windows Certificate Store.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 36}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates Export Certificate - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-certificate*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_export_certificate_filter`

[ESCU - Windows Steal Authentication Certificates Export PfxCertificate - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the use of export-pfxcertificate, the PowerShell cmdlet, being utilized on the command-line in an attempt to export the certifcate from the local Windows Certificate Store.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of export-pfxcertificate, the PowerShell cmdlet, being utilized on the command-line in an attempt to export the certifcate from the local Windows Certificate Store.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Filtering may be requried based on automated utilities and third party applications that may export certificates.
action.escu.creation_date = 2023-02-01
action.escu.modification_date = 2023-02-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal Authentication Certificates Export PfxCertificate - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Certificate Services"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export a certificate from the local Windows Certificate Store.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 36}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal Authentication Certificates Export PfxCertificate - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Certificate Services"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1649"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-pfxcertificate*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_export_pfxcertificate_filter`

[ESCU - Windows Steal or Forge Kerberos Tickets Klist - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of Windows OS klist.exe tool. This tool is being abused or used by several post exploitation tool such as winpeas that being used by ransomware prestige to display or gather list of currently cached kerberos ticket. This cahced data can be used for lateral movement or even privilege escalation on the targeted host. This hunting query can be a good pivot in possible kerberos attack or pass the hash technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1558"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of Windows OS klist.exe tool. This tool is being abused or used by several post exploitation tool such as winpeas that being used by ransomware prestige to display or gather list of currently cached kerberos ticket. This cahced data can be used for lateral movement or even privilege escalation on the targeted host. This hunting query can be a good pivot in possible kerberos attack or pass the hash technique.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Steal or Forge Kerberos Tickets Klist - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Steal or Forge Kerberos Tickets Klist - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1558"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="klist.exe" OR Processes.original_file_name = "klist.exe" Processes.parent_process_name IN ("cmd.exe", "powershell*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_or_forge_kerberos_tickets_klist_filter`

[ESCU - Windows Suspect Process With Authentication Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies executables running from public or temporary locations that are communicating over windows domain authentication ports/protocol. The ports/protocols include LDAP(389), LDAPS(636), and Kerberos(88). Authentications from applications  running from user controlled locations may not be malicious, however actors often attempt to access domain resources after initial  compromise from executables in these locations.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1087", "T1087.002", "T1204", "T1204.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This analytic identifies executables running from public or temporary locations that are communicating over windows domain authentication ports/protocol. The ports/protocols include LDAP(389), LDAPS(636), and Kerberos(88). Authentications from applications  running from user controlled locations may not be malicious, however actors often attempt to access domain resources after initial  compromise from executables in these locations.
action.escu.how_to_implement = To implement this analytic, Sysmon should be installed in the environment and generating network events for  userland and/or known public writable locations.
action.escu.known_false_positives = Known applications running from these locations for legitimate purposes. Targeting only kerberos (port 88) may significantly reduce noise.
action.escu.creation_date = 2023-06-13
action.escu.modification_date = 2023-06-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Suspect Process With Authentication Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = The process $process_name$ on $src$ has been communicating with $dest$ on $dest_port$.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"threat_object_field": "process_name", "threat_object_type": "other"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Suspect Process With Authentication Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1087", "T1087.002", "T1204", "T1204.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.process_id) as process_id  from datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port IN ("88","389","636")  AND All_Traffic.app IN ("*\\users\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*")  by All_Traffic.app,All_Traffic.src,All_Traffic.src_ip,All_Traffic.user,All_Traffic.dest,All_Traffic.dest_ip,All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)`  | rex field=app ".*\\\(?<process_name>.*)$" | rename app as process | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `windows_suspect_process_with_authentication_traffic_filter`

[ESCU - Windows System Binary Proxy Execution Compiled HTML File Decompile - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the decompile parameter with the HTML Help application, HH.exe. This is a uncommon command to see ran and behavior. Most recently this was seen in a APT41 campaign where a CHM file was delivered and a script inside used a technique for running an arbitrary command in a CHM file via an ActiveX object. This unpacks an HTML help file to a specified path for launching the next stage.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1218.001", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the decompile parameter with the HTML Help application, HH.exe. This is a uncommon command to see ran and behavior. Most recently this was seen in a APT41 campaign where a CHM file was delivered and a script inside used a technique for running an arbitrary command in a CHM file via an ActiveX object. This unpacks an HTML help file to a specified path for launching the next stage.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, filter as needed.
action.escu.creation_date = 2022-08-31
action.escu.modification_date = 2022-08-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Binary Proxy Execution Compiled HTML File Decompile - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious Compiled HTML Activity", "Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = $process_name$ has been identified using decompile against a CHM on $dest$ under user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Binary Proxy Execution Compiled HTML File Decompile - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity", "Living Off The Land"], "cis20": ["CIS 10"], "confidence": 90, "impact": 100, "mitre_attack": ["T1218.001", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the decompile parameter with the HTML Help application, HH.exe. This is a uncommon command to see ran and behavior. Most recently this was seen in a APT41 campaign where a CHM file was delivered and a script inside used a technique for running an arbitrary command in a CHM file via an ActiveX object. This unpacks an HTML help file to a specified path for launching the next stage.
action.notable.param.rule_title = Windows System Binary Proxy Execution Compiled HTML File Decompile
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*-decompile* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_binary_proxy_execution_compiled_html_file_decompile_filter`

[ESCU - Windows System Discovery Using ldap Nslookup - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the execution of nslookup.exe tool to get domain information. Nslookup.exe is a command-line tool that can display information to diagnose domain name systems. This Nslookup feature is being abused by Qakbot malware to gather domain information such as SRV service location records, server name and many more.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the execution of nslookup.exe tool to get domain information. Nslookup.exe is a command-line tool that can display information to diagnose domain name systems. This Nslookup feature is being abused by Qakbot malware to gather domain information such as SRV service location records, server name and many more.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = dministrator may execute this commandline tool for auditing purposes. Filter as needed.
action.escu.creation_date = 2022-10-21
action.escu.modification_date = 2022-10-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Discovery Using ldap Nslookup - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = System nslookup domain discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 1}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Discovery Using ldap Nslookup - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 10, "impact": 10, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "nslookup.exe" OR Processes.original_file_name = "nslookup.exe") AND Processes.process = "*_ldap._tcp.dc._msdcs*" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_system_discovery_using_ldap_nslookup_filter`

[ESCU - Windows System Discovery Using Qwinsta - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the execution of qwinsta.exe executable in Windows Operating System. This Windows executable file can display information about sessions on a remote desktop session host server. The information includes servername, sessionname, username and many more. This tool is being abused of Qakbot malware to gather information to the targeted or compromised host that will be send back to its Command And Control server.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the execution of qwinsta.exe executable in Windows Operating System. This Windows executable file can display information about sessions on a remote desktop session host server. The information includes servername, sessionname, username and many more. This tool is being abused of Qakbot malware to gather information to the targeted or compromised host that will be send back to its Command And Control server.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator may execute this commandline tool for auditing purposes. Filter as needed.
action.escu.creation_date = 2022-10-21
action.escu.modification_date = 2022-10-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Discovery Using Qwinsta - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Qakbot"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Discovery Using Qwinsta - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "qwinsta.exe" OR Processes.original_file_name = "qwinsta.exe" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_system_discovery_using_qwinsta_filter`

[ESCU - Windows System File on Disk - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic will assist with identifying new .sys files introduced in the environment. This query is meant to identify sys file creates on disk. There will be noise, but reducing common process names or applications should help to limit any volume. The idea is to identify new sys files written to disk and identify them before they're added as a new kernel mode driver.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1068"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic will assist with identifying new .sys files introduced in the environment. This query is meant to identify sys file creates on disk. There will be noise, but reducing common process names or applications should help to limit any volume. The idea is to identify new sys files written to disk and identify them before they're added as a new kernel mode driver.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on files from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. In addition, filtering may occur by adding NOT (Filesystem.file_path IN ("*\\Windows\\*", "*\\Program File*", "*\\systemroot\\*","%SystemRoot%*", "system32\*")). This will level out the noise generated to potentally lead to generating notables.
action.escu.known_false_positives = False positives will be present. Filter as needed.
action.escu.creation_date = 2022-05-16
action.escu.modification_date = 2022-05-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System File on Disk - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Drivers", "CISA AA22-264A"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System File on Disk - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers", "CISA AA22-264A"], "cis20": ["CIS 10"], "confidence": 50, "impact": 20, "mitre_attack": ["T1068"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.sys*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.file_hash | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_system_file_on_disk_filter`

[ESCU - Windows System LogOff Commandline - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies Windows commandline to logoff a windows host machine. This technique was seen in several APT, RAT like dcrat and other commodity malware to shutdown the machine to add more impact, interrupt access, aid destruction of the system like wiping disk or inhibit system recovery. This TTP is a good pivot to check why application trigger this commandline which is not so common way to logoff a machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1529"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies Windows commandline to logoff a windows host machine. This technique was seen in several APT, RAT like dcrat and other commodity malware to shutdown the machine to add more impact, interrupt access, aid destruction of the system like wiping disk or inhibit system recovery. This TTP is a good pivot to check why application trigger this commandline which is not so common way to logoff a machine.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator may execute this commandline to trigger shutdown, logoff or restart the host machine.
action.escu.creation_date = 2022-07-27
action.escu.modification_date = 2022-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System LogOff Commandline - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Process name $process_name$ is seen to execute logoff commandline on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System LogOff Commandline - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 70, "mitre_attack": ["T1529"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" Processes.process IN ("* /l*", "* -l*") Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_logoff_commandline_filter`

[ESCU - Windows System Network Config Discovery Display DNS - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process command line that retrieves dns reply information using Windows OS built-in tool IPConfig. This technique is being abused by threat actors, adversaries and post exploitation tools like WINPEAS to retrieve DNS information for the targeted host. This IPConfig parameter (/displaydns) can show dns server resource record, record name, record type, time to live data length and dns reply. This hunting detection can be a good pivot to check which process is executing this command line in specific host system that may lead to malware or adversaries gathering network information.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1016"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process command line that retrieves dns reply information using Windows OS built-in tool IPConfig. This technique is being abused by threat actors, adversaries and post exploitation tools like WINPEAS to retrieve DNS information for the targeted host. This IPConfig parameter (/displaydns) can show dns server resource record, record name, record type, time to live data length and dns reply. This hunting detection can be a good pivot to check which process is executing this command line in specific host system that may lead to malware or adversaries gathering network information.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Network Config Discovery Display DNS - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ with commandline $process$ is executed in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Network Config Discovery Display DNS - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1016"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="ipconfig.exe" OR Processes.original_file_name = "ipconfig.exe" AND Processes.process = "*/displaydns*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_network_config_discovery_display_dns_filter`

[ESCU - Windows System Network Connections Discovery Netsh - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of Windows OS built-in tool netsh.exe to show state, configuration and profile of host firewall. This tool is being used or abused by several adversaries or even post exploitation tool to bypass firewall rules or to discover firewall settings. This hunting detection can help to detect a possible suspicious usage of netsh.exe to retrieve firewall settings or even firewall wlan profile. We recommend checking which parent process and process name execute this command. Also check the process file path for verification that may lead to further TTP's threat behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of Windows OS built-in tool netsh.exe to show state, configuration and profile of host firewall. This tool is being used or abused by several adversaries or even post exploitation tool to bypass firewall rules or to discover firewall settings. This hunting detection can help to detect a possible suspicious usage of netsh.exe to retrieve firewall settings or even firewall wlan profile. We recommend checking which parent process and process name execute this command. Also check the process file path for verification that may lead to further TTP's threat behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network administrator can use this tool for auditing process.
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Network Connections Discovery Netsh - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = netsh process with command line $process$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Network Connections Discovery Netsh - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1049"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh`AND Processes.process = "* show *" Processes.process IN ("*state*", "*config*", "*wlan*", "*profile*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_network_connections_discovery_netsh_filter`

[ESCU - Windows System Reboot CommandLine - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies Windows commandline to reboot a windows host machine. This technique was seen in several APT, RAT like dcrat and other commodity malware to shutdown the machine to add more impact, interrupt access, aid destruction of the system like wiping disk or inhibit system recovery. This TTP is a good pivot to check why application trigger this commandline which is not so common way to reboot a machine. Compare to shutdown and logoff shutdown.exe feature, reboot seen in some automation script like ansible to reboot the machine.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1529"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies Windows commandline to reboot a windows host machine. This technique was seen in several APT, RAT like dcrat and other commodity malware to shutdown the machine to add more impact, interrupt access, aid destruction of the system like wiping disk or inhibit system recovery. This TTP is a good pivot to check why application trigger this commandline which is not so common way to reboot a machine. Compare to shutdown and logoff shutdown.exe feature, reboot seen in some automation script like ansible to reboot the machine.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator may execute this commandline to trigger shutdown or restart the host machine.
action.escu.creation_date = 2022-07-27
action.escu.modification_date = 2022-07-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Reboot CommandLine - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Process $process_name$ that executed reboot via commandline on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Reboot CommandLine - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1529"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" Processes.process IN ("* /r*", "* -r*") Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_reboot_commandline_filter`

[ESCU - Windows System Script Proxy Execution Syncappvpublishingserver - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the abuse of Syncappvpublishingserver.vbs, which is a native script on Windows that may be utilized to download remote files or perform privilege escalation.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1216", "T1218"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the abuse of Syncappvpublishingserver.vbs, which is a native script on Windows that may be utilized to download remote files or perform privilege escalation.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives may be present if the vbscript syncappvpublishingserver is used for legitimate purposes. Filter as needed. Adding a n; to the command-line arguments may help reduce any noise.
action.escu.creation_date = 2022-09-26
action.escu.modification_date = 2022-09-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Script Proxy Execution Syncappvpublishingserver - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Living Off The Land"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download files or evade critical controls.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 30}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Script Proxy Execution Syncappvpublishingserver - Rule
action.correlationsearch.annotations = {"analytic_story": ["Living Off The Land"], "cis20": ["CIS 10"], "confidence": 50, "impact": 60, "mitre_attack": ["T1216", "T1218"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the abuse of Syncappvpublishingserver.vbs, which is a native script on Windows that may be utilized to download remote files or perform privilege escalation.
action.notable.param.rule_title = Windows System Script Proxy Execution Syncappvpublishingserver
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("wscript.exe","cscript.exe") Processes.process="*syncappvpublishingserver.vbs*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_script_proxy_execution_syncappvpublishingserver_filter`

[ESCU - Windows System Shutdown CommandLine - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection rule is designed to identify the execution of the Windows shutdown command via command line interface. The shutdown command can be utilized by system administrators to properly halt, power off, or reboot a computer. However, in a security context, attackers who have gained unauthorized access to a system may also use this command in an effort to erase tracks, or to cause disruption and denial of service. In some instances, they might execute the shutdown command after installing a backdoor, to force the system to restart, ensuring that changes take effect or evading detection by security tools. Monitoring for the use of the Windows shutdown command, especially in conjunction with other unusual or unauthorized activities, can be an important part of identifying malicious behavior within a network. It is advised that security professionals analyze the context in which the shutdown command is being executed to differentiate between legitimate administrative functions and potentially malicious activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1529"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This detection rule is designed to identify the execution of the Windows shutdown command via command line interface. The shutdown command can be utilized by system administrators to properly halt, power off, or reboot a computer. However, in a security context, attackers who have gained unauthorized access to a system may also use this command in an effort to erase tracks, or to cause disruption and denial of service. In some instances, they might execute the shutdown command after installing a backdoor, to force the system to restart, ensuring that changes take effect or evading detection by security tools. Monitoring for the use of the Windows shutdown command, especially in conjunction with other unusual or unauthorized activities, can be an important part of identifying malicious behavior within a network. It is advised that security professionals analyze the context in which the shutdown command is being executed to differentiate between legitimate administrative functions and potentially malicious activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrator may execute this commandline to trigger shutdown or restart the host machine.
action.escu.creation_date = 2023-06-20
action.escu.modification_date = 2023-06-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Shutdown CommandLine - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT", "Sandworm Tools", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = Process $process_name$ seen to execute shutdown via commandline on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Shutdown CommandLine - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT", "Sandworm Tools", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1529"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" AND Processes.process IN("* /s*", "* -s*") AND Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_shutdown_commandline_filter`

[ESCU - Windows System Time Discovery W32tm Delay - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies DCRat delay time tactics using w32tm. This technique was seen in DCRAT malware where it uses stripchart function of w32tm.exe application to delay the execution of its payload like c2 communication , beaconing and execution. This anomaly detection may help the analyst to check other possible event like the process who execute this command that may lead to DCRat attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1124"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies DCRat delay time tactics using w32tm. This technique was seen in DCRAT malware where it uses stripchart function of w32tm.exe application to delay the execution of its payload like c2 communication , beaconing and execution. This anomaly detection may help the analyst to check other possible event like the process who execute this command that may lead to DCRat attack.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-07-28
action.escu.modification_date = 2022-07-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System Time Discovery W32tm Delay - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["DarkCrystal RAT"]
action.risk = 1
action.risk.param._risk_message = Process name w32tm.exe is using suspcicious command line arguments $process$ on host $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System Time Discovery W32tm Delay - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkCrystal RAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1124"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = w32tm.exe Processes.process= "* /stripchart *" Processes.process= "* /computer:localhost *" Processes.process= "* /period:*" Processes.process= "* /dataonly *" Processes.process= "* /samples:*" by  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_time_discovery_w32tm_delay_filter`

[ESCU - Windows System User Discovery Via Quser - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a process execution of Windows OS quser.exe tool. This tool is being abused or used by several post exploitation tool such as winpeas that being used by ransomware prestige to display or gather information about user sessions on a Remote Desktop Session Host server. This command can find out if a specific user is logged on to a specific Remote Desktop Session Host server. This tool can retrieve some RDP information that can be use by attacker for further attack like Name of the user , Name of the session on the Remote Desktop Session Host server, Session ID, State of the session (active or disconnected), Idle time (the number of minutes since the last keystroke or mouse movement at the session) and Date and time the user logged on.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies a process execution of Windows OS quser.exe tool. This tool is being abused or used by several post exploitation tool such as winpeas that being used by ransomware prestige to display or gather information about user sessions on a Remote Desktop Session Host server. This command can find out if a specific user is logged on to a specific Remote Desktop Session Host server. This tool can retrieve some RDP information that can be use by attacker for further attack like Name of the user , Name of the session on the Remote Desktop Session Host server, Session ID, State of the session (active or disconnected), Idle time (the number of minutes since the last keystroke or mouse movement at the session) and Date and time the user logged on.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = network administrator can use this command tool to audit RDP access of user in specific network or host.
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows System User Discovery Via Quser - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows System User Discovery Via Quser - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 30, "impact": 30, "mitre_attack": ["T1033"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="quser.exe" OR Processes.original_file_name = "quser.exe" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_user_discovery_via_quser_filter`

[ESCU - Windows Terminating Lsass Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious process terminating Lsass process. Lsass process is known to be a critical process that is responsible for enforcing security policy system. This process was commonly targetted by threat actor or red teamer to gain privilege escalation or persistence in the targeted machine because it handles credentials of the logon users. In this analytic we tried to detect a suspicious process having a granted access PROCESS_TERMINATE to lsass process to modify or delete protected registrys. This technique was seen in doublezero malware that tries to wipe files and registry in compromised hosts. This anomaly detection can be a good pivot of incident response for possible credential dumping or evading security policy in a host or network environment.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to detect a suspicious process terminating Lsass process. Lsass process is known to be a critical process that is responsible for enforcing security policy system. This process was commonly targetted by threat actor or red teamer to gain privilege escalation or persistence in the targeted machine because it handles credentials of the logon users. In this analytic we tried to detect a suspicious process having a granted access PROCESS_TERMINATE to lsass process to modify or delete protected registrys. This technique was seen in doublezero malware that tries to wipe files and registry in compromised hosts. This anomaly detection can be a good pivot of incident response for possible credential dumping or evading security policy in a host or network environment.
action.escu.how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Terminating Lsass Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Destruction", "Double Zero Destructor"]
action.risk = 1
action.risk.param._risk_message = a process $SourceImage$ terminates Lsass process in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "TargetImage", "risk_object_type": "other", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Terminating Lsass Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Destruction", "Double Zero Destructor"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 TargetImage=*lsass.exe GrantedAccess = 0x1 | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage, TargetImage, TargetProcessId, SourceProcessId, GrantedAccess CallTrace, dest | rename dest as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_terminating_lsass_process_filter`

[ESCU - Windows Time Based Evasion - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to detect potentially malicious processes that initiate a ping delay using an invalid IP address. This evasion technique was observed in NJRAT, where the malware employed ping commands as a means to introduce a time delay before self-deletion on the compromised host. Identifying this (TTP) behavior can serve as a valuable indicator for detecting NJRAT infections or other malware that employ time delays as evasion tactics.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1497", "T1497.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is designed to detect potentially malicious processes that initiate a ping delay using an invalid IP address. This evasion technique was observed in NJRAT, where the malware employed ping commands as a means to introduce a time delay before self-deletion on the compromised host. Identifying this (TTP) behavior can serve as a valuable indicator for detecting NJRAT infections or other malware that employ time delays as evasion tactics.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2023-09-08
action.escu.modification_date = 2023-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Time Based Evasion - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["NjRAT"]
action.risk = 1
action.risk.param._risk_message = A $process_name$ did a suspicious ping to invalid IP address on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Time Based Evasion - Rule
action.correlationsearch.annotations = {"analytic_story": ["NjRAT"], "cis20": ["CIS 10"], "confidence": 60, "impact": 60, "mitre_attack": ["T1497", "T1497.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is designed to detect potentially malicious processes that initiate a ping delay using an invalid IP address. This evasion technique was observed in NJRAT, where the malware employed ping commands as a means to introduce a time delay before self-deletion on the compromised host. Identifying this (TTP) behavior can serve as a valuable indicator for detecting NJRAT infections or other malware that employ time delays as evasion tactics.
action.notable.param.rule_title = Windows Time Based Evasion
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "ping.exe" Processes.parent_process = "* ping 0 -n *" OR Processes.process = "* ping 0 -n *" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_time_based_evasion_filter`

[ESCU - Windows Unsigned DLL Side-Loading - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic focuses on detecting potentially malicious unsigned DLLs created in either the c:\windows\system32 or c:\windows\syswow64 folders. This particular technique was observed in the context of the Warzone (Ave Maria) RAT, where it employed a method known as DLL hijacking (dll-side-loading) by dropping the "dismcore.dll" to achieve privilege escalation. DLL hijacking is a stealthy attack technique used by cybercriminals to exploit the way Windows searches and loads DLLs. By placing a malicious DLL with the same name as one that a legitimate application is expected to load, the attacker can gain unauthorized access and execute malicious code. In the case of Warzone RAT (Ave Maria), the dropped "dismcore.dll" was intended to deceive the system into loading the rogue DLL instead of the legitimate version, thereby granting the malware elevated privileges and enabling further compromise of the target system. Detecting such suspicious DLLs is crucial in preventing privilege escalation attacks and other potential security breaches. Regular security assessments, thorough monitoring, and implementing security best practices are essential in safeguarding systems from such threats.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1574.002"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This analytic focuses on detecting potentially malicious unsigned DLLs created in either the c:\windows\system32 or c:\windows\syswow64 folders. This particular technique was observed in the context of the Warzone (Ave Maria) RAT, where it employed a method known as DLL hijacking (dll-side-loading) by dropping the "dismcore.dll" to achieve privilege escalation. DLL hijacking is a stealthy attack technique used by cybercriminals to exploit the way Windows searches and loads DLLs. By placing a malicious DLL with the same name as one that a legitimate application is expected to load, the attacker can gain unauthorized access and execute malicious code. In the case of Warzone RAT (Ave Maria), the dropped "dismcore.dll" was intended to deceive the system into loading the rogue DLL instead of the legitimate version, thereby granting the malware elevated privileges and enabling further compromise of the target system. Detecting such suspicious DLLs is crucial in preventing privilege escalation attacks and other potential security breaches. Regular security assessments, thorough monitoring, and implementing security best practices are essential in safeguarding systems from such threats.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = It is possible some Administrative utilities will load dismcore.dll outside of normal system paths, filter as needed.
action.escu.creation_date = 2023-07-26
action.escu.modification_date = 2023-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unsigned DLL Side-Loading - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Warzone RAT", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = An unsigned dll module was loaded on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unsigned DLL Side-Loading - Rule
action.correlationsearch.annotations = {"analytic_story": ["Warzone RAT", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1574.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=7 Signed=false OriginalFileName = "-" SignatureStatus="unavailable" ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*") | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed SignatureStatus OriginalFileName process_name dest EventCode ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_filter`

[ESCU - Windows Unusual Count Of Disabled Users Failed Auth Using Kerberos - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with multiple disabled domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code `0x12` stands for `clients credentials have been revoked` (account disabled, expired or locked out).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with multiple disabled domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code `0x12` stands for `clients credentials have been revoked` (account disabled, expired or locked out).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Disabled Users Failed Auth Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Disabled Users Failed Auth Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_disabled_users_failed_auth_using_kerberos_filter` 

[ESCU - Windows Unusual Count Of Invalid Users Fail To Auth Using Kerberos - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with multiple invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for `client not found in Kerberos database` (the attempted user is not a valid domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with multiple invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for `client not found in Kerberos database` (the attempted user is not a valid domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Invalid Users Fail To Auth Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Invalid Users Fail To Auth Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos_filter` 

[ESCU - Windows Unusual Count Of Invalid Users Failed To Auth Using NTLM - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with multiple invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for `The username you typed does not exist` (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with multiple invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for `The username you typed does not exist` (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Invalid Users Failed To Auth Using NTLM - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential NTLM based password spraying attack from $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Invalid Users Failed To Auth Using NTLM - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | rename Workstation as src |`windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm_filter`

[ESCU - Windows Unusual Count Of Users Fail To Auth Wth ExplicitCredentials - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a source user failing to authenticate with multiple users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a source user failing to authenticate with multiple users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Users Fail To Auth Wth ExplicitCredentials - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack from $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Users Fail To Auth Wth ExplicitCredentials - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) as  tried_account by _time, Computer, Caller_User_Name | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials_filter` 

[ESCU - Windows Unusual Count Of Users Failed To Auth Using Kerberos - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for `wrong password provided` (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for `wrong password provided` (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Users Failed To Auth Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $IpAddress$
action.risk.param._risk = [{"risk_object_field": "IpAddress", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Users Failed To Auth Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Active Directory Kerberos Attacks", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_auth_using_kerberos_filter`

[ESCU - Windows Unusual Count Of Users Failed To Authenticate From Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a source process name failing to authenticate with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a source process name failing to authenticate with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Users Failed To Authenticate From Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack from $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Users Failed To Authenticate From Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Insider Threat", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security`  EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, ProcessName, SubjectUserName, Computer | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ProcessName, SubjectUserName, Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_from_process_filter` 

[ESCU - Windows Unusual Count Of Users Failed To Authenticate Using NTLM - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Users Failed To Authenticate Using NTLM - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential NTLM based password spraying attack from $Workstation$
action.risk.param._risk = [{"risk_object_field": "Workstation", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Users Failed To Authenticate Using NTLM - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security`  EventCode=4776 TargetUserName!=*$ Status=0xC000006A | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_using_ntlm_filter`

[ESCU - Windows Unusual Count Of Users Remotely Failed To Auth From Host - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a source host failing to authenticate against a remote host with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a source host failing to authenticate against a remote host with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
action.escu.known_false_positives = A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc.
action.escu.creation_date = 2022-09-22
action.escu.modification_date = 2022-09-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Unusual Count Of Users Remotely Failed To Auth From Host - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Active Directory Password Spraying", "Volt Typhoon"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Unusual Count Of Users Remotely Failed To Auth From Host - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying", "Volt Typhoon"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1110.003", "T1110"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search =  `wineventlog_security`  EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress, Computer | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress, Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_remotely_failed_to_auth_from_host_filter` 

[ESCU - Windows User Execution Malicious URL Shortcut File - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will identify suspicious creation of URL shortcut link files. This technique was seen in CHAOS ransomware where it will drop this .url link file in %startup% folder that contains the path of its malicious dropped file to execute upon the reboot of the targeted host. The creation of this file can be created by a normal application or software but it is a good practice to verify this type of file specially the resource it tries to execute which is commonly a website.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1204.002", "T1204"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify suspicious creation of URL shortcut link files. This technique was seen in CHAOS ransomware where it will drop this .url link file in %startup% folder that contains the path of its malicious dropped file to execute upon the reboot of the targeted host. The creation of this file can be created by a normal application or software but it is a good practice to verify this type of file specially the resource it tries to execute which is commonly a website.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
action.escu.known_false_positives = Administrators may allow creation of script or exe in this path.
action.escu.creation_date = 2023-01-12
action.escu.modification_date = 2023-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows User Execution Malicious URL Shortcut File - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Chaos Ransomware", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = a process created URL shortcut file in $file_path$ of $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows User Execution Malicious URL Shortcut File - Rule
action.correlationsearch.annotations = {"analytic_story": ["Chaos Ransomware", "NjRAT"], "cis20": ["CIS 10"], "confidence": 80, "impact": 80, "mitre_attack": ["T1204.002", "T1204"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic will identify suspicious creation of URL shortcut link files. This technique was seen in CHAOS ransomware where it will drop this .url link file in %startup% folder that contains the path of its malicious dropped file to execute upon the reboot of the targeted host. The creation of this file can be created by a normal application or software but it is a good practice to verify this type of file specially the resource it tries to execute which is commonly a website.
action.notable.param.rule_title = Windows User Execution Malicious URL Shortcut File
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where NOT(Filesystem.file_path IN ("*\\Program Files*")) Filesystem.file_name = *.url by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user Filesystem.file_path Filesystem.process_guid Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_user_execution_malicious_url_shortcut_file_filter`

[ESCU - Windows Valid Account With Never Expires Password - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies net.exe updating user account policies for password requirement with non-expiring password. This technique was seen in several adversaries and malware like Azorult to maintain the foothold (persistence), gaining privilege escalation, defense evasion and possible for lateral movement for specific users or created user account on the targeted host. This TTP detections is a good pivot to see further what other events that users executes on the machines.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies net.exe updating user account policies for password requirement with non-expiring password. This technique was seen in several adversaries and malware like Azorult to maintain the foothold (persistence), gaining privilege escalation, defense evasion and possible for lateral movement for specific users or created user account on the targeted host. This TTP detections is a good pivot to see further what other events that users executes on the machines.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = This behavior is not commonly seen in production environment and not advisable, filter as needed.
action.escu.creation_date = 2022-06-23
action.escu.modification_date = 2022-06-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Valid Account With Never Expires Password - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Azorult"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to make non-expiring password on host user accounts.
action.risk.param._risk = [{"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Valid Account With Never Expires Password - Rule
action.correlationsearch.annotations = {"analytic_story": ["Azorult"], "cis20": ["CIS 10"], "confidence": 100, "impact": 100, "mitre_attack": ["T1489"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies net.exe updating user account policies for password requirement with non-expiring password. This technique was seen in several adversaries and malware like Azorult to maintain the foothold (persistence), gaining privilege escalation, defense evasion and possible for lateral movement for specific users or created user account on the targeted host. This TTP detections is a good pivot to see further what other events that users executes on the machines.
action.notable.param.rule_title = Windows Valid Account With Never Expires Password
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="* accounts *" AND Processes.process="* /maxpwage:unlimited" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_valid_account_with_never_expires_password_filter`

[ESCU - Windows Vulnerable 3CX Software - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic leverages Sysmon, a powerful system monitoring and logging tool, to pinpoint instances of the 3CXDesktopApp.exe with a FileVersion of 18.12.x.Recently, 3CX has discovered a vulnerability specifically in versions 18.12.407 and 18.12.416 of the desktop app.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1195.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Sysmon, a powerful system monitoring and logging tool, to pinpoint instances of the 3CXDesktopApp.exe with a FileVersion of 18.12.x.Recently, 3CX has discovered a vulnerability specifically in versions 18.12.407 and 18.12.416 of the desktop app.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = False positives may be present based on file version, modify the analytic to only look for version between 18.12.407 and 18.12.416 as needed.
action.escu.creation_date = 2023-03-30
action.escu.modification_date = 2023-03-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Vulnerable 3CX Software - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["3CX Supply Chain Attack"]
action.risk = 1
action.risk.param._risk_message = A known vulnerable instance of 3CX Software $process_name$ ran on $dest$, related to a supply chain attack.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Vulnerable 3CX Software - Rule
action.correlationsearch.annotations = {"analytic_story": ["3CX Supply Chain Attack"], "cis20": ["CIS 10"], "confidence": 90, "cve": ["CVE-2023-29059"], "impact": 100, "mitre_attack": ["T1195.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic leverages Sysmon, a powerful system monitoring and logging tool, to pinpoint instances of the 3CXDesktopApp.exe with a FileVersion of 18.12.x.Recently, 3CX has discovered a vulnerability specifically in versions 18.12.407 and 18.12.416 of the desktop app.
action.notable.param.rule_title = Windows Vulnerable 3CX Software
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` (process_name=3CXDesktopApp.exe OR OriginalFileName=3CXDesktopApp.exe)  FileVersion=18.12.* | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name,process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_vulnerable_3cx_software_filter`

[ESCU - Windows Vulnerable Driver Loaded - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes a known list of vulnerable Windows drivers to help defenders find potential persistence or privelege escalation via a vulnerable driver. This analytic uses Sysmon EventCode 6, driver loading. A known gap with this lookup is that it does not use the hash or known signer of the vulnerable driver therefore it is up to the defender to identify version and signing info and confirm it is a vulnerable driver.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes a known list of vulnerable Windows drivers to help defenders find potential persistence or privelege escalation via a vulnerable driver. This analytic uses Sysmon EventCode 6, driver loading. A known gap with this lookup is that it does not use the hash or known signer of the vulnerable driver therefore it is up to the defender to identify version and signing info and confirm it is a vulnerable driver.
action.escu.how_to_implement = Sysmon collects driver loads via EventID 6, however you may modify the query to utilize this lookup to identify potentially persistent drivers that are known to be vulnerable.
action.escu.known_false_positives = False positives will be present. Drill down into the driver further by version number and cross reference by signer. Review the reference material in the lookup. In addition, modify the query to look within specific paths, which will remove a lot of "normal" drivers.
action.escu.creation_date = 2022-12-12
action.escu.modification_date = 2022-12-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Vulnerable Driver Loaded - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows Drivers", "BlackByte Ransomware"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Vulnerable Driver Loaded - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Drivers", "BlackByte Ransomware"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1543.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=6 | lookup loldrivers driver_name AS ImageLoaded OUTPUT is_driver driver_description | search is_driver = TRUE | stats  min(_time) as firstTime max(_time) as lastTime count by dest ImageLoaded driver_description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_vulnerable_driver_loaded_filter`

[ESCU - Windows WinLogon with Public Network Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect anomalous behavior associated with the BlackLotus Campaign, a sophisticated bootkit attack reported by ESET and further investigated in a blog by Microsoft, which provided hunting queries for security analysts. The primary focus of this analytic is to identify instances of Winlogon.exe, a critical Windows process, connecting to public IP space, which is indicative of potential malicious activity.\ The BlackLotus Campaign is a bootkit-based attack that compromises system integrity by infecting the Master Boot Record (MBR) and Volume Boot Record (VBR). This malware variant can bypass traditional security measures, load before the operating system, and maintain persistence on the target system.\
Winlogon.exe is a critical Windows process responsible for managing user logon and logoff processes. Under normal circumstances, Winlogon.exe should not be connecting to public IP addresses. However, if it does, it may indicate that the process has been compromised as part of the BlackLotus Campaign or another malicious operation.\
This analytic monitors network connections made by Winlogon.exe and triggers an alert if it detects connections to public IP space. By identifying such anomalous behavior, security analysts can investigate further and respond swiftly to potential threats.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1542.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint", "Network_Traffic"]
action.escu.eli5 = The following analytic is designed to detect anomalous behavior associated with the BlackLotus Campaign, a sophisticated bootkit attack reported by ESET and further investigated in a blog by Microsoft, which provided hunting queries for security analysts. The primary focus of this analytic is to identify instances of Winlogon.exe, a critical Windows process, connecting to public IP space, which is indicative of potential malicious activity.\ The BlackLotus Campaign is a bootkit-based attack that compromises system integrity by infecting the Master Boot Record (MBR) and Volume Boot Record (VBR). This malware variant can bypass traditional security measures, load before the operating system, and maintain persistence on the target system.\
Winlogon.exe is a critical Windows process responsible for managing user logon and logoff processes. Under normal circumstances, Winlogon.exe should not be connecting to public IP addresses. However, if it does, it may indicate that the process has been compromised as part of the BlackLotus Campaign or another malicious operation.\
This analytic monitors network connections made by Winlogon.exe and triggers an alert if it detects connections to public IP space. By identifying such anomalous behavior, security analysts can investigate further and respond swiftly to potential threats.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives will be present and filtering will be required. Legitimate IPs will be present and need to be filtered.
action.escu.creation_date = 2023-05-03
action.escu.modification_date = 2023-05-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows WinLogon with Public Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["BlackLotus Campaign"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows WinLogon with Public Network Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["BlackLotus Campaign"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1542.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN (winlogon.exe)  Processes.process!=unknown by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 NOT (All_Traffic.dest IN (127.0.0.1,10.0.0.0/12,172.16.0.0/12, 192.168.0.0/16, 0:0:0:0:0:0:0:1)) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as publicIp ] | table dest parent_process_name process_name process_path process process_id dest_port publicIp | `windows_winlogon_with_public_network_connection_filter`

[ESCU - Windows WMI Impersonate Token - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a possible wmi token impersonation activities in a process or command. This technique was seen in Qakbot malware where it will execute a vbscript code contains wmi impersonation object to gain privilege escalation or as defense evasion. This Anomaly detection looks for wmiprvse.exe SourceImage having a duplicate handle or full granted access in a target process.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies a possible wmi token impersonation activities in a process or command. This technique was seen in Qakbot malware where it will execute a vbscript code contains wmi impersonation object to gain privilege escalation or as defense evasion. This Anomaly detection looks for wmiprvse.exe SourceImage having a duplicate handle or full granted access in a target process.
action.escu.how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
action.escu.known_false_positives = administrator may execute impersonate wmi object script for auditing. Filter is needed.
action.escu.creation_date = 2022-10-24
action.escu.modification_date = 2022-10-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows WMI Impersonate Token - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot"]
action.risk = 1
action.risk.param._risk_message = wmiprvse.exe process having a duplicate or full Granted Access $GrantedAccess$ to $TargetImage$ process in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows WMI Impersonate Token - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=10 SourceImage = "*\\wmiprvse.exe"  GrantedAccess IN ("0x1478", "0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId GrantedAccess CallTrace dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_impersonate_token_filter`

[ESCU - Windows WMI Process And Service List - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious process command line, where WMI is performing an event query looking for running processes or running services. This technique is commonly found where the adversary will identify services and system information on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies suspicious process command line, where WMI is performing an event query looking for running processes or running services. This technique is commonly found where the adversary will identify services and system information on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = netowrk administrator or IT may execute this command for auditing processes and services.
action.escu.creation_date = 2022-11-30
action.escu.modification_date = 2022-11-30
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows WMI Process And Service List - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Post-Exploitation", "Prestige Ransomware"]
action.risk = 1
action.risk.param._risk_message = wmi command $process$ to list processes and services in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 4}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows WMI Process And Service List - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Post-Exploitation", "Prestige Ransomware"], "cis20": ["CIS 10"], "confidence": 20, "impact": 20, "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process IN ("*process list*", "*service list*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_process_and_service_list_filter`

[ESCU - Windows WMI Process Call Create - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to look for wmi commandlines to execute or create process. This technique was used by adversaries or threat actor to execute their malicious payload in local or remote host. This hunting query is a good pivot to start to look further which process trigger the wmi or what process it execute locally or remotely.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to look for wmi commandlines to execute or create process. This technique was used by adversaries or threat actor to execute their malicious payload in local or remote host. This hunting query is a good pivot to start to look further which process trigger the wmi or what process it execute locally or remotely.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may execute this command for testing or auditing.
action.escu.creation_date = 2022-02-22
action.escu.modification_date = 2022-02-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows WMI Process Call Create - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious WMI Use", "Qakbot", "Volt Typhoon", "IcedID"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows WMI Process Call Create - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use", "Qakbot", "Volt Typhoon", "IcedID"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1047"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process = "* process *" Processes.process = "* call *" Processes.process = "* create *" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_path Processes.process_guid Processes.parent_process_id Processes.dest Processes.user Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_process_call_create_filter`

[ESCU - WinEvent Scheduled Task Created to Spawn Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query utilizes Windows Security EventCode 4698, indicating 'a scheduled task was created', to identify potentially suspicious tasks. These tasks may be registered on Windows through either schtasks.exe or TaskService, and are set up to execute a command with a native Windows shell such as PowerShell, Cmd, Wscript, or Cscript.\
The search will return the initial and final times the task was registered, along with details like the 'Command' set to be executed, 'Task Name', 'Author', whether it's 'Enabled', and if it is 'Hidden'.\
Schtasks.exe is typically found in C:\Windows\system32 and C:\Windows\syswow64. The DLL 'taskschd.dll' is loaded when either schtasks.exe or TaskService is launched. If this DLL is found loaded by another process, it's possible that a scheduled task is being registered within the context of that process in memory.\
During triage, it's essential to identify the source of the scheduled task. Was it registered via schtasks.exe or TaskService? Review the job that was created and the command set to be executed. It's also recommended to capture and review any artifacts on disk, and identify any parallel processes within the same timeframe to locate the source.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, indicating 'a scheduled task was created', to identify potentially suspicious tasks. These tasks may be registered on Windows through either schtasks.exe or TaskService, and are set up to execute a command with a native Windows shell such as PowerShell, Cmd, Wscript, or Cscript.\
The search will return the initial and final times the task was registered, along with details like the 'Command' set to be executed, 'Task Name', 'Author', whether it's 'Enabled', and if it is 'Hidden'.\
Schtasks.exe is typically found in C:\Windows\system32 and C:\Windows\syswow64. The DLL 'taskschd.dll' is loaded when either schtasks.exe or TaskService is launched. If this DLL is found loaded by another process, it's possible that a scheduled task is being registered within the context of that process in memory.\
During triage, it's essential to identify the source of the scheduled task. Was it registered via schtasks.exe or TaskService? Review the job that was created and the command set to be executed. It's also recommended to capture and review any artifacts on disk, and identify any parallel processes within the same timeframe to locate the source.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - WinEvent Scheduled Task Created to Spawn Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Ransomware", "Windows Persistence Techniques", "Winter Vivern", "Ryuk Ransomware", "CISA AA22-257A", "Scheduled Tasks", "Windows Error Reporting Service Elevation of Privilege Vulnerability"]
action.risk = 1
action.risk.param._risk_message = A windows scheduled task was created (task name=$Task_Name$) on $dest$ by the following command: $Command$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "Command", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WinEvent Scheduled Task Created to Spawn Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Persistence Techniques", "Winter Vivern", "Ryuk Ransomware", "CISA AA22-257A", "Scheduled Tasks", "Windows Error Reporting Service Elevation of Privilege Vulnerability"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, indicating 'a scheduled task was created', to identify potentially suspicious tasks. These tasks may be registered on Windows through either schtasks.exe or TaskService, and are set up to execute a command with a native Windows shell such as PowerShell, Cmd, Wscript, or Cscript.\
The search will return the initial and final times the task was registered, along with details like the 'Command' set to be executed, 'Task Name', 'Author', whether it's 'Enabled', and if it is 'Hidden'.\
Schtasks.exe is typically found in C:\Windows\system32 and C:\Windows\syswow64. The DLL 'taskschd.dll' is loaded when either schtasks.exe or TaskService is launched. If this DLL is found loaded by another process, it's possible that a scheduled task is being registered within the context of that process in memory.\
During triage, it's essential to identify the source of the scheduled task. Was it registered via schtasks.exe or TaskService? Review the job that was created and the command set to be executed. It's also recommended to capture and review any artifacts on disk, and identify any parallel processes within the same timeframe to locate the source.
action.notable.param.rule_title = WinEvent Scheduled Task Created to Spawn Shell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_to_spawn_shell_filter`

[ESCU - WinEvent Scheduled Task Created Within Public Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes Windows Security EventCode 4698, which indicates the creation of a scheduled task on a Windows system. The purpose of this query is to identify suspicious tasks that have been registered using either schtasks.exe or TaskService and involve executing a command from a user-writable file path.\
When this analytic is triggered, it provides information such as the first and last registration time of the task, the command to be executed, the task name, author, and whether it is set as hidden or not. It is worth noting that schtasks.exe is commonly located in C:\Windows\system32 and C:\Windows\syswow64, and it loads the taskschd.dll DLL when launched. If this DLL is loaded by another process, it suggests that a scheduled task may be registered within that process's context in memory.\
During the triage process, it is essential to identify the source of the scheduled task creation, whether it was initiated through schtasks.exe or TaskService. The analyst should review the task that was created, including the command to be executed. Additionally, any artifacts on disk related to the task should be captured and analyzed. It is also recommended to identify any parallel processes that occurred within the same timeframe to determine the source of the task creation.\
By conducting this triage process, security analysts can gain insights into potentially malicious or suspicious scheduled tasks, helping them identify the source and assess the impact of the task. This analytic is valuable for a Security Operations Center (SOC) as it can detect unauthorized or suspicious activity that could indicate an attacker's attempt to establish persistence or execute unauthorized commands on the system.\
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes Windows Security EventCode 4698, which indicates the creation of a scheduled task on a Windows system. The purpose of this query is to identify suspicious tasks that have been registered using either schtasks.exe or TaskService and involve executing a command from a user-writable file path.\
When this analytic is triggered, it provides information such as the first and last registration time of the task, the command to be executed, the task name, author, and whether it is set as hidden or not. It is worth noting that schtasks.exe is commonly located in C:\Windows\system32 and C:\Windows\syswow64, and it loads the taskschd.dll DLL when launched. If this DLL is loaded by another process, it suggests that a scheduled task may be registered within that process's context in memory.\
During the triage process, it is essential to identify the source of the scheduled task creation, whether it was initiated through schtasks.exe or TaskService. The analyst should review the task that was created, including the command to be executed. Additionally, any artifacts on disk related to the task should be captured and analyzed. It is also recommended to identify any parallel processes that occurred within the same timeframe to determine the source of the task creation.\
By conducting this triage process, security analysts can gain insights into potentially malicious or suspicious scheduled tasks, helping them identify the source and assess the impact of the task. This analytic is valuable for a Security Operations Center (SOC) as it can detect unauthorized or suspicious activity that could indicate an attacker's attempt to establish persistence or execute unauthorized commands on the system.\
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
action.escu.known_false_positives = False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - WinEvent Scheduled Task Created Within Public Path - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Ransomware", "Windows Persistence Techniques", "Winter Vivern", "Prestige Ransomware", "AsyncRAT", "Ryuk Ransomware", "IcedID", "CISA AA22-257A", "Industroyer2", "Active Directory Lateral Movement", "Data Destruction", "Scheduled Tasks"]
action.risk = 1
action.risk.param._risk_message = A windows scheduled task was created (task name=$Task_Name$) on $dest$ by the following command: $Command$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "Command", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WinEvent Scheduled Task Created Within Public Path - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Windows Persistence Techniques", "Winter Vivern", "Prestige Ransomware", "AsyncRAT", "Ryuk Ransomware", "IcedID", "CISA AA22-257A", "Industroyer2", "Active Directory Lateral Movement", "Data Destruction", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1053.005", "T1053"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes Windows Security EventCode 4698, which indicates the creation of a scheduled task on a Windows system. The purpose of this query is to identify suspicious tasks that have been registered using either schtasks.exe or TaskService and involve executing a command from a user-writable file path.\
When this analytic is triggered, it provides information such as the first and last registration time of the task, the command to be executed, the task name, author, and whether it is set as hidden or not. It is worth noting that schtasks.exe is commonly located in C:\Windows\system32 and C:\Windows\syswow64, and it loads the taskschd.dll DLL when launched. If this DLL is loaded by another process, it suggests that a scheduled task may be registered within that process's context in memory.\
During the triage process, it is essential to identify the source of the scheduled task creation, whether it was initiated through schtasks.exe or TaskService. The analyst should review the task that was created, including the command to be executed. Additionally, any artifacts on disk related to the task should be captured and analyzed. It is also recommended to identify any parallel processes that occurred within the same timeframe to determine the source of the task creation.\
By conducting this triage process, security analysts can gain insights into potentially malicious or suspicious scheduled tasks, helping them identify the source and assess the impact of the task. This analytic is valuable for a Security Operations Center (SOC) as it can detect unauthorized or suspicious activity that could indicate an attacker's attempt to establish persistence or execute unauthorized commands on the system.\
action.notable.param.rule_title = WinEvent Scheduled Task Created Within Public Path
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_within_public_path_filter`

[ESCU - WinEvent Windows Task Scheduler Event Action Started - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic aims to identify suspicious tasks that have been registered and executed in Windows using EventID 200 (action run) and 201 (action completed) from the Windows Task Scheduler logs. This analytic helps detect evasive techniques used to register tasks on Windows systems. It is recommended to filter the results based on the ActionName field by specifying specific paths that are not commonly used in your environment.\
After implementing this analytic, it is important to review parallel events related to the scheduled tasks. EventID 106 will be generated when a new task is created, but it does not necessarily mean that the task has been executed. Analysts should capture any files on disk associated with the task and perform further analysis.\
To implement this analytic, Task Scheduler logs must be collected. This can be done by adding a stanza for [WinEventLog://Microsoft-Windows-TaskScheduler/Operational] in the inputs.conf file and setting renderXml=false. It is worth noting that not translating the logs into XML may require specific extraction of items from the Message field.\
False positives are expected with this analytic, so it is important to filter the results based on the paths or specific keywords of interest in the ActionName field to reduce noise.\
Identifying and analyzing scheduled tasks that have been executed is crucial for a Security Operations Center (SOC) as it helps detect potentially malicious or unauthorized activities on Windows systems. By capturing and investigating the associated events, analysts can uncover signs of persistence mechanisms, unauthorized code execution, or suspicious behaviors. The impact of a true positive could range from unauthorized access to data exfiltration or the execution of malicious payloads.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1053.005"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic aims to identify suspicious tasks that have been registered and executed in Windows using EventID 200 (action run) and 201 (action completed) from the Windows Task Scheduler logs. This analytic helps detect evasive techniques used to register tasks on Windows systems. It is recommended to filter the results based on the ActionName field by specifying specific paths that are not commonly used in your environment.\
After implementing this analytic, it is important to review parallel events related to the scheduled tasks. EventID 106 will be generated when a new task is created, but it does not necessarily mean that the task has been executed. Analysts should capture any files on disk associated with the task and perform further analysis.\
To implement this analytic, Task Scheduler logs must be collected. This can be done by adding a stanza for [WinEventLog://Microsoft-Windows-TaskScheduler/Operational] in the inputs.conf file and setting renderXml=false. It is worth noting that not translating the logs into XML may require specific extraction of items from the Message field.\
False positives are expected with this analytic, so it is important to filter the results based on the paths or specific keywords of interest in the ActionName field to reduce noise.\
Identifying and analyzing scheduled tasks that have been executed is crucial for a Security Operations Center (SOC) as it helps detect potentially malicious or unauthorized activities on Windows systems. By capturing and investigating the associated events, analysts can uncover signs of persistence mechanisms, unauthorized code execution, or suspicious behaviors. The impact of a true positive could range from unauthorized access to data exfiltration or the execution of malicious payloads.
action.escu.how_to_implement = Task Scheduler logs are required to be collected. Enable logging with inputs.conf by adding a stanza for [WinEventLog://Microsoft-Windows-TaskScheduler/Operational] and renderXml=false. Note, not translating it in XML may require a proper extraction of specific items in the Message.
action.escu.known_false_positives = False positives will be present. Filter based on ActionName paths or specify keywords of interest.
action.escu.creation_date = 2023-04-05
action.escu.modification_date = 2023-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - WinEvent Windows Task Scheduler Event Action Started - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Qakbot", "Windows Persistence Techniques", "Winter Vivern", "Prestige Ransomware", "DarkCrystal RAT", "AsyncRAT", "Winter Vivern", "IcedID", "CISA AA22-257A", "Industroyer2", "Sandworm Tools", "Data Destruction", "Amadey", "Scheduled Tasks"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WinEvent Windows Task Scheduler Event Action Started - Rule
action.correlationsearch.annotations = {"analytic_story": ["Qakbot", "Windows Persistence Techniques", "Winter Vivern", "Prestige Ransomware", "DarkCrystal RAT", "AsyncRAT", "Winter Vivern", "IcedID", "CISA AA22-257A", "Industroyer2", "Sandworm Tools", "Data Destruction", "Amadey", "Scheduled Tasks"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1053.005"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_task_scheduler` EventCode IN ("200","201") | rename ComputerName as dest | stats count min(_time) as firstTime max(_time) as lastTime by Message dest EventCode category | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_windows_task_scheduler_event_action_started_filter`

[ESCU - Winhlp32 Spawning a Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies winhlp32.exe, found natively in `c:\windows\`, spawning a child process that loads a file out of appdata, programdata, or temp. Winhlp32.exe has a rocky past in that multiple vulnerabilities were found and added to MetaSploit. WinHlp32.exe is required to display 32-bit Help files that have the ".hlp" file name extension. This particular instance is related to a Remcos sample where dynwrapx.dll is added to the registry under inprocserver32, and later module loaded by winhlp32.exe to spawn wscript.exe and load a vbs or file from disk. During triage, review parallel processes to identify further suspicious behavior. Review module loads for unsuspecting unsigned modules. Capture any file modifications and analyze.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies winhlp32.exe, found natively in `c:\windows\`, spawning a child process that loads a file out of appdata, programdata, or temp. Winhlp32.exe has a rocky past in that multiple vulnerabilities were found and added to MetaSploit. WinHlp32.exe is required to display 32-bit Help files that have the ".hlp" file name extension. This particular instance is related to a Remcos sample where dynwrapx.dll is added to the registry under inprocserver32, and later module loaded by winhlp32.exe to spawn wscript.exe and load a vbs or file from disk. During triage, review parallel processes to identify further suspicious behavior. Review module loads for unsuspecting unsigned modules. Capture any file modifications and analyze.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited as winhlp32.exe is typically not used with the latest flavors of Windows OS. However, filter as needed.
action.escu.creation_date = 2021-10-05
action.escu.modification_date = 2021-10-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Winhlp32 Spawning a Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$, and is not typical activity for this process.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Winhlp32 Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1055"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies winhlp32.exe, found natively in `c:\windows\`, spawning a child process that loads a file out of appdata, programdata, or temp. Winhlp32.exe has a rocky past in that multiple vulnerabilities were found and added to MetaSploit. WinHlp32.exe is required to display 32-bit Help files that have the ".hlp" file name extension. This particular instance is related to a Remcos sample where dynwrapx.dll is added to the registry under inprocserver32, and later module loaded by winhlp32.exe to spawn wscript.exe and load a vbs or file from disk. During triage, review parallel processes to identify further suspicious behavior. Review module loads for unsuspecting unsigned modules. Capture any file modifications and analyze.
action.notable.param.rule_title = Winhlp32 Spawning a Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winhlp32.exe Processes.process IN ("*\\appdata\\*","*\\programdata\\*", "*\\temp\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winhlp32_spawning_a_process_filter`

[ESCU - WinRAR Spawning Shell Application - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the execution of Windows shell processes initiated by WinRAR, specifically looking for instances where WinRAR spawns processes like "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe", or "bitsadmin.exe". This behavior is worth identifying for a Security Operations Center (SOC) because it is indicative of a spoofing attack exploit, such as the one associated with WinRAR CVE-2023-38831. Cybercriminals exploited this vulnerability to craft ZIP archives with spoofed extensions, hiding the launch of malicious scripts within an archive. When a victim opened the specially crafted archive, it executed the malware, leading to unauthorized access to their broker accounts and enabling the cybercriminals to perform illicit financial transactions and withdraw funds. If a true positive is found, it suggests that an attacker has successfully exploited the vulnerability to execute malicious scripts, leading to unauthorized access, financial loss, and potentially the delivery of additional malicious payloads. The impact of the attack could be severe, involving financial loss, unauthorized access to sensitive accounts, and the potential for further malicious activity such as data theft or ransomware attacks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic detects the execution of Windows shell processes initiated by WinRAR, specifically looking for instances where WinRAR spawns processes like "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe", or "bitsadmin.exe". This behavior is worth identifying for a Security Operations Center (SOC) because it is indicative of a spoofing attack exploit, such as the one associated with WinRAR CVE-2023-38831. Cybercriminals exploited this vulnerability to craft ZIP archives with spoofed extensions, hiding the launch of malicious scripts within an archive. When a victim opened the specially crafted archive, it executed the malware, leading to unauthorized access to their broker accounts and enabling the cybercriminals to perform illicit financial transactions and withdraw funds. If a true positive is found, it suggests that an attacker has successfully exploited the vulnerability to execute malicious scripts, leading to unauthorized access, financial loss, and potentially the delivery of additional malicious payloads. The impact of the attack could be severe, involving financial loss, unauthorized access to sensitive accounts, and the potential for further malicious activity such as data theft or ransomware attacks.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Be aware of potential false positives - legitimate uses of WinRAR and the listed processes in your environment may cause benign activities to be flagged. Upon triage, review the destination, user, parent process, and process name involved in the flagged activity. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.creation_date = 2023-08-29
action.escu.modification_date = 2023-08-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - WinRAR Spawning Shell Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["WinRAR Spoofing Attack CVE-2023-38831"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to decode a file.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 70}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WinRAR Spawning Shell Application - Rule
action.correlationsearch.annotations = {"analytic_story": ["WinRAR Spoofing Attack CVE-2023-38831"], "cis20": ["CIS 10"], "confidence": 70, "cve": ["CVE-2023-38831"], "impact": 100, "mitre_attack": ["T1105"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the execution of Windows shell processes initiated by WinRAR, specifically looking for instances where WinRAR spawns processes like "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe", or "bitsadmin.exe". This behavior is worth identifying for a Security Operations Center (SOC) because it is indicative of a spoofing attack exploit, such as the one associated with WinRAR CVE-2023-38831. Cybercriminals exploited this vulnerability to craft ZIP archives with spoofed extensions, hiding the launch of malicious scripts within an archive. When a victim opened the specially crafted archive, it executed the malware, leading to unauthorized access to their broker accounts and enabling the cybercriminals to perform illicit financial transactions and withdraw funds. If a true positive is found, it suggests that an attacker has successfully exploited the vulnerability to execute malicious scripts, leading to unauthorized access, financial loss, and potentially the delivery of additional malicious payloads. The impact of the attack could be severe, involving financial loss, unauthorized access to sensitive accounts, and the potential for further malicious activity such as data theft or ransomware attacks.
action.notable.param.rule_title = WinRAR Spawning Shell Application
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winrar.exe `windows_shells` OR Processes.process_name IN ("certutil.exe","mshta.exe","bitsadmin.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrar_spawning_shell_application_filter`

[ESCU - WinRM Spawning a Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious processes spawning from WinRM (wsmprovhost.exe). This analytic is related to potential exploitation of CVE-2021-31166. which is a kernel-mode device driver http.sys vulnerability. Current proof of concept code will blue-screen the operating system. However, http.sys used by many different Windows processes, including WinRM. In this case, identifying suspicious process create (child processes) from `wsmprovhost.exe` is what this analytic is identifying.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies suspicious processes spawning from WinRM (wsmprovhost.exe). This analytic is related to potential exploitation of CVE-2021-31166. which is a kernel-mode device driver http.sys vulnerability. Current proof of concept code will blue-screen the operating system. However, http.sys used by many different Windows processes, including WinRM. In this case, identifying suspicious process create (child processes) from `wsmprovhost.exe` is what this analytic is identifying.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Unknown. Add new processes or filter as needed. It is possible system management software may spawn processes from `wsmprovhost.exe`.
action.escu.creation_date = 2021-05-21
action.escu.modification_date = 2021-05-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - WinRM Spawning a Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WinRM Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2021-31166"], "impact": 50, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies suspicious processes spawning from WinRM (wsmprovhost.exe). This analytic is related to potential exploitation of CVE-2021-31166. which is a kernel-mode device driver http.sys vulnerability. Current proof of concept code will blue-screen the operating system. However, http.sys used by many different Windows processes, including WinRM. In this case, identifying suspicious process create (child processes) from `wsmprovhost.exe` is what this analytic is identifying.
action.notable.param.rule_title = WinRM Spawning a Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrm_spawning_a_process_filter`

[ESCU - Winword Spawning Cmd - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies Microsoft Word spawning `cmd.exe`. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to `explorer.exe` or `wmiprvse.exe`.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies Microsoft Word spawning `cmd.exe`. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to `explorer.exe` or `wmiprvse.exe`.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, but if any are present, filter as needed.
action.escu.creation_date = 2021-04-22
action.escu.modification_date = 2021-04-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Winword Spawning Cmd - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "DarkCrystal RAT", "CVE-2023-21716 Word RTF Heap Corruption"]
action.risk = 1
action.risk.param._risk_message = $parent_process_name$ on $dest$ by $user$ launched command: $process_name$ which is very common in spearphishing attacks.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Winword Spawning Cmd - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "DarkCrystal RAT", "CVE-2023-21716 Word RTF Heap Corruption"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies Microsoft Word spawning `cmd.exe`. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to `explorer.exe` or `wmiprvse.exe`.
action.notable.param.rule_title = Winword Spawning Cmd
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe `process_cmd` by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winword_spawning_cmd_filter`

[ESCU - Winword Spawning PowerShell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives should be limited, but if any are present, filter as needed.
action.escu.creation_date = 2021-04-12
action.escu.modification_date = 2021-04-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Winword Spawning PowerShell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "DarkCrystal RAT", "CVE-2023-21716 Word RTF Heap Corruption"]
action.risk = 1
action.risk.param._risk_message = $parent_process_name$ on $dest$ by $user$ launched the following powershell process: $process_name$ which is very common in spearphishing attacks
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Winword Spawning PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "DarkCrystal RAT", "CVE-2023-21716 Word RTF Heap Corruption"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
action.notable.param.rule_title = Winword Spawning PowerShell
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" `process_powershell` by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `winword_spawning_powershell_filter`

[ESCU - Winword Spawning Windows Script Host - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies Microsoft Winword.exe spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64\`. `cscript.exe` or `wscript.exe` spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies Microsoft Winword.exe spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64\`. `cscript.exe` or `wscript.exe` spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = There will be limited false positives and it will be different for every environment. Tune by child process or command-line as needed.
action.escu.creation_date = 2021-04-12
action.escu.modification_date = 2021-04-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Winword Spawning Windows Script Host - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Spearphishing Attachments", "CVE-2023-21716 Word RTF Heap Corruption"]
action.risk = 1
action.risk.param._risk_message = User $user$ on $dest$ spawned Windows Script Host from Winword.exe
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Winword Spawning Windows Script Host - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "CVE-2023-21716 Word RTF Heap Corruption"], "cis20": ["CIS 10"], "confidence": 100, "impact": 70, "mitre_attack": ["T1566", "T1566.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following detection identifies Microsoft Winword.exe spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64\`. `cscript.exe` or `wscript.exe` spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
action.notable.param.rule_title = Winword Spawning Windows Script Host
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winword_spawning_windows_script_host_filter`

[ESCU - WMI Permanent Event Subscription - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of permanent event subscriptions using Windows Management Instrumentation (WMI), which is used by attackers to achieve persistence in a compromised system. By creating a permanent event subscription, an attacker can run malicious scripts or binaries in response to specific system events that enables them to maintain access to the system undetected. The detection is made by using Sysmon Event ID 5 data to detect instances where the consumers of these events are not the expected "NTEventLogEventConsumer." The detection is important because it identifies unusual or unexpected subscription creation, which suggests that an attacker is attempting to achieve persistence within the environment and might be executing malicious scripts or binaries in response to specific system events. The impact of such an attack can be severe, potentially leading to data theft, ransomware, or other damaging outcomes. False positives might occur since False positives might occur since WMI event subscriptions can be used for legitimate purposes by system administrators. You must have a thorough understanding of WMI activity within the context of the monitored environment to effectively differentiate between legitimate and malicious activity.Next steps include investigating the associated scripts or binaries and identifying the source of the attack.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the creation of permanent event subscriptions using Windows Management Instrumentation (WMI), which is used by attackers to achieve persistence in a compromised system. By creating a permanent event subscription, an attacker can run malicious scripts or binaries in response to specific system events that enables them to maintain access to the system undetected. The detection is made by using Sysmon Event ID 5 data to detect instances where the consumers of these events are not the expected "NTEventLogEventConsumer." The detection is important because it identifies unusual or unexpected subscription creation, which suggests that an attacker is attempting to achieve persistence within the environment and might be executing malicious scripts or binaries in response to specific system events. The impact of such an attack can be severe, potentially leading to data theft, ransomware, or other damaging outcomes. False positives might occur since False positives might occur since WMI event subscriptions can be used for legitimate purposes by system administrators. You must have a thorough understanding of WMI activity within the context of the monitored environment to effectively differentiate between legitimate and malicious activity.Next steps include investigating the associated scripts or binaries and identifying the source of the attack.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
action.escu.known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
action.escu.creation_date = 2018-10-23
action.escu.modification_date = 2018-10-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of permanent event subscriptions using Windows Management Instrumentation (WMI), which is used by attackers to achieve persistence in a compromised system. By creating a permanent event subscription, an attacker can run malicious scripts or binaries in response to specific system events that enables them to maintain access to the system undetected. The detection is made by using Sysmon Event ID 5 data to detect instances where the consumers of these events are not the expected "NTEventLogEventConsumer." The detection is important because it identifies unusual or unexpected subscription creation, which suggests that an attacker is attempting to achieve persistence within the environment and might be executing malicious scripts or binaries in response to specific system events. The impact of such an attack can be severe, potentially leading to data theft, ransomware, or other damaging outcomes. False positives might occur since False positives might occur since WMI event subscriptions can be used for legitimate purposes by system administrators. You must have a thorough understanding of WMI activity within the context of the monitored environment to effectively differentiate between legitimate and malicious activity.Next steps include investigating the associated scripts or binaries and identifying the source of the attack.
action.notable.param.rule_title = WMI Permanent Event Subscription
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?<consumer>[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter`

[ESCU - WMI Permanent Event Subscription - Sysmon - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic looks for the creation of WMI permanent event subscriptions. The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID = 19 \
1. Consumer - An action to take upon triggering the filter. EventID = 20 \
1. Binding - Registers a filter to a consumer. EventID = 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1546.003", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic looks for the creation of WMI permanent event subscriptions. The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID = 19 \
1. Consumer - An action to take upon triggering the filter. EventID = 20 \
1. Binding - Registers a filter to a consumer. EventID = 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.
action.escu.how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity (eventID= 19, 20, 21). In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.
action.escu.known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
action.escu.creation_date = 2020-12-08
action.escu.modification_date = 2020-12-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Sysmon - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = User $user$ on $host$ executed the following suspicious WMI query: $Query$.  Filter: $filter$. Consumer: $Consumer$.  EventCode: $EventCode$
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Sysmon - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 100, "impact": 30, "mitre_attack": ["T1546.003", "T1546"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic looks for the creation of WMI permanent event subscriptions. The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID = 19 \
1. Consumer - An action to take upon triggering the filter. EventID = 20 \
1. Binding - Registers a filter to a consumer. EventID = 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.
action.notable.param.rule_title = WMI Permanent Event Subscription - Sysmon
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter`

[ESCU - WMI Recon Running Process Or Services - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies suspicious PowerShell script execution via EventCode 4104, where WMI is performing an event query looking for running processes or running services. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1592"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies suspicious PowerShell script execution via EventCode 4104, where WMI is performing an event query looking for running processes or running services. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.
action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
action.escu.known_false_positives = Network administrator may used this command for checking purposes
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - WMI Recon Running Process Or Services - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Microsoft Windows"]
action.escu.analytic_story = ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"]
action.risk = 1
action.risk.param._risk_message = Suspicious powerShell script execution by $user$ on $Computer$ via EventCode 4104, where WMI is performing an event query looking for running processes or running services
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 20}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 20}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WMI Recon Running Process Or Services - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Hermetic Wiper", "Data Destruction"], "cis20": ["CIS 10"], "confidence": 100, "impact": 20, "mitre_attack": ["T1592"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `powershell` EventCode=4104 ScriptBlockText= "*SELECT*" AND (ScriptBlockText="*Win32_Process*" OR ScriptBlockText="*Win32_Service*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmi_recon_running_process_or_services_filter`

[ESCU - WMI Temporary Event Subscription - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the creation of WMI temporary event subscriptions. WMI (Windows Management Instrumentation) is a management technology that allows administrators to perform various tasks on Windows-based systems. Temporary event subscriptions are created to monitor specific events or changes on a system that help to detect potential threats early and take proactive measures to protect the organization's systems and data. The detection is made by using the Splunk query `wmi` EventCode=5860 Temporary to search for events with EventCode 5860, which indicates the creation of a temporary WMI event subscription. To further refine the search results, the query uses regular expressions (rex) to extract the query used in the event subscription. Then, it filters known benign queries related to system processes such as 'wsmprovhost.exe' and 'AntiVirusProduct', 'FirewallProduct', 'AntiSpywareProduct', which helps to focus on potentially malicious or suspicious queries. The detection is important because it indicates malicious activity since attackers use WMI to run commands, gather information, or maintain persistence within a compromised system. False positives might occur since legitimate uses of WMI event subscriptions in the environment might trigger benign activities to be flagged. Therefore, an extensive triage is necessary to review the specific query and assess its intent. Additionally, capturing and inspecting relevant on-disk artifacts and analyzing concurrent processes can help to identify the source of the attack. Detecting the creation of these event subscriptions to identify potential threats early and take appropriate actions to mitigate the risks.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects the creation of WMI temporary event subscriptions. WMI (Windows Management Instrumentation) is a management technology that allows administrators to perform various tasks on Windows-based systems. Temporary event subscriptions are created to monitor specific events or changes on a system that help to detect potential threats early and take proactive measures to protect the organization's systems and data. The detection is made by using the Splunk query `wmi` EventCode=5860 Temporary to search for events with EventCode 5860, which indicates the creation of a temporary WMI event subscription. To further refine the search results, the query uses regular expressions (rex) to extract the query used in the event subscription. Then, it filters known benign queries related to system processes such as 'wsmprovhost.exe' and 'AntiVirusProduct', 'FirewallProduct', 'AntiSpywareProduct', which helps to focus on potentially malicious or suspicious queries. The detection is important because it indicates malicious activity since attackers use WMI to run commands, gather information, or maintain persistence within a compromised system. False positives might occur since legitimate uses of WMI event subscriptions in the environment might trigger benign activities to be flagged. Therefore, an extensive triage is necessary to review the specific query and assess its intent. Additionally, capturing and inspecting relevant on-disk artifacts and analyzing concurrent processes can help to identify the source of the attack. Detecting the creation of these event subscriptions to identify potential threats early and take appropriate actions to mitigate the risks.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
action.escu.known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events.
action.escu.creation_date = 2018-10-23
action.escu.modification_date = 2018-10-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - WMI Temporary Event Subscription - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WMI Temporary Event Subscription - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the creation of WMI temporary event subscriptions. WMI (Windows Management Instrumentation) is a management technology that allows administrators to perform various tasks on Windows-based systems. Temporary event subscriptions are created to monitor specific events or changes on a system that help to detect potential threats early and take proactive measures to protect the organization's systems and data. The detection is made by using the Splunk query `wmi` EventCode=5860 Temporary to search for events with EventCode 5860, which indicates the creation of a temporary WMI event subscription. To further refine the search results, the query uses regular expressions (rex) to extract the query used in the event subscription. Then, it filters known benign queries related to system processes such as 'wsmprovhost.exe' and 'AntiVirusProduct', 'FirewallProduct', 'AntiSpywareProduct', which helps to focus on potentially malicious or suspicious queries. The detection is important because it indicates malicious activity since attackers use WMI to run commands, gather information, or maintain persistence within a compromised system. False positives might occur since legitimate uses of WMI event subscriptions in the environment might trigger benign activities to be flagged. Therefore, an extensive triage is necessary to review the specific query and assess its intent. Additionally, capturing and inspecting relevant on-disk artifacts and analyzing concurrent processes can help to identify the source of the attack. Detecting the creation of these event subscriptions to identify potential threats early and take appropriate actions to mitigate the risks.
action.notable.param.rule_title = WMI Temporary Event Subscription
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?<query>[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter`

[ESCU - Wmic Group Discovery - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies the use of `wmic.exe` enumerating local groups on the endpoint. \
Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \
During triage, review parallel processes and identify any further suspicious behavior.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following hunting analytic identifies the use of `wmic.exe` enumerating local groups on the endpoint. \
Typically, by itself, is not malicious but may raise suspicion based on time of day, endpoint and username. \
During triage, review parallel processes and identify any further suspicious behavior.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators or power users may use this command for troubleshooting.
action.escu.creation_date = 2021-09-14
action.escu.modification_date = 2021-09-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wmic Group Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Discovery"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wmic Group Discovery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "cis20": ["CIS 10"], "confidence": 50, "impact": 30, "mitre_attack": ["T1069", "T1069.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe (Processes.process="*group get name*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmic_group_discovery_filter`

[ESCU - Wmic NonInteractive App Uninstallation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic indentifies WMIC command-line attempting to uninstall application non-interactively. This technique was seen in IcedID to uninstall AV products on the compromised host to evade detection. This Hunting query maybe a good indicator that some process tries to uninstall application using wmic which is not a common behavior. This approach may seen in some script or third part appication to uninstall their application but it is a good thing to check what it uninstall and why.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic indentifies WMIC command-line attempting to uninstall application non-interactively. This technique was seen in IcedID to uninstall AV products on the compromised host to evade detection. This Hunting query maybe a good indicator that some process tries to uninstall application using wmic which is not a common behavior. This approach may seen in some script or third part appication to uninstall their application but it is a good thing to check what it uninstall and why.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Third party application may use this approach to uninstall applications.
action.escu.creation_date = 2022-07-19
action.escu.modification_date = 2022-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wmic NonInteractive App Uninstallation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["IcedID", "Azorult"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wmic NonInteractive App Uninstallation - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Azorult"], "cis20": ["CIS 10"], "confidence": 50, "impact": 50, "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe Processes.process="* product *" Processes.process="*where name*" Processes.process="*call uninstall*" Processes.process="*/nointeractive*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmic_noninteractive_app_uninstallation_filter`

[ESCU - WMIC XSL Execution via URL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `wmic.exe` loading a remote XSL (eXtensible Stylesheet Language) script. This originally was identified by Casey Smith, dubbed Squiblytwo, as an application control bypass. Many adversaries will utilize this technique to invoke JScript or VBScript within an XSL file. This technique can also execute local/remote scripts and, similar to its Regsvr32 "Squiblydoo" counterpart, leverages a trusted, built-in Windows tool. Adversaries may abuse any alias in Windows Management Instrumentation provided they utilize the /FORMAT switch. Upon identifying a suspicious execution, review for confirmed network connnection and script download.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1220"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `wmic.exe` loading a remote XSL (eXtensible Stylesheet Language) script. This originally was identified by Casey Smith, dubbed Squiblytwo, as an application control bypass. Many adversaries will utilize this technique to invoke JScript or VBScript within an XSL file. This technique can also execute local/remote scripts and, similar to its Regsvr32 "Squiblydoo" counterpart, leverages a trusted, built-in Windows tool. Adversaries may abuse any alias in Windows Management Instrumentation provided they utilize the /FORMAT switch. Upon identifying a suspicious execution, review for confirmed network connnection and script download.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = False positives are limited as legitimate applications typically do not download files or xsl using WMIC. Filter as needed.
action.escu.creation_date = 2021-11-11
action.escu.modification_date = 2021-11-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - WMIC XSL Execution via URL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to download a remote XSL script.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 80}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WMIC XSL Execution via URL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1220"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `wmic.exe` loading a remote XSL (eXtensible Stylesheet Language) script. This originally was identified by Casey Smith, dubbed Squiblytwo, as an application control bypass. Many adversaries will utilize this technique to invoke JScript or VBScript within an XSL file. This technique can also execute local/remote scripts and, similar to its Regsvr32 "Squiblydoo" counterpart, leverages a trusted, built-in Windows tool. Adversaries may abuse any alias in Windows Management Instrumentation provided they utilize the /FORMAT switch. Upon identifying a suspicious execution, review for confirmed network connnection and script download.
action.notable.param.rule_title = WMIC XSL Execution via URL
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process IN ("*http://*", "*https://*") Processes.process="*/format:*" by Processes.parent_process_name Processes.original_file_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmic_xsl_execution_via_url_filter`

[ESCU - Wmiprsve LOLBAS Execution Process Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `wmiprsve.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing Windows Management Instrumentation (WMI), the executed command is spawned as a child process of `wmiprvse.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of wmiprvse.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `wmiprsve.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing Windows Management Instrumentation (WMI), the executed command is spawned as a child process of `wmiprvse.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of wmiprvse.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate applications may trigger this behavior, filter as needed.
action.escu.creation_date = 2021-11-22
action.escu.modification_date = 2021-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wmiprsve LOLBAS Execution Process Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = Wmiprsve.exe spawned a LOLBAS process on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wmiprsve LOLBAS Execution Process Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1047"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `wmiprsve.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing Windows Management Instrumentation (WMI), the executed command is spawned as a child process of `wmiprvse.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of wmiprvse.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.notable.param.rule_title = Wmiprsve LOLBAS Execution Process Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmiprsve_lolbas_execution_process_spawn_filter`

[ESCU - Wscript Or Cscript Suspicious Child Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a suspicious spawned process by WScript or CScript process. This technique was a common technique used by adversaries and malware to execute different LOLBIN, other scripts like PowerShell or spawn a suspended process to inject its code as a defense evasion. This TTP may detect some normal script that using several application tool that are in the list of the child process it detects but a good pivot and indicator that a script is may execute suspicious code.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1055", "T1543", "T1134.004", "T1134"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a suspicious spawned process by WScript or CScript process. This technique was a common technique used by adversaries and malware to execute different LOLBIN, other scripts like PowerShell or spawn a suspended process to inject its code as a defense evasion. This TTP may detect some normal script that using several application tool that are in the list of the child process it detects but a good pivot and indicator that a script is may execute suspicious code.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Administrators may create vbs or js script that use several tool as part of its execution. Filter as needed.
action.escu.creation_date = 2023-04-14
action.escu.modification_date = 2023-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wscript Or Cscript Suspicious Child Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Remcos", "FIN7", "Unusual Processes", "Data Destruction", "WhisperGate", "NjRAT"]
action.risk = 1
action.risk.param._risk_message = wscript or cscript parent process spawned $process_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wscript Or Cscript Suspicious Child Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos", "FIN7", "Unusual Processes", "Data Destruction", "WhisperGate", "NjRAT"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1055", "T1543", "T1134.004", "T1134"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies a suspicious spawned process by WScript or CScript process. This technique was a common technique used by adversaries and malware to execute different LOLBIN, other scripts like PowerShell or spawn a suspended process to inject its code as a defense evasion. This TTP may detect some normal script that using several application tool that are in the list of the child process it detects but a good pivot and indicator that a script is may execute suspicious code.
action.notable.param.rule_title = Wscript Or Cscript Suspicious Child Process
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("cscript.exe", "wscript.exe") Processes.process_name IN ("regsvr32.exe", "rundll32.exe","winhlp32.exe","certutil.exe","msbuild.exe","cmd.exe","powershell*","wmic.exe","mshta.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wscript_or_cscript_suspicious_child_process_filter`

[ESCU - Wsmprovhost LOLBAS Execution Process Spawn - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Windows Remote Management (WinRm) protocol, the executed command is spawned as a child processs of `Wsmprovhost.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of Wsmprovhost.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Windows Remote Management (WinRm) protocol, the executed command is spawned as a child processs of `Wsmprovhost.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of Wsmprovhost.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = Legitimate applications may trigger this behavior, filter as needed.
action.escu.creation_date = 2021-11-22
action.escu.modification_date = 2021-11-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Wsmprovhost LOLBAS Execution Process Spawn - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = Wsmprovhost.exe spawned a LOLBAS process on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 54}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Wsmprovhost LOLBAS Execution Process Spawn - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 10"], "confidence": 60, "impact": 90, "mitre_attack": ["T1021", "T1021.006"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Windows Remote Management (WinRm) protocol, the executed command is spawned as a child processs of `Wsmprovhost.exe`. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. Looking for child processes of Wsmprovhost.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.notable.param.rule_title = Wsmprovhost LOLBAS Execution Process Spawn
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wsmprovhost.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wsmprovhost_lolbas_execution_process_spawn_filter`

[ESCU - WSReset UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious modification of registry related to UAC bypass. This technique is to modify the registry in this detection, create a registry value with the path of the payload and run WSreset.exe to bypass User account Control.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious modification of registry related to UAC bypass. This technique is to modify the registry in this detection, create a registry value with the path of the payload and run WSreset.exe to bypass User account Control.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2022-11-14
action.escu.modification_date = 2022-11-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - WSReset UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Living Off The Land", "Windows Registry Abuse"]
action.risk = 1
action.risk.param._risk_message = Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WSReset UAC Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Living Off The Land", "Windows Registry Abuse"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1548.002", "T1548"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious modification of registry related to UAC bypass. This technique is to modify the registry in this detection, create a registry value with the path of the payload and run WSreset.exe to bypass User account Control.
action.notable.param.rule_title = WSReset UAC Bypass
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" AND (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "DelegateExecute") by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter`

[ESCU - XMRIG Driver Loaded - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies XMRIG coinminer driver installation on the system. The XMRIG driver name by default is `WinRing0x64.sys`. This cpu miner is an open source project that is commonly abused by adversaries to infect and mine bitcoin.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies XMRIG coinminer driver installation on the system. The XMRIG driver name by default is `WinRing0x64.sys`. This cpu miner is an open source project that is commonly abused by adversaries to infect and mine bitcoin.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = False positives should be limited.
action.escu.creation_date = 2021-04-29
action.escu.modification_date = 2021-04-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - XMRIG Driver Loaded - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["XMRig", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = A driver $ImageLoaded$ related to xmrig crytominer loaded in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - XMRIG Driver Loaded - Rule
action.correlationsearch.annotations = {"analytic_story": ["XMRig", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 100, "impact": 80, "mitre_attack": ["T1543.003", "T1543"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic identifies XMRIG coinminer driver installation on the system. The XMRIG driver name by default is `WinRing0x64.sys`. This cpu miner is an open source project that is commonly abused by adversaries to infect and mine bitcoin.
action.notable.param.rule_title = XMRIG Driver Loaded
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" |  stats  min(_time) as firstTime max(_time) as lastTime count by  dest ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xmrig_driver_loaded_filter`

[ESCU - XSL Script Execution With WMIC - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious wmic.exe process or renamed wmic process to execute malicious xsl file. This technique was seen in FIN7 to execute its malicous jscript using the .xsl as the loader with the help of wmic.exe process. This TTP is really a good indicator for you to hunt further for FIN7 or other attacker that known to used this technique.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1220"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search is to detect a suspicious wmic.exe process or renamed wmic process to execute malicious xsl file. This technique was seen in FIN7 to execute its malicous jscript using the .xsl as the loader with the help of wmic.exe process. This TTP is really a good indicator for you to hunt further for FIN7 or other attacker that known to used this technique.
action.escu.how_to_implement = The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-09-13
action.escu.modification_date = 2021-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - XSL Script Execution With WMIC - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = ["Sysmon", "Microsoft Windows", "Carbon Black Response", "CrowdStrike Falcon", "Symantec Endpoint Protection"]
action.escu.analytic_story = ["FIN7", "Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to load a XSL script.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "parent_process_name", "risk_object_type": "other", "risk_score": 49}, {"risk_object_field": "process_name", "risk_object_type": "other", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - XSL Script Execution With WMIC - Rule
action.correlationsearch.annotations = {"analytic_story": ["FIN7", "Suspicious WMI Use"], "cis20": ["CIS 10"], "confidence": 70, "impact": 70, "mitre_attack": ["T1220"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect a suspicious wmic.exe process or renamed wmic process to execute malicious xsl file. This technique was seen in FIN7 to execute its malicous jscript using the .xsl as the loader with the help of wmic.exe process. This TTP is really a good indicator for you to hunt further for FIN7 or other attacker that known to used this technique.
action.notable.param.rule_title = XSL Script Execution With WMIC
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process = "*os get*" Processes.process="*/format:*" Processes.process = "*.xsl*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xsl_script_execution_with_wmic_filter`

[ESCU - Detect ARP Poisoning - Rule]
action.escu = 0
action.escu.enabled = 1
description = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure.
action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
action.escu.known_false_positives = This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely).
action.escu.creation_date = 2020-08-11
action.escu.modification_date = 2020-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect ARP Poisoning - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect ARP Poisoning - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure.
action.notable.param.rule_title = Detect ARP Poisoning
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter`

[ESCU - Detect DGA domains using pretrained model in DSDL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses a pre trained deep learning model to detect Domain Generation Algorithm (DGA) generated domains. The model is trained independently and is then made available for download. One of the prominent indicators of a domain being DGA generated is if the domain name consists of unusual character sequences or concatenated dictionary words. Adversaries often use clever techniques to obfuscate machine generated domain names as human generated. Predicting DGA generated domain names requires analysis and building a model based on carefully chosen features. The deep learning model we have developed uses the domain name to analyze patterns of character sequences along with carefully chosen custom features to predict if a domain is DGA generated. The model takes a domain name consisting of second-level and top-level domain names as input and outputs a dga_score. Higher the dga_score, the more likely the input domain is a DGA domain. The threshold for flagging a domain as DGA is set at 0.5.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1568.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The following analytic uses a pre trained deep learning model to detect Domain Generation Algorithm (DGA) generated domains. The model is trained independently and is then made available for download. One of the prominent indicators of a domain being DGA generated is if the domain name consists of unusual character sequences or concatenated dictionary words. Adversaries often use clever techniques to obfuscate machine generated domain names as human generated. Predicting DGA generated domain names requires analysis and building a model based on carefully chosen features. The deep learning model we have developed uses the domain name to analyze patterns of character sequences along with carefully chosen custom features to predict if a domain is DGA generated. The model takes a domain name consisting of second-level and top-level domain names as input and outputs a dga_score. Higher the dga_score, the more likely the input domain is a DGA domain. The threshold for flagging a domain as DGA is set at 0.5.
action.escu.how_to_implement = Steps to deploy DGA detection model into Splunk App DSDL.\ This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU.\ * Download the artifacts .tar.gz file from the link  `https://seal.splunkresearch.com/pretrained_dga_model_dsdl.tar.gz`\
* Download the pretrained_dga_model_dsdl.ipynb Jupyter notebook from `https://github.com/splunk/security_content/notebooks`\
* Login to the Jupyter Lab for pretrained_dga_model_dsdl container. This container should be listed on Containers page for DSDL app.\
* Below steps need to be followed inside Jupyter lab \
* Upload the pretrained_dga_model_dsdl.tar.gz file into `app/model/data` path using the upload option in the jupyter notebook.\
*  Untar the artifact `pretrained_dga_model_dsdl.tar.gz` using `tar -xf app/model/data/pretrained_dga_model_dsdl.tar.gz -C app/model/data`\
* Upload `pretrained_dga_model_dsdl.pynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab\
* Save the notebook using the save option in jupyter notebook.\
* Upload `pretrained_dga_model_dsdl.json` into `notebooks/data` folder.
action.escu.known_false_positives = False positives may be present if domain name is similar to dga generated domains.
action.escu.creation_date = 2023-01-18
action.escu.modification_date = 2023-01-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect DGA domains using pretrained model in DSDL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Exfiltration", "DNS Hijacking", "Suspicious DNS Traffic", "Dynamic DNS", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = A potential connection to a DGA domain $domain$ was detected from host $src$, kindly review.
action.risk.param._risk = [{"threat_object_field": "domain", "threat_object_type": "url string"}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect DGA domains using pretrained model in DSDL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration", "DNS Hijacking", "Suspicious DNS Traffic", "Dynamic DNS", "Command And Control"], "cis20": ["CIS 10"], "confidence": 90, "impact": 70, "mitre_attack": ["T1568.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime  max(_time) as lastTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(DNS)` | rename query AS domain | fields IPs, src, domain, firstTime, lastTime | apply pretrained_dga_model_dsdl | rename pred_dga_proba AS dga_score | where dga_score>0.5 | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | table src, domain, IPs, firstTime, lastTime, dga_score | `detect_dga_domains_using_pretrained_model_in_dsdl_filter`

[ESCU - Detect DNS Data Exfiltration using pretrained model in DSDL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses a pre trained deep learning model to detect DNS data exfiltration. The model is trained on the data we collected and is inferred on live data. This detection detects low throughput DNS Tunneling (data exfiltration) using features computed from past events between the same src and domain. The search uses macros from URL ToolBox app to generate features used by the model. The model is a deep learning model that accepts DNS request as input along with a few custom features to generate a pred_is_exfiltration_proba score. The higher the pred_is_exfiltration_proba, the more likely the DNS request is data exfiltration. The threshold for flagging a request as DNS exfiltration is set at 0.5.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The following analytic uses a pre trained deep learning model to detect DNS data exfiltration. The model is trained on the data we collected and is inferred on live data. This detection detects low throughput DNS Tunneling (data exfiltration) using features computed from past events between the same src and domain. The search uses macros from URL ToolBox app to generate features used by the model. The model is a deep learning model that accepts DNS request as input along with a few custom features to generate a pred_is_exfiltration_proba score. The higher the pred_is_exfiltration_proba, the more likely the DNS request is data exfiltration. The threshold for flagging a request as DNS exfiltration is set at 0.5.
action.escu.how_to_implement = Steps to deploy detect DNS data exfiltration model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\
* Download the `artifacts .tar.gz` file from the link - https://seal.splunkresearch.com/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz Download the `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.ipynb` Jupyter notebook from https://github.com/splunk/security_content/notebooks\
* Login to the Jupyter Lab assigned for detect_dns_data_exfiltration_using_pretrained_model_in_dsdl container. This container should be listed on Containers page for DSDL app.\
* Below steps need to be followed inside Jupyter lab \
* Upload the detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz file into `app/model/data` path using the upload option in the jupyter notebook.\
* Untar the artifact detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data`\
* Upload detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.pynb into Jupyter lab notebooks folder using the upload option in Jupyter lab \
* Save the notebook using the save option in jupyter notebook. \
* Upload `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.json` into `notebooks/data` folder.
action.escu.known_false_positives = False positives may be present if DNS data exfiltration request look very similar to benign DNS requests.
action.escu.creation_date = 2023-04-27
action.escu.modification_date = 2023-04-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect DNS Data Exfiltration using pretrained model in DSDL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DNS Hijacking", "Suspicious DNS Traffic", "Command and Control"]
action.risk = 1
action.risk.param._risk_message = A DNS data exfiltration request was sent by this host $src$ , kindly review.
action.risk.param._risk = [{"threat_object_field": "query", "threat_object_type": "other"}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect DNS Data Exfiltration using pretrained model in DSDL - Rule
action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking", "Suspicious DNS Traffic", "Command and Control"], "cis20": ["CIS 13"], "confidence": 90, "impact": 50, "mitre_attack": ["T1048.003"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.src _time DNS.query | `drop_dm_object_name("DNS")` | sort - _time,src, query | streamstats count as rank by src query | where rank < 10 | table src,query,rank,_time | apply detect_dns_data_exfiltration_using_pretrained_model_in_dsdl | table src,_time,query,rank,pred_is_dns_data_exfiltration_proba,pred_is_dns_data_exfiltration | where rank == 1 | rename pred_is_dns_data_exfiltration_proba as is_exfiltration_score | rename pred_is_dns_data_exfiltration as is_exfiltration | where is_exfiltration_score > 0.5 | `security_content_ctime(_time)` | table src, _time,query,is_exfiltration_score,is_exfiltration | `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter`

[ESCU - Detect hosts connecting to dynamic domain providers - Rule]
action.escu = 0
action.escu.enabled = 1
description = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive Command And Control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive Command And Control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.
action.escu.how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\
This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\
1. \
1. **Label:** DNS Answer, **Field:** answer\
1. \
1. **Label:** IsDynamicDNS, **Field:** isDynDNS\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.
action.escu.creation_date = 2021-01-14
action.escu.modification_date = 2021-01-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect hosts connecting to dynamic domain providers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Protection", "Prohibited Traffic Allowed or Protocol Mismatch", "DNS Hijacking", "Suspicious DNS Traffic", "Dynamic DNS", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = A dns query $query$ from your infra connecting to suspicious domain in host  $host$
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect hosts connecting to dynamic domain providers - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Protection", "Prohibited Traffic Allowed or Protocol Mismatch", "DNS Hijacking", "Suspicious DNS Traffic", "Dynamic DNS", "Command And Control"], "cis20": ["CIS 13"], "confidence": 80, "impact": 70, "mitre_attack": ["T1189"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive Command And Control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.
action.notable.param.rule_title = Detect hosts connecting to dynamic domain providers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter`

[ESCU - Detect IPv6 Network Infrastructure Threats - Rule]
action.escu = 0
action.escu.enabled = 1
description = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure.
action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
action.escu.known_false_positives = None currently known
action.escu.creation_date = 2020-10-28
action.escu.modification_date = 2020-10-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect IPv6 Network Infrastructure Threats - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect IPv6 Network Infrastructure Threats - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure.
action.notable.param.rule_title = Detect IPv6 Network Infrastructure Threats
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter`

[ESCU - Detect Large Outbound ICMP Packets - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1095"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.
action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model
action.escu.known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with Command And Control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list.
action.escu.creation_date = 2018-06-01
action.escu.modification_date = 2018-06-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Large Outbound ICMP Packets - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Large Outbound ICMP Packets - Rule
action.correlationsearch.annotations = {"analytic_story": ["Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1095"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.
action.notable.param.rule_title = Detect Large Outbound ICMP Packets
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter`

[ESCU - Detect Outbound LDAP Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = Malicious actors often abuse misconfigured LDAP servers or applications that use the LDAP servers in organizations. Outbound LDAP traffic should not be allowed outbound through your perimeter firewall.  This search will help determine if you have any LDAP connections to IP addresses outside of private (RFC1918) address space.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1059"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = Malicious actors often abuse misconfigured LDAP servers or applications that use the LDAP servers in organizations. Outbound LDAP traffic should not be allowed outbound through your perimeter firewall.  This search will help determine if you have any LDAP connections to IP addresses outside of private (RFC1918) address space.
action.escu.how_to_implement = You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format and should be mapped to the Network Traffic datamodels that are in use for this search.
action.escu.known_false_positives = Unknown at this moment. Outbound LDAP traffic should not be allowed outbound through your perimeter firewall. Please check those servers to verify if the activity is legitimate.
action.escu.creation_date = 2021-12-13
action.escu.modification_date = 2021-12-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Outbound LDAP Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Outbound LDAP Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2021-44228"], "impact": 70, "mitre_attack": ["T1190", "T1059"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats earliest(_time) as earliest_time latest(_time) as latest_time values(All_Traffic.dest_ip) as dest_ip from datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port = 389 OR All_Traffic.dest_port = 636 AND NOT (All_Traffic.dest_ip = 10.0.0.0/8 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip = 172.16.0.0/12) by All_Traffic.src_ip All_Traffic.dest_ip |`drop_dm_object_name("All_Traffic")` | where src_ip != dest_ip | `security_content_ctime(latest_time)`  | `security_content_ctime(earliest_time)` |`detect_outbound_ldap_traffic_filter`

[ESCU - Detect Outbound SMB Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1071.002", "T1071"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.
action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model
action.escu.known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Outbound SMB Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A", "NOBELIUM Group"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Outbound SMB Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "DHS Report TA18-074A", "NOBELIUM Group"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.002", "T1071"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.
action.notable.param.rule_title = Detect Outbound SMB Traffic
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter`

[ESCU - Detect Port Security Violation - Rule]
action.escu = 0
action.escu.enabled = 1
description = By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs.
action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
action.escu.known_false_positives = This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network.
action.escu.creation_date = 2020-10-28
action.escu.modification_date = 2020-10-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Port Security Violation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Port Security Violation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs.
action.notable.param.rule_title = Detect Port Security Violation
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter`

[ESCU - Detect Rogue DHCP Server - Rule]
action.escu = 0
action.escu.enabled = 1
description = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack).
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack).
action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
action.escu.known_false_positives = This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface.
action.escu.creation_date = 2020-08-11
action.escu.modification_date = 2020-08-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Rogue DHCP Server - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Rogue DHCP Server - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack).
action.notable.param.rule_title = Detect Rogue DHCP Server
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter`

[ESCU - Detect SNICat SNI Exfiltration - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for commands that the SNICat tool uses in the TLS SNI field.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1041"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for commands that the SNICat tool uses in the TLS SNI field.
action.escu.how_to_implement = You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito.  You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place.
action.escu.known_false_positives = Unknown
action.escu.creation_date = 2020-10-21
action.escu.modification_date = 2020-10-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect SNICat SNI Exfiltration - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect SNICat SNI Exfiltration - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1041"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for commands that the SNICat tool uses in the TLS SNI field.
action.notable.param.rule_title = Detect SNICat SNI Exfiltration
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`

[ESCU - Detect Software Download To Network Device - Rule]
action.escu = 0
action.escu.enabled = 1
description = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1542.005", "T1542"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.
action.escu.how_to_implement = This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory.
action.escu.known_false_positives = This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices.
action.escu.creation_date = 2020-10-28
action.escu.modification_date = 2020-10-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Software Download To Network Device - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Software Download To Network Device - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1542.005", "T1542"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.
action.notable.param.rule_title = Detect Software Download To Network Device
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter`

[ESCU - Detect suspicious DNS TXT records using pretrained model in DSDL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses a pre trained deep learning model to detect suspicious DNS TXT records. The model is trained independently and is then made available for download. The DNS TXT records are categorized into commonly identified types like email, verification, http using regular expressions https://www.tide-project.nl/blog/wtmc2020/. The TXT records that do not match regular expressions for well known types are labeled as 1 for "unknown/suspicious" and otherwise 0 for "not suspicious". The deep learning model we have developed uses DNS TXT responses to analyze patterns of character sequences to predict if a DNS TXT is suspicious or not. The higher the pred_is_unknown_proba, the more likely the DNS TXT record is suspicious. The threshold for flagging a domain as suspicious is set at 0.5.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1568.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The following analytic uses a pre trained deep learning model to detect suspicious DNS TXT records. The model is trained independently and is then made available for download. The DNS TXT records are categorized into commonly identified types like email, verification, http using regular expressions https://www.tide-project.nl/blog/wtmc2020/. The TXT records that do not match regular expressions for well known types are labeled as 1 for "unknown/suspicious" and otherwise 0 for "not suspicious". The deep learning model we have developed uses DNS TXT responses to analyze patterns of character sequences to predict if a DNS TXT is suspicious or not. The higher the pred_is_unknown_proba, the more likely the DNS TXT record is suspicious. The threshold for flagging a domain as suspicious is set at 0.5.
action.escu.how_to_implement = Steps to deploy detect suspicious DNS TXT records model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - `https://splunkbase.splunk.com/app/4607/` and the Network Resolution datamodel which can be found here - `https://splunkbase.splunk.com/app/1621/`. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\
* Download the `artifacts .tar.gz` file from the link - `https://seal.splunkresearch.com/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`.\
* Download the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` Jupyter notebook from `https://github.com/splunk/security_content/notebooks`.\
* Login to the Jupyter Lab assigned for  `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl` container. This container should be listed on Containers page for DSDL app.\
* Below steps need to be followed inside Jupyter lab.\
* Upload the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` file into `app/model/data` path using the upload option in the jupyter notebook.\
* Untar the artifact `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data`.\
* Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab.\
* Save the notebook using the save option in Jupyter notebook.\
* Upload `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json` into `notebooks/data` folder.
action.escu.known_false_positives = False positives may be present if DNS TXT record contents are similar to benign DNS TXT record contents.
action.escu.creation_date = 2023-01-15
action.escu.modification_date = 2023-01-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect suspicious DNS TXT records using pretrained model in DSDL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DNS Hijacking", "Suspicious DNS Traffic", "Command and Control"]
action.risk = 1
action.risk.param._risk_message = A suspicious DNS TXT response was detected on host $src$ , kindly review.
action.risk.param._risk = [{"threat_object_field": "answer", "threat_object_type": "other"}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect suspicious DNS TXT records using pretrained model in DSDL - Rule
action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking", "Suspicious DNS Traffic", "Command and Control"], "cis20": ["CIS 13"], "confidence": 90, "impact": 50, "mitre_attack": ["T1568.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | rename answer as text | fields firstTime, lastTime, message_type,record_type,src,dest, text | apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl | rename predicted_is_unknown as is_suspicious_score | where is_suspicious_score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score | `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter`

[ESCU - Detect Traffic Mirroring - Rule]
action.escu = 0
action.escu.enabled = 1
description = Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1200", "T1020", "T1498", "T1020.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device.
action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring.
action.escu.known_false_positives = This search will return false positives for any legitimate traffic captures by network administrators.
action.escu.creation_date = 2020-10-28
action.escu.modification_date = 2020-10-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Traffic Mirroring - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Router and Infrastructure Security"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Traffic Mirroring - Rule
action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1200", "T1020", "T1498", "T1020.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device.
action.notable.param.rule_title = Detect Traffic Mirroring
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter`

[ESCU - Detect Unauthorized Assets by MAC address - Rule]
action.escu = 0
action.escu.enabled = 1
description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Sessions"]
action.escu.eli5 = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found.
action.escu.how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated.
action.escu.known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information.
action.escu.creation_date = 2017-09-13
action.escu.modification_date = 2017-09-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Unauthorized Assets by MAC address - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Asset Tracking"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Unauthorized Assets by MAC address - Rule
action.correlationsearch.annotations = {"analytic_story": ["Asset Tracking"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found.
action.notable.param.rule_title = Detect Unauthorized Assets by MAC address
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.tag=dhcp by All_Sessions.dest_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter`

[ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule]
action.escu = 0
action.escu.enabled = 1
description = Ensure that the following prerequisites are met: (i) Both Splunk Stream DNS and TCP data are ingested. (ii) The macros 'stream:dns' and 'stream:tcp' are replaced with the appropriate configurations that are specific to your Splunk environment. The following analytic detects SIGRed exploitation attempts. SIGRed is a critical wormable vulnerability found in Windows DNS servers, known as CVE-2020-1350, which allows remote code execution. The detection is made by using an experimental search that focuses on identifying specific indicators that might suggest the presence of the SIGRed exploit such as DNS SIG records, KEY records, and TCP payloads greater than 65KB. This detection is important because it detects and responds to potential SIGRed exploitation attempts and minimizes the risk of a successful attack and its impact on the organization's infrastructure and data. False positives might occur due to the experimental nature of this analytic. Next steps include reviewing and investigating each case thoroughly given the potential for unauthorized Windows DNS server access, data breaches, and service disruptions. Additionally, you must stay updated with Microsoft's guidance on the SIGRed vulnerability.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Ensure that the following prerequisites are met: (i) Both Splunk Stream DNS and TCP data are ingested. (ii) The macros 'stream:dns' and 'stream:tcp' are replaced with the appropriate configurations that are specific to your Splunk environment. The following analytic detects SIGRed exploitation attempts. SIGRed is a critical wormable vulnerability found in Windows DNS servers, known as CVE-2020-1350, which allows remote code execution. The detection is made by using an experimental search that focuses on identifying specific indicators that might suggest the presence of the SIGRed exploit such as DNS SIG records, KEY records, and TCP payloads greater than 65KB. This detection is important because it detects and responds to potential SIGRed exploitation attempts and minimizes the risk of a successful attack and its impact on the organization's infrastructure and data. False positives might occur due to the experimental nature of this analytic. Next steps include reviewing and investigating each case thoroughly given the potential for unauthorized Windows DNS server access, data breaches, and service disruptions. Additionally, you must stay updated with Microsoft's guidance on the SIGRed vulnerability.
action.escu.how_to_implement = You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp.  Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-07-28
action.escu.modification_date = 2020-07-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows DNS SIGRed CVE-2020-1350"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows DNS SIGRed CVE-2020-1350"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2020-1350"], "impact": 50, "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = Ensure that the following prerequisites are met: (i) Both Splunk Stream DNS and TCP data are ingested. (ii) The macros 'stream:dns' and 'stream:tcp' are replaced with the appropriate configurations that are specific to your Splunk environment. The following analytic detects SIGRed exploitation attempts. SIGRed is a critical wormable vulnerability found in Windows DNS servers, known as CVE-2020-1350, which allows remote code execution. The detection is made by using an experimental search that focuses on identifying specific indicators that might suggest the presence of the SIGRed exploit such as DNS SIG records, KEY records, and TCP payloads greater than 65KB. This detection is important because it detects and responds to potential SIGRed exploitation attempts and minimizes the risk of a successful attack and its impact on the organization's infrastructure and data. False positives might occur due to the experimental nature of this analytic. Next steps include reviewing and investigating each case thoroughly given the potential for unauthorized Windows DNS server access, data breaches, and service disruptions. Additionally, you must stay updated with Microsoft's guidance on the SIGRed vulnerability.
action.notable.param.rule_title = Detect Windows DNS SIGRed via Splunk Stream
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count

[ESCU - Detect Windows DNS SIGRed via Zeek - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the presence of SIGRed, a critical DNS vulnerability, using Zeek DNS and Zeek Conn data. SIGRed vulnerability allows attackers to run remote code on Windows DNS servers. By detecting SIGRed early, you can prevent further damage and protect the organization's network infrastructure. The detection is made by identifying specific DNS query types (SIG and KEY) in the Zeek DNS data and checks for high data transfer in the Zeek Conn data. If multiple instances of these indicators are found within a flow, it suggests the presence of SIGRed. The detection is important because it indicates a potential compromise of Windows DNS servers that suggests that an attacker might have gained unauthorized access to the DNS server and can run arbitrary code. The impact of this attack can be severe, leading to data exfiltration, unauthorized access, or disruption of critical services. Next steps include investigating the affected flow and taking immediate action to mitigate the vulnerability. This can involve patching the affected DNS server, isolating the server from the network, or conducting a forensic analysis to determine the extent of the compromise.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution", "Network_Traffic"]
action.escu.eli5 = The following analytic detects the presence of SIGRed, a critical DNS vulnerability, using Zeek DNS and Zeek Conn data. SIGRed vulnerability allows attackers to run remote code on Windows DNS servers. By detecting SIGRed early, you can prevent further damage and protect the organization's network infrastructure. The detection is made by identifying specific DNS query types (SIG and KEY) in the Zeek DNS data and checks for high data transfer in the Zeek Conn data. If multiple instances of these indicators are found within a flow, it suggests the presence of SIGRed. The detection is important because it indicates a potential compromise of Windows DNS servers that suggests that an attacker might have gained unauthorized access to the DNS server and can run arbitrary code. The impact of this attack can be severe, leading to data exfiltration, unauthorized access, or disruption of critical services. Next steps include investigating the affected flow and taking immediate action to mitigate the vulnerability. This can involve patching the affected DNS server, isolating the server from the network, or conducting a forensic analysis to determine the extent of the compromise.
action.escu.how_to_implement = You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The Network Resolution and Network Traffic datamodels are in use for this search.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-07-28
action.escu.modification_date = 2020-07-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Windows DNS SIGRed via Zeek - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Windows DNS SIGRed CVE-2020-1350"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Windows DNS SIGRed via Zeek - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows DNS SIGRed CVE-2020-1350"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2020-1350"], "impact": 50, "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the presence of SIGRed, a critical DNS vulnerability, using Zeek DNS and Zeek Conn data. SIGRed vulnerability allows attackers to run remote code on Windows DNS servers. By detecting SIGRed early, you can prevent further damage and protect the organization's network infrastructure. The detection is made by identifying specific DNS query types (SIG and KEY) in the Zeek DNS data and checks for high data transfer in the Zeek Conn data. If multiple instances of these indicators are found within a flow, it suggests the presence of SIGRed. The detection is important because it indicates a potential compromise of Windows DNS servers that suggests that an attacker might have gained unauthorized access to the DNS server and can run arbitrary code. The impact of this attack can be severe, leading to data exfiltration, unauthorized access, or disruption of critical services. Next steps include investigating the affected flow and taking immediate action to mitigate the vulnerability. This can involve patching the affected DNS server, isolating the server from the network, or conducting a forensic analysis to determine the extent of the compromise.
action.notable.param.rule_title = Detect Windows DNS SIGRed via Zeek
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats  `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count 

[ESCU - Detect Zerologon via Zeek - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability through Zeek RPC. By detecting attempts to exploit the Zerologon vulnerability through Zeek RPC, SOC analysts can identify potential threats earlier and take appropriate action to mitigate the risks. This detection is made by a Splunk query that looks for specific Zeek RPC operations, including NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3, which are aggregated by source and destination IP address and time. This detection is important because it suggests that an attacker is attempting to exploit the Zerologon vulnerability to gain unauthorized access to the domain controller. Zerologon vulnerability is a critical vulnerability that allows attackers to take over domain controllers without authentication, leading to a complete takeover of an organization's IT infrastructure. The impact of such an attack can be severe, potentially leading to data theft, ransomware, or other devastating outcomes. False positives might occur since legitimate Zeek RPC activity can trigger the analytic. Next steps include reviewing the identified source and destination IP addresses and the specific RPC operations used. Capture and inspect any relevant on-disk artifacts, and review concurrent processes to identify the attack source upon triage .
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability through Zeek RPC. By detecting attempts to exploit the Zerologon vulnerability through Zeek RPC, SOC analysts can identify potential threats earlier and take appropriate action to mitigate the risks. This detection is made by a Splunk query that looks for specific Zeek RPC operations, including NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3, which are aggregated by source and destination IP address and time. This detection is important because it suggests that an attacker is attempting to exploit the Zerologon vulnerability to gain unauthorized access to the domain controller. Zerologon vulnerability is a critical vulnerability that allows attackers to take over domain controllers without authentication, leading to a complete takeover of an organization's IT infrastructure. The impact of such an attack can be severe, potentially leading to data theft, ransomware, or other devastating outcomes. False positives might occur since legitimate Zeek RPC activity can trigger the analytic. Next steps include reviewing the identified source and destination IP addresses and the specific RPC operations used. Capture and inspect any relevant on-disk artifacts, and review concurrent processes to identify the attack source upon triage .
action.escu.how_to_implement = You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-09-15
action.escu.modification_date = 2020-09-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Zerologon via Zeek - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Detect Zerologon Attack"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Zerologon via Zeek - Rule
action.correlationsearch.annotations = {"analytic_story": ["Detect Zerologon Attack"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2020-1472"], "impact": 50, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability through Zeek RPC. By detecting attempts to exploit the Zerologon vulnerability through Zeek RPC, SOC analysts can identify potential threats earlier and take appropriate action to mitigate the risks. This detection is made by a Splunk query that looks for specific Zeek RPC operations, including NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3, which are aggregated by source and destination IP address and time. This detection is important because it suggests that an attacker is attempting to exploit the Zerologon vulnerability to gain unauthorized access to the domain controller. Zerologon vulnerability is a critical vulnerability that allows attackers to take over domain controllers without authentication, leading to a complete takeover of an organization's IT infrastructure. The impact of such an attack can be severe, potentially leading to data theft, ransomware, or other devastating outcomes. False positives might occur since legitimate Zeek RPC activity can trigger the analytic. Next steps include reviewing the identified source and destination IP addresses and the specific RPC operations used. Capture and inspect any relevant on-disk artifacts, and review concurrent processes to identify the attack source upon triage .
action.notable.param.rule_title = Detect Zerologon via Zeek
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter`

[ESCU - DNS Query Length Outliers - MLTK - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1071.004", "T1071"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.
action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\
This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\
1. \
1. **Label:** DNS Query Length, **Field:** query_length\
1. \
1. **Label:** Number of events, **Field:** count\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.
action.escu.creation_date = 2020-01-22
action.escu.modification_date = 2020-01-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - DNS Query Length Outliers - MLTK - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - DNS Query Length Outliers - MLTK - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.004", "T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* |  `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` 

[ESCU - DNS Query Length With High Standard Deviation - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment.
action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.
action.escu.known_false_positives = It's possible there can be long domain names that are legitimate.
action.escu.creation_date = 2021-10-06
action.escu.modification_date = 2021-10-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - DNS Query Length With High Standard Deviation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = A dns query $query$ with 2 time standard deviation of name len of the dns query in host  $host$
action.risk.param._risk = [{"risk_object_field": "host", "risk_object_type": "system", "risk_score": 56}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - DNS Query Length With High Standard Deviation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command And Control"], "cis20": ["CIS 13"], "confidence": 80, "impact": 70, "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where NOT DNS.message_type IN("Pointer","PTR") by DNS.query host| `drop_dm_object_name("DNS")` | eval tlds=split(query,".") | eval tld=mvindex(tlds,-1) | eval tld_len=len(tld) | search tld_len<=24 | eval query_length = len(query) | table host query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter`

[ESCU - Excessive DNS Failures - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1071.004", "T1071"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences.
action.escu.how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
action.escu.known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment.
action.escu.creation_date = 2022-12-21
action.escu.modification_date = 2022-12-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive DNS Failures - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Suspicious DNS Traffic", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = Excessive DNS failures detected on $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Excessive DNS Failures - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious DNS Traffic", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1071.004", "T1071"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src" "DNS.query" "DNS.reply_code" | `drop_dm_object_name("DNS")` | lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain | where isnull(domain) | lookup update=true alexa_lookup_by_str domain as query OUTPUT rank | where isnull(rank) | eventstats max(count) as mc by src reply_code | eval mode_query=if(count=mc, query, null()) | stats sum(count) as count values(mode_query) as query values(mc) as max_query_count by src reply_code | where count>50 | `get_asset(src)` | `excessive_dns_failures_filter` 

[ESCU - F5 BIG-IP iControl REST Vulnerability CVE-2022-1388 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a recent unauthenticated remote code execution vulnerablity against the F5 BIG-IP iControl REST API. The analytic identifies the URI path found in the POCs and the HTTP Method of POST. In addition, the request header will have the commands that may be executed in fields utilcmdargs and the auth field of X-F5-Auth-Token, which may have a random base64 encoded value.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies a recent unauthenticated remote code execution vulnerablity against the F5 BIG-IP iControl REST API. The analytic identifies the URI path found in the POCs and the HTTP Method of POST. In addition, the request header will have the commands that may be executed in fields utilcmdargs and the auth field of X-F5-Auth-Token, which may have a random base64 encoded value.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
action.escu.known_false_positives = False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed.
action.escu.creation_date = 2022-05-10
action.escu.modification_date = 2022-05-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - F5 BIG-IP iControl REST Vulnerability CVE-2022-1388 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["F5 BIG-IP Vulnerability CVE-2022-1388"]
action.risk = 1
action.risk.param._risk_message = An attempt to exploit CVE-2022-1388 against an F5 appliance $dest$ has occurred.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 70}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - F5 BIG-IP iControl REST Vulnerability CVE-2022-1388 - Rule
action.correlationsearch.annotations = {"analytic_story": ["F5 BIG-IP Vulnerability CVE-2022-1388"], "cis20": ["CIS 13"], "confidence": 70, "cve": ["CVE-2022-1388"], "impact": 100, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a recent unauthenticated remote code execution vulnerablity against the F5 BIG-IP iControl REST API. The analytic identifies the URI path found in the POCs and the HTTP Method of POST. In addition, the request header will have the commands that may be executed in fields utilcmdargs and the auth field of X-F5-Auth-Token, which may have a random base64 encoded value.
action.notable.param.rule_title = F5 BIG-IP iControl REST Vulnerability CVE-2022-1388
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.url="*/mgmt/tm/util/bash*" Web.http_method="POST" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388_filter`

[ESCU - Hosts receiving high volume of network traffic from email server - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1114.002", "T1114"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.
action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model.  Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.
action.escu.known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Hosts receiving high volume of network traffic from email server - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Collection and Staging"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Hosts receiving high volume of network traffic from email server - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1114.002", "T1114"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`

[ESCU - Large Volume of DNS ANY Queries - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1498", "T1498.002"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.
action.escu.how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
action.escu.known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.
action.escu.creation_date = 2017-09-20
action.escu.modification_date = 2017-09-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Large Volume of DNS ANY Queries - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["DNS Amplification Attacks"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Large Volume of DNS ANY Queries - Rule
action.correlationsearch.annotations = {"analytic_story": ["DNS Amplification Attacks"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1498", "T1498.002"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter`

[ESCU - Multiple Archive Files Http Post Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is designed to detect high frequency of archive files data exfiltration through HTTP POST method protocol. This are one of the common techniques used by APT or trojan spy after doing the data collection like screenshot, recording, sensitive data to the infected machines. The attacker may execute archiving command to the collected data, save it a temp folder with a hidden attribute then send it to its C2 through HTTP POST. Sometimes adversaries will rename the archive files or encode/encrypt to cover their tracks. This detection can detect a renamed archive files transfer to HTTP POST since it checks the request body header. Unfortunately this detection cannot support archive that was encrypted or encoded before doing the exfiltration.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is designed to detect high frequency of archive files data exfiltration through HTTP POST method protocol. This are one of the common techniques used by APT or trojan spy after doing the data collection like screenshot, recording, sensitive data to the infected machines. The attacker may execute archiving command to the collected data, save it a temp folder with a hidden attribute then send it to its C2 through HTTP POST. Sometimes adversaries will rename the archive files or encode/encrypt to cover their tracks. This detection can detect a renamed archive files transfer to HTTP POST since it checks the request body header. Unfortunately this detection cannot support archive that was encrypted or encoded before doing the exfiltration.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled in stream http configuration.
action.escu.known_false_positives = Normal archive transfer via HTTP protocol may trip this detection.
action.escu.creation_date = 2021-04-21
action.escu.modification_date = 2021-04-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Archive Files Http Post Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Exfiltration", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = A http post $http_method$ sending packet with possible archive bytes header 4form_data$ in uri path $uri_path$
action.risk.param._risk = [{"threat_object_field": "uri_path", "threat_object_type": "url string"}, {"risk_object_field": "form_data", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Multiple Archive Files Http Post Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is designed to detect high frequency of archive files data exfiltration through HTTP POST method protocol. This are one of the common techniques used by APT or trojan spy after doing the data collection like screenshot, recording, sensitive data to the infected machines. The attacker may execute archiving command to the collected data, save it a temp folder with a hidden attribute then send it to its C2 through HTTP POST. Sometimes adversaries will rename the archive files or encode/encrypt to cover their tracks. This detection can detect a renamed archive files transfer to HTTP POST since it checks the request body header. Unfortunately this detection cannot support archive that was encrypted or encoded before doing the exfiltration.
action.notable.param.rule_title = Multiple Archive Files Http Post Traffic
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_method=POST |eval archive_hdr1=substr(form_data,1,2) | eval archive_hdr2 = substr(form_data,1,4) |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 |where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `multiple_archive_files_http_post_traffic_filter`

[ESCU - Ngrok Reverse Proxy on Network - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the 4 most common Ngrok used domains based on DNS queries under the Network Resolution datamodel. It's possible these domains may be ran against the Web datamodel or ran with a direct query across network/proxy traffic. The sign of someone using Ngrok is not malicious, however, more recenctly it has become an adversary tool.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1572", "T1090", "T1102"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = The following analytic identifies the 4 most common Ngrok used domains based on DNS queries under the Network Resolution datamodel. It's possible these domains may be ran against the Web datamodel or ran with a direct query across network/proxy traffic. The sign of someone using Ngrok is not malicious, however, more recenctly it has become an adversary tool.
action.escu.how_to_implement = The Network Resolution Datamodel will need to have data mapped to it regarding DNS queries. Modify query as needed to use another source.
action.escu.known_false_positives = False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed.
action.escu.creation_date = 2022-11-16
action.escu.modification_date = 2022-11-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ngrok Reverse Proxy on Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Reverse Network Proxy", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = An endpoint, $src$, is beaconing out to the reverse proxy service of Ngrok.
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 50}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ngrok Reverse Proxy on Network - Rule
action.correlationsearch.annotations = {"analytic_story": ["Reverse Network Proxy", "CISA AA22-320A"], "cis20": ["CIS 13"], "confidence": 100, "impact": 50, "mitre_attack": ["T1572", "T1090", "T1102"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Network_Resolution where DNS.query IN ("*.ngrok.com","*.ngrok.io", "ngrok.*.tunnel.com", "korgn.*.lennut.com") by DNS.src DNS.query DNS.answer |  `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ngrok_reverse_proxy_on_network_filter`

[ESCU - Plain HTTP POST Exfiltrated Data - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect potential plain HTTP POST method data exfiltration. This network traffic is commonly used by trickbot, trojanspy, keylogger or APT adversary where arguments or commands are sent in plain text to the remote C2 server using HTTP POST method as part of data exfiltration.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect potential plain HTTP POST method data exfiltration. This network traffic is commonly used by trickbot, trojanspy, keylogger or APT adversary where arguments or commands are sent in plain text to the remote C2 server using HTTP POST method as part of data exfiltration.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled.
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-04-22
action.escu.modification_date = 2021-04-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - Plain HTTP POST Exfiltrated Data - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Data Exfiltration", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = A http post $http_method$ sending packet with plain text of information $form_data$ in uri path $uri_path$
action.risk.param._risk = [{"threat_object_field": "uri_path", "threat_object_type": "url string"}, {"risk_object_field": "form_data", "risk_object_type": "other", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Plain HTTP POST Exfiltrated Data - Rule
action.correlationsearch.annotations = {"analytic_story": ["Data Exfiltration", "Command And Control"], "cis20": ["CIS 13"], "confidence": 90, "impact": 70, "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is to detect potential plain HTTP POST method data exfiltration. This network traffic is commonly used by trickbot, trojanspy, keylogger or APT adversary where arguments or commands are sent in plain text to the remote C2 server using HTTP POST method as part of data exfiltration.
action.notable.param.rule_title = Plain HTTP POST Exfiltrated Data
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `plain_http_post_exfiltrated_data_filter`

[ESCU - Prohibited Network Traffic Allowed - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device.
action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Prohibited Network Traffic Allowed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1048"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device.
action.notable.param.rule_title = Prohibited Network Traffic Allowed
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter`

[ESCU - Protocol or Port Mismatch - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected.
action.escu.how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports.
action.escu.known_false_positives = None identified
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Protocol or Port Mismatch - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Protocol or Port Mismatch - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Command And Control"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter`

[ESCU - Protocols passing authentication in cleartext - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances.
action.escu.how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. For more accurate result it's better to limit destination to organization private and public IP range, like All_Traffic.dest IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22)
action.escu.known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare.
action.escu.creation_date = 2021-08-19
action.escu.modification_date = 2021-08-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - Protocols passing authentication in cleartext - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Use of Cleartext Protocols"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Protocols passing authentication in cleartext - Rule
action.correlationsearch.annotations = {"analytic_story": ["Use of Cleartext Protocols"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances.
action.notable.param.rule_title = Protocols passing authentication in cleartext
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action!=blocked AND All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter`

[ESCU - Remote Desktop Network Bruteforce - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic.
action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model.
action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware"]
action.risk = 1
action.risk.param._risk_message = $dest$ may be the target of an RDP Bruteforce
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Desktop Network Bruteforce - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic.
action.notable.param.rule_title = Remote Desktop Network Bruteforce
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter`

[ESCU - Remote Desktop Network Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network.
action.escu.how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework.  This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.
action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network.
action.escu.creation_date = 2020-07-07
action.escu.modification_date = 2020-07-07
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Remote Desktop Network Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Active Directory Lateral Movement"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source AND all_Traffic.action="allowed" by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` 

[ESCU - SMB Traffic Spike - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects spikes in the number of Server Message Block (SMB) traffic connections. SMB is a network protocol used for sharing files, printers, and other resources between computers. This detection is made by a Splunk query that looks for SMB traffic connections on ports 139 and 445, as well as connections using the SMB application. The query calculates the average and standard deviation of the number of SMB connections over the past 70 minutes, and identifies any sources that exceed two standard deviations from the average. This helps to filter out false positives caused by normal fluctuations in SMB traffic. This detection is important because it identifies potential SMB-based attacks, such as ransomware or data theft, which often involve a large number of SMB connections. This  suggests that an attacker is attempting to exfiltrate data or spread malware within the network. Next steps include investigating the source of the traffic and determining if it is malicious. This can involve reviewing network logs, capturing and analyzing any relevant network packets, and correlating with other security events to identify the attack source and mitigate the risk.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = The following analytic detects spikes in the number of Server Message Block (SMB) traffic connections. SMB is a network protocol used for sharing files, printers, and other resources between computers. This detection is made by a Splunk query that looks for SMB traffic connections on ports 139 and 445, as well as connections using the SMB application. The query calculates the average and standard deviation of the number of SMB connections over the past 70 minutes, and identifies any sources that exceed two standard deviations from the average. This helps to filter out false positives caused by normal fluctuations in SMB traffic. This detection is important because it identifies potential SMB-based attacks, such as ransomware or data theft, which often involve a large number of SMB connections. This  suggests that an attacker is attempting to exfiltrate data or spread malware within the network. Next steps include investigating the source of the traffic and determining if it is malicious. This can involve reviewing network logs, capturing and analyzing any relevant network packets, and correlating with other security events to identify the attack source and mitigate the risk.
action.escu.how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model.
action.escu.known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger.
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SMB Traffic Spike - Rule
action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` 

[ESCU - SMB Traffic Spike - MLTK - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.
action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Traffic data model. In addition, the latest version of Machine Learning Toolkit (MLTK) must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\
This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \
1. **Label:** Number of events, **Field:** count\
Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
action.escu.known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
action.escu.confidence = high
action.escu.full_search_name = ESCU - SMB Traffic Spike - MLTK - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SMB Traffic Spike - MLTK - Rule
action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` 

[ESCU - Splunk Identified SSL TLS Certificates - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic uses tags of SSL, TLS and certificate to identify the usage of the Splunk default certificates being utilized in the environment. Recommended guidance is to utilize valid TLS certificates which documentation may be found in Splunk Docs - https://docs.splunk.com/Documentation/Splunk/8.2.6/Security/AboutsecuringyourSplunkconfigurationwithSSL.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1040"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic uses tags of SSL, TLS and certificate to identify the usage of the Splunk default certificates being utilized in the environment. Recommended guidance is to utilize valid TLS certificates which documentation may be found in Splunk Docs - https://docs.splunk.com/Documentation/Splunk/8.2.6/Security/AboutsecuringyourSplunkconfigurationwithSSL.
action.escu.how_to_implement = Ingestion of SSL/TLS data is needed and to be tagged properly as ssl, tls or certificate. This data may come from a proxy, zeek, or Splunk Streams. Splunk SOAR customers can find a SOAR workbook that walks an analyst through the process of running these hunting searches in the references list of this detection. In order to use this workbook, a user will need to run a curl command to post the file to their SOAR instance such as "curl -u username:password https://soar.instance.name/rest/rest/workbook_template -d @splunk_psa_0622.json". A user should then create an empty container or case, attach the workbook, and begin working through the tasks.
action.escu.known_false_positives = False positives will not be present as it is meant to assist with identifying default certificates being utilized.
action.escu.creation_date = 2022-05-25
action.escu.modification_date = 2022-05-25
action.escu.confidence = high
action.escu.full_search_name = ESCU - Splunk Identified SSL TLS Certificates - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Splunk Vulnerabilities"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Splunk Identified SSL TLS Certificates - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Vulnerabilities"], "cis20": ["CIS 13"], "confidence": 70, "cve": ["CVE-2022-32151", "CVE-2022-32152"], "impact": 60, "mitre_attack": ["T1040"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = tag IN (ssl, tls, certificate) ssl_issuer_common_name=*splunk* | stats values(src) AS "Host(s) with Default Cert" count by ssl_issuer ssl_subject_common_name ssl_subject_organization ssl_subject host sourcetype | `splunk_identified_ssl_tls_certificates_filter`

[ESCU - SSL Certificates with Punycode - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes the Certificates Datamodel to look for punycode domains, starting with xn--, found in the SSL issuer email domain. The presence of punycode here does not equate to evil, therefore we need to decode the punycode to determine what it translates to. Remove the CyberChef recipe as needed and decode manually. Note that this is not the exact location of the malicious punycode to trip CVE-2022-3602, but a method to at least identify fuzzing occurring on these email paths. What does evil look like? it will start with
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1573"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes the Certificates Datamodel to look for punycode domains, starting with xn--, found in the SSL issuer email domain. The presence of punycode here does not equate to evil, therefore we need to decode the punycode to determine what it translates to. Remove the CyberChef recipe as needed and decode manually. Note that this is not the exact location of the malicious punycode to trip CVE-2022-3602, but a method to at least identify fuzzing occurring on these email paths. What does evil look like? it will start with
action.escu.how_to_implement = Ensure data is properly being ingested into the Certificates datamodel. If decoding the of interest, the CyberChef app is needed https://splunkbase.splunk.com/app/5348. If decoding is not needed, remove the cyberchef lines.
action.escu.known_false_positives = False positives may be present if the organization works with international businesses. Filter as needed.
action.escu.creation_date = 2022-11-01
action.escu.modification_date = 2022-11-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - SSL Certificates with Punycode - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["OpenSSL CVE-2022-3602"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SSL Certificates with Punycode - Rule
action.correlationsearch.annotations = {"analytic_story": ["OpenSSL CVE-2022-3602"], "cis20": ["CIS 13"], "confidence": 30, "impact": 50, "mitre_attack": ["T1573"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Certificates.All_Certificates by All_Certificates.SSL.ssl_issuer_email_domain All_Certificates.SSL.ssl_issuer All_Certificates.SSL.ssl_subject_email All_Certificates.SSL.dest All_Certificates.SSL.src All_Certificates.SSL.sourcetype All_Certificates.SSL.ssl_subject_email_domain | `drop_dm_object_name("All_Certificates.SSL")` | eval punycode=if(like(ssl_issuer_email_domain,"%xn--%"),1,0) | where punycode=1 | cyberchef infield="ssl_issuer_email_domain" outfield="convertedPuny" jsonrecipe="[{"op":"From Punycode","args":[true]}]" | table ssl_issuer_email_domain convertedPuny ssl_issuer ssl_subject_email dest src sourcetype ssl_subject_email_domain | `ssl_certificates_with_punycode_filter`

[ESCU - TOR Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic looks for allowed network traffic to The Onion Router(TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. Detecting Tor traffic is paramount for upholding network security and mitigating potential threats. Tor's capacity to provide users with anonymity has been exploited by cybercriminals for activities like hacking, data breaches, and illicit content dissemination. Additionally, organizations must monitor Tor usage within their networks to ensure compliance with policies and regulations, as it can bypass conventional monitoring and filtering measures. Lastly, the ability to identify Tor traffic empowers security teams to promptly investigate and address potential security incidents, fortifying the protection of sensitive data and preserving the integrity of the network environment.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1090", "T1090.003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = The following analytic looks for allowed network traffic to The Onion Router(TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. Detecting Tor traffic is paramount for upholding network security and mitigating potential threats. Tor's capacity to provide users with anonymity has been exploited by cybercriminals for activities like hacking, data breaches, and illicit content dissemination. Additionally, organizations must monitor Tor usage within their networks to ensure compliance with policies and regulations, as it can bypass conventional monitoring and filtering measures. Lastly, the ability to identify Tor traffic empowers security teams to promptly investigate and address potential security incidents, fortifying the protection of sensitive data and preserving the integrity of the network environment.
action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from Next Generation Firewalls like Palo Alto Networks Firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model to be populated.
action.escu.known_false_positives = None at this time
action.escu.creation_date = 2023-09-20
action.escu.modification_date = 2023-09-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - TOR Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "NOBELIUM Group", "Command And Control"]
action.risk = 1
action.risk.param._risk_message = Suspicious network traffic allowed using TOR has been detected from $src_ip$ to $dest_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 80}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - TOR Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "NOBELIUM Group", "Command And Control"], "cis20": ["CIS 13"], "confidence": 80, "impact": 100, "mitre_attack": ["T1090", "T1090.003"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic looks for allowed network traffic to The Onion Router(TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. Detecting Tor traffic is paramount for upholding network security and mitigating potential threats. Tor's capacity to provide users with anonymity has been exploited by cybercriminals for activities like hacking, data breaches, and illicit content dissemination. Additionally, organizations must monitor Tor usage within their networks to ensure compliance with policies and regulations, as it can bypass conventional monitoring and filtering measures. Lastly, the ability to identify Tor traffic empowers security teams to promptly investigate and address potential security incidents, fortifying the protection of sensitive data and preserving the integrity of the network environment.
action.notable.param.rule_title = TOR Traffic
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter`

[ESCU - Unusually Long Content-Type Length - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for unusually long strings in the Content-Type http header that the client sends the server.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for unusually long strings in the Content-Type http header that the client sends the server.
action.escu.how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field.
action.escu.known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters.
action.escu.creation_date = 2017-10-13
action.escu.modification_date = 2017-10-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unusually Long Content-Type Length - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Apache Struts Vulnerability"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unusually Long Content-Type Length - Rule
action.correlationsearch.annotations = {"analytic_story": ["Apache Struts Vulnerability"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter`

[ESCU - Windows AD Replication Service Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for evidence of Active Directory replication traffic [MS-DRSR] from unexpected sources. This traffic is often seen exclusively between Domain Controllers for AD database replication. Any detections from non-domain controller source to a domain controller may indicate the usage of DCSync or DCShadow credential dumping techniques.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1003", "T1003.006", "T1207"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for evidence of Active Directory replication traffic [MS-DRSR] from unexpected sources. This traffic is often seen exclusively between Domain Controllers for AD database replication. Any detections from non-domain controller source to a domain controller may indicate the usage of DCSync or DCShadow credential dumping techniques.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting application aware firewall or proxy logs into the Network Datamodel. Categorize all known domain controller Assets servers with an appropriate category for filtering.
action.escu.known_false_positives = New domain controllers or certian scripts run by administrators.
action.escu.creation_date = 2022-11-26
action.escu.modification_date = 2022-11-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Replication Service Traffic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = Active Directory Replication Traffic from Unknown Source - $src$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Replication Service Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 13"], "confidence": 100, "impact": 100, "mitre_attack": ["T1003", "T1003.006", "T1207"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for evidence of Active Directory replication traffic [MS-DRSR] from unexpected sources. This traffic is often seen exclusively between Domain Controllers for AD database replication. Any detections from non-domain controller source to a domain controller may indicate the usage of DCSync or DCShadow credential dumping techniques.
action.notable.param.rule_title = Windows AD Replication Service Traffic
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count values(All_Traffic.transport) as transport values(All_Traffic.user) as user values(All_Traffic.src_category) as src_category values(All_Traffic.dest_category) as dest_category min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app IN ("ms-dc-replication","*drsr*","ad drs") by All_Traffic.src All_Traffic.dest All_Traffic.app | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `windows_ad_replication_service_traffic_filter`

[ESCU - Windows AD Rogue Domain Controller Network Activity - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection is looking at zeek wiredata for specific replication RPC calls being performed from a device which is not a domain controller. If you would like to capture these RPC calls using Splunk Stream, please vote for my idea here https://ideas.splunk.com/ideas/APPSID-I-619 ;)
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This detection is looking at zeek wiredata for specific replication RPC calls being performed from a device which is not a domain controller. If you would like to capture these RPC calls using Splunk Stream, please vote for my idea here https://ideas.splunk.com/ideas/APPSID-I-619 ;)
action.escu.how_to_implement = Run zeek on domain controllers to capture the DCE RPC calls, ensure the domain controller categories are defined in Assets and Identities.
action.escu.known_false_positives = None.
action.escu.creation_date = 2022-09-08
action.escu.modification_date = 2022-09-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows AD Rogue Domain Controller Network Activity - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Sneaky Active Directory Persistence Tricks"]
action.risk = 1
action.risk.param._risk_message = Rogue DC Activity Detected from $src_category$ device $src$ to $dest$ ($dest_category$)
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows AD Rogue Domain Controller Network Activity - Rule
action.correlationsearch.annotations = {"analytic_story": ["Sneaky Active Directory Persistence Tricks"], "cis20": ["CIS 13"], "confidence": 100, "impact": 100, "mitre_attack": ["T1207"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This detection is looking at zeek wiredata for specific replication RPC calls being performed from a device which is not a domain controller. If you would like to capture these RPC calls using Splunk Stream, please vote for my idea here https://ideas.splunk.com/ideas/APPSID-I-619 ;)
action.notable.param.rule_title = Windows AD Rogue Domain Controller Network Activity
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `zeek_rpc` DrsReplicaAdd OR DRSGetNCChanges | where NOT (dest_category="Domain Controller") OR NOT (src_category="Domain Controller") | fillnull value="Unknown" src_category, dest_category | table _time endpoint operation src src_category dest dest_category | `windows_ad_rogue_domain_controller_network_activity_filter`

[ESCU - Zeek x509 Certificate with Punycode - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes the Zeek x509 log. Modify the zeek_x509 macro with your index and sourcetype as needed. You will need to ensure the full x509 is logged as the potentially malicious punycode is nested under subject alternative names. In this particular analytic, it will identify punycode within the subject alternative name email and other fields. Note, that OtherFields is meant to be BOOL (true,false), therefore we may never see xn-- in that field. Upon identifying punycode, manually copy and paste, or add CyberChef recipe to query, and decode the punycode manually.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1573"], "nist": ["DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes the Zeek x509 log. Modify the zeek_x509 macro with your index and sourcetype as needed. You will need to ensure the full x509 is logged as the potentially malicious punycode is nested under subject alternative names. In this particular analytic, it will identify punycode within the subject alternative name email and other fields. Note, that OtherFields is meant to be BOOL (true,false), therefore we may never see xn-- in that field. Upon identifying punycode, manually copy and paste, or add CyberChef recipe to query, and decode the punycode manually.
action.escu.how_to_implement = The following analytic requires x509 certificate data to be logged entirely. In particular, for CVE-2022-3602, the punycode will be within the leaf certificate. The analytic may be modified to look for all xn--, or utilize a network IDS/monitoring tool like Zeek or Suricata to drill down into cert captured. Note for Suricata, the certificate is base64 encoded and will need to be decoded to capture the punycode (punycode will need to be decoded after).
action.escu.known_false_positives = False positives may be present if the organization works with international businesses. Filter as needed.
action.escu.creation_date = 2022-11-03
action.escu.modification_date = 2022-11-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Zeek x509 Certificate with Punycode - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["OpenSSL CVE-2022-3602"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Zeek x509 Certificate with Punycode - Rule
action.correlationsearch.annotations = {"analytic_story": ["OpenSSL CVE-2022-3602"], "cis20": ["CIS 13"], "confidence": 30, "impact": 50, "mitre_attack": ["T1573"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `zeek_x509` | rex field=san.email{} "\@(?<domain_detected>xn--.*)" | rex field=san.other_fields{} "\@(?<domain_detected>xn--.*)" | stats values(domain_detected) by  basic_constraints.ca source host | `zeek_x509_certificate_with_punycode_filter`

[ESCU - Adobe ColdFusion Access Control Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential exploitation attempts against Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. These vulnerabilities pertain to an access control bypass and an arbitrary file read due to deserialization, respectively. By monitoring for requests to specific ColdFusion Administrator endpoints, especially those with an unexpected additional forward slash, the analytic identifies attempts to bypass access controls. Such behavior is crucial for a Security Operations Center (SOC) to identify, as exploitation can grant unauthorized access to ColdFusion administration endpoints, potentially leading to information leakage, brute force attacks, or further exploitation of other vulnerabilities. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the ColdFusion environment, potentially leading to data theft or other malicious activities. SOCs must be vigilant in monitoring for these patterns, ensuring timely detection and response to such threats, thus safeguarding the integrity and security of their ColdFusion deployments.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects potential exploitation attempts against Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. These vulnerabilities pertain to an access control bypass and an arbitrary file read due to deserialization, respectively. By monitoring for requests to specific ColdFusion Administrator endpoints, especially those with an unexpected additional forward slash, the analytic identifies attempts to bypass access controls. Such behavior is crucial for a Security Operations Center (SOC) to identify, as exploitation can grant unauthorized access to ColdFusion administration endpoints, potentially leading to information leakage, brute force attacks, or further exploitation of other vulnerabilities. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the ColdFusion environment, potentially leading to data theft or other malicious activities. SOCs must be vigilant in monitoring for these patterns, ensuring timely detection and response to such threats, thus safeguarding the integrity and security of their ColdFusion deployments.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
action.escu.known_false_positives = This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
action.escu.creation_date = 2023-08-23
action.escu.modification_date = 2023-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Adobe ColdFusion Access Control Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360"]
action.risk = 1
action.risk.param._risk_message = Possible exploitation of CVE-2023-29298 against $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Adobe ColdFusion Access Control Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2023-29298"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects potential exploitation attempts against Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. These vulnerabilities pertain to an access control bypass and an arbitrary file read due to deserialization, respectively. By monitoring for requests to specific ColdFusion Administrator endpoints, especially those with an unexpected additional forward slash, the analytic identifies attempts to bypass access controls. Such behavior is crucial for a Security Operations Center (SOC) to identify, as exploitation can grant unauthorized access to ColdFusion administration endpoints, potentially leading to information leakage, brute force attacks, or further exploitation of other vulnerabilities. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the ColdFusion environment, potentially leading to data theft or other malicious activities. SOCs must be vigilant in monitoring for these patterns, ensuring timely detection and response to such threats, thus safeguarding the integrity and security of their ColdFusion deployments.
action.notable.param.rule_title = Adobe ColdFusion Access Control Bypass
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("//restplay*", "//CFIDE/restplay*", "//CFIDE/administrator*", "//CFIDE/adminapi*", "//CFIDE/main*", "//CFIDE/componentutils*", "//CFIDE/wizards*", "//CFIDE/servermanager*","/restplay*", "/CFIDE/restplay*", "/CFIDE/administrator*", "/CFIDE/adminapi*", "/CFIDE/main*", "/CFIDE/componentutils*", "/CFIDE/wizards*", "/CFIDE/servermanager*") Web.status=200 by Web.http_user_agent, Web.status, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adobe_coldfusion_access_control_bypass_filter`

[ESCU - Adobe ColdFusion Unauthenticated Arbitrary File Read - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential exploitation of the critical Adobe ColdFusion vulnerability, CVE-2023-26360. This flaw, rooted in the deserialization of untrusted data, enables Unauthenticated Arbitrary File Read. Exploitation often targets specific ColdFusion paths, especially related to CKEditor's file manager. \
Our analytic pinpoints exploitation by monitoring web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path. This focus helps differentiate malicious activity from standard ColdFusion traffic. For SOCs, detecting such attempts is vital given the vulnerability's CVSS score of 9.8, signaling its severity. Successful exploitation can lead to unauthorized data access, further attacks, or severe operational disruptions. \
If a true positive arises, it indicates an active breach attempt, potentially causing data theft, operational disruption, or reputational damage. In essence, this analytic provides a targeted approach to identify attempts exploiting a high-risk ColdFusion vulnerability. While false positives may occur from legitimate accesses, any alerts should be treated as high-priority, warranting immediate investigation to ensure security.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects potential exploitation of the critical Adobe ColdFusion vulnerability, CVE-2023-26360. This flaw, rooted in the deserialization of untrusted data, enables Unauthenticated Arbitrary File Read. Exploitation often targets specific ColdFusion paths, especially related to CKEditor's file manager. \
Our analytic pinpoints exploitation by monitoring web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path. This focus helps differentiate malicious activity from standard ColdFusion traffic. For SOCs, detecting such attempts is vital given the vulnerability's CVSS score of 9.8, signaling its severity. Successful exploitation can lead to unauthorized data access, further attacks, or severe operational disruptions. \
If a true positive arises, it indicates an active breach attempt, potentially causing data theft, operational disruption, or reputational damage. In essence, this analytic provides a targeted approach to identify attempts exploiting a high-risk ColdFusion vulnerability. While false positives may occur from legitimate accesses, any alerts should be treated as high-priority, warranting immediate investigation to ensure security.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
action.escu.known_false_positives = In the wild, we have observed three different types of attempts that could potentially trigger false positives if the HTTP status code is not in the query. Please check this github gist for the specific URIs : https://gist.github.com/patel-bhavin/d10830f3f375a2397233f6a4fe38d5c9 . These could be legitimate requests depending on the context of your organization. Therefore, it is recommended to modify the analytic as needed to suit your specific environment.
action.escu.creation_date = 2023-08-23
action.escu.modification_date = 2023-08-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Adobe ColdFusion Unauthenticated Arbitrary File Read - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360"]
action.risk = 1
action.risk.param._risk_message = Possible exploitation of CVE-2023-26360 against $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Adobe ColdFusion Unauthenticated Arbitrary File Read - Rule
action.correlationsearch.annotations = {"analytic_story": ["Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2023-26360"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects potential exploitation of the critical Adobe ColdFusion vulnerability, CVE-2023-26360. This flaw, rooted in the deserialization of untrusted data, enables Unauthenticated Arbitrary File Read. Exploitation often targets specific ColdFusion paths, especially related to CKEditor's file manager. \
Our analytic pinpoints exploitation by monitoring web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path. This focus helps differentiate malicious activity from standard ColdFusion traffic. For SOCs, detecting such attempts is vital given the vulnerability's CVSS score of 9.8, signaling its severity. Successful exploitation can lead to unauthorized data access, further attacks, or severe operational disruptions. \
If a true positive arises, it indicates an active breach attempt, potentially causing data theft, operational disruption, or reputational damage. In essence, this analytic provides a targeted approach to identify attempts exploiting a high-risk ColdFusion vulnerability. While false positives may occur from legitimate accesses, any alerts should be treated as high-priority, warranting immediate investigation to ensure security.
action.notable.param.rule_title = Adobe ColdFusion Unauthenticated Arbitrary File Read
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/cf_scripts/scripts/ajax/ckeditor/*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adobe_coldfusion_unauthenticated_arbitrary_file_read_filter`

[ESCU - Cisco IOS XE Implant Access - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies potential exploitation of a previously unknown vulnerability in the Web User Interface (Web UI) feature of Cisco IOS XE software (CVE-2023-20198). Successful exploitation allows an attacker to create an account on the affected device with privilege level 15 access, granting them full control of the compromised device. The detection is based on the observation of suspicious account creation and subsequent actions, including the deployment of an implant consisting of a configuration file. The implant is saved under the file path //usr//binos//conf//nginx-conf//cisco_service.conf and is not persistent, meaning a device reboot will remove it, but the newly created local user accounts remain active even after system reboots. The new user accounts have level 15 privileges, meaning they have full administrator access to the device. This privileged access to the devices and subsequent creation of new users is tracked as CVE-2023-20198.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies potential exploitation of a previously unknown vulnerability in the Web User Interface (Web UI) feature of Cisco IOS XE software (CVE-2023-20198). Successful exploitation allows an attacker to create an account on the affected device with privilege level 15 access, granting them full control of the compromised device. The detection is based on the observation of suspicious account creation and subsequent actions, including the deployment of an implant consisting of a configuration file. The implant is saved under the file path //usr//binos//conf//nginx-conf//cisco_service.conf and is not persistent, meaning a device reboot will remove it, but the newly created local user accounts remain active even after system reboots. The new user accounts have level 15 privileges, meaning they have full administrator access to the device. This privileged access to the devices and subsequent creation of new users is tracked as CVE-2023-20198.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
action.escu.known_false_positives = False positives may be present, restrict to Cisco IOS XE devices or perimeter appliances. Modify the analytic as needed based on hunting for successful exploitation of CVE-2023-20198.
action.escu.creation_date = 2023-10-17
action.escu.modification_date = 2023-10-17
action.escu.confidence = high
action.escu.full_search_name = ESCU - Cisco IOS XE Implant Access - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Cisco IOS XE Software Web Management User Interface vulnerability"]
action.risk = 1
action.risk.param._risk_message = Possible exploitation of CVE-2023-20198 against $dest$ by $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Cisco IOS XE Implant Access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cisco IOS XE Software Web Management User Interface vulnerability"], "cis20": ["CIS 13"], "confidence": 90, "cve": ["CVE-2023-20198"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies potential exploitation of a previously unknown vulnerability in the Web User Interface (Web UI) feature of Cisco IOS XE software (CVE-2023-20198). Successful exploitation allows an attacker to create an account on the affected device with privilege level 15 access, granting them full control of the compromised device. The detection is based on the observation of suspicious account creation and subsequent actions, including the deployment of an implant consisting of a configuration file. The implant is saved under the file path //usr//binos//conf//nginx-conf//cisco_service.conf and is not persistent, meaning a device reboot will remove it, but the newly created local user accounts remain active even after system reboots. The new user accounts have level 15 privileges, meaning they have full administrator access to the device. This privileged access to the devices and subsequent creation of new users is tracked as CVE-2023-20198.
action.notable.param.rule_title = Cisco IOS XE Implant Access
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/webui/logoutconfirm.html?logon_hash=*") Web.http_method=POST Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cisco_ios_xe_implant_access_filter`

[ESCU - Citrix ADC and Gateway Unauthorized Data Disclosure - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects attempts to exploit the Citrix Bleed vulnerability, which can lead to the leaking of session tokens. The vulnerability, identified as CVE-2023-4966, pertains to sensitive information disclosure in NetScaler ADC and NetScaler Gateway when set up as various server configurations. The analytic specifically searches for HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration URL endpoint. By parsing web traffic and filtering based on the aforementioned criteria along with specific user agent details, HTTP method, source and destination IPs, and the sourcetype, the analytic aims to identify potentially malicious requests that fit the profile of this exploit. \
This behavior is essential for a Security Operations Center (SOC) to identify because if successfully exploited, attackers can gain unauthorized access, leading to a potential breach or further malicious activities within the organization's network. As the Citrix Bleed vulnerability can disclose session tokens, a successful exploit can allow attackers to impersonate legitimate users, bypassing authentication mechanisms and accessing sensitive data or systems. \
If a true positive is confirmed, it implies that an attacker is actively exploiting the vulnerability within the organization's environment. This could lead to severe consequences, including unauthorized data access, further propagation within the network, and potential disruptions or exfiltration of critical information. \
Upon flagging such activity, it's crucial for analysts to swiftly validate the alert, assess the nature and extent of the exposure, and implement necessary measures to mitigate the threat. Reviewing the details such as user agent, source, and destination IP can help in understanding the context and intent of the attack. While it's imperative to patch vulnerable systems to prevent this exploitation, early detection through this analytic provides a valuable layer of defense, enabling timely response to thwart potential breaches.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects attempts to exploit the Citrix Bleed vulnerability, which can lead to the leaking of session tokens. The vulnerability, identified as CVE-2023-4966, pertains to sensitive information disclosure in NetScaler ADC and NetScaler Gateway when set up as various server configurations. The analytic specifically searches for HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration URL endpoint. By parsing web traffic and filtering based on the aforementioned criteria along with specific user agent details, HTTP method, source and destination IPs, and the sourcetype, the analytic aims to identify potentially malicious requests that fit the profile of this exploit. \
This behavior is essential for a Security Operations Center (SOC) to identify because if successfully exploited, attackers can gain unauthorized access, leading to a potential breach or further malicious activities within the organization's network. As the Citrix Bleed vulnerability can disclose session tokens, a successful exploit can allow attackers to impersonate legitimate users, bypassing authentication mechanisms and accessing sensitive data or systems. \
If a true positive is confirmed, it implies that an attacker is actively exploiting the vulnerability within the organization's environment. This could lead to severe consequences, including unauthorized data access, further propagation within the network, and potential disruptions or exfiltration of critical information. \
Upon flagging such activity, it's crucial for analysts to swiftly validate the alert, assess the nature and extent of the exposure, and implement necessary measures to mitigate the threat. Reviewing the details such as user agent, source, and destination IP can help in understanding the context and intent of the attack. While it's imperative to patch vulnerable systems to prevent this exploitation, early detection through this analytic provides a valuable layer of defense, enabling timely response to thwart potential breaches.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. We recommend hunting in the environment first to understand the scope of the issue and then deploying this detection to monitor for future exploitation attempts. Limit or restrict to Citrix devices only if possible.
action.escu.known_false_positives = False positives may be present based on organization use of Citrix ADC and Gateway. Filter, or restrict the analytic to Citrix devices only.
action.escu.creation_date = 2023-10-24
action.escu.modification_date = 2023-10-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Citrix ADC and Gateway Unauthorized Data Disclosure - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Citrix NetScaler ADC and NetScaler Gateway CVE-2023-4966"]
action.risk = 1
action.risk.param._risk_message = Possible exploitation of Citrix Bleed vulnerability against $dest$ fron $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 90}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 90}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Citrix ADC and Gateway Unauthorized Data Disclosure - Rule
action.correlationsearch.annotations = {"analytic_story": ["Citrix NetScaler ADC and NetScaler Gateway CVE-2023-4966"], "cis20": ["CIS 13"], "confidence": 90, "impact": 100, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects attempts to exploit the Citrix Bleed vulnerability, which can lead to the leaking of session tokens. The vulnerability, identified as CVE-2023-4966, pertains to sensitive information disclosure in NetScaler ADC and NetScaler Gateway when set up as various server configurations. The analytic specifically searches for HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration URL endpoint. By parsing web traffic and filtering based on the aforementioned criteria along with specific user agent details, HTTP method, source and destination IPs, and the sourcetype, the analytic aims to identify potentially malicious requests that fit the profile of this exploit. \
This behavior is essential for a Security Operations Center (SOC) to identify because if successfully exploited, attackers can gain unauthorized access, leading to a potential breach or further malicious activities within the organization's network. As the Citrix Bleed vulnerability can disclose session tokens, a successful exploit can allow attackers to impersonate legitimate users, bypassing authentication mechanisms and accessing sensitive data or systems. \
If a true positive is confirmed, it implies that an attacker is actively exploiting the vulnerability within the organization's environment. This could lead to severe consequences, including unauthorized data access, further propagation within the network, and potential disruptions or exfiltration of critical information. \
Upon flagging such activity, it's crucial for analysts to swiftly validate the alert, assess the nature and extent of the exposure, and implement necessary measures to mitigate the threat. Reviewing the details such as user agent, source, and destination IP can help in understanding the context and intent of the attack. While it's imperative to patch vulnerable systems to prevent this exploitation, early detection through this analytic provides a valuable layer of defense, enabling timely response to thwart potential breaches.
action.notable.param.rule_title = Citrix ADC and Gateway Unauthorized Data Disclosure
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/oauth/idp/.well-known/openid-configuration*")  Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `citrix_adc_and_gateway_unauthorized_data_disclosure_filter`

[ESCU - Citrix ADC Exploitation CVE-2023-3519 - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to assist in hunting for potential exploitation attempts against Citrix ADC in relation to CVE-2023-3519. This vulnerability, identified within Citrix ADC and NetScaler Gateway, appears to be linked with SAML processing components, with an overflow issue allowing for possible memory corruption. Preliminary findings indicate that for the exploit to be viable, SAML has to be enabled. The analytic targets POST requests to certain web endpoints which have been associated with the exploitation process. \
Given the specific nature of the vulnerability, upon deploying this analytic it is recommended to filter and narrow the focus towards your ADC assets to reduce potential noise and improve the signal of the analytic. Please note that the exploitation of this vulnerability has been reported in the wild, therefore monitoring for potential signs of exploitation should be considered high priority. \
The search query provided examines web data for POST requests made to specific URLs associated with the exploitation of this vulnerability. It aggregates and presents data to highlight potential exploitation attempts, taking into account elements like user agent, HTTP method, URL length, source, and destination. \
Please be aware that this analytic is based on current understanding of the vulnerability, and adjustments may be required as more information becomes available.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This analytic is designed to assist in hunting for potential exploitation attempts against Citrix ADC in relation to CVE-2023-3519. This vulnerability, identified within Citrix ADC and NetScaler Gateway, appears to be linked with SAML processing components, with an overflow issue allowing for possible memory corruption. Preliminary findings indicate that for the exploit to be viable, SAML has to be enabled. The analytic targets POST requests to certain web endpoints which have been associated with the exploitation process. \
Given the specific nature of the vulnerability, upon deploying this analytic it is recommended to filter and narrow the focus towards your ADC assets to reduce potential noise and improve the signal of the analytic. Please note that the exploitation of this vulnerability has been reported in the wild, therefore monitoring for potential signs of exploitation should be considered high priority. \
The search query provided examines web data for POST requests made to specific URLs associated with the exploitation of this vulnerability. It aggregates and presents data to highlight potential exploitation attempts, taking into account elements like user agent, HTTP method, URL length, source, and destination. \
Please be aware that this analytic is based on current understanding of the vulnerability, and adjustments may be required as more information becomes available.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
action.escu.known_false_positives = False positives may be present based on organization use of SAML utilities. Filter, or restrict the analytic to Citrix devices only.
action.escu.creation_date = 2023-07-21
action.escu.modification_date = 2023-07-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Citrix ADC Exploitation CVE-2023-3519 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Citrix Netscaler ADC CVE-2023-3519"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Citrix ADC Exploitation CVE-2023-3519 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Citrix Netscaler ADC CVE-2023-3519"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-3519"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/saml/login","/cgi/samlauth","*/saml/activelogin","/cgi/samlart?samlart=*","*/cgi/logout","/gwtest/formssso?event=start&target=*","/netscaler/ns_gui/vpn/*")  Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `citrix_adc_exploitation_cve_2023_3519_filter`

[ESCU - Citrix ShareFile Exploitation CVE-2023-24489 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects a potentially malicious file upload attempt to Documentum, an enterprise content management platform, via specific suspicious URLs and the HTTP POST method. This detection occurs through pattern recognition within the datamodel=Web, focusing on URL patterns that follow "/documentum/upload.aspx?parentid=", "/documentum/upload.aspx?filename=", "/documentum/upload.aspx?uploadId=*", combined with the HTTP POST method, indicative of a file upload attempt. \
This behavior is significant for a Security Operations Center (SOC) to identify, as it can signify a potential attack vector. Malicious actors might use this method to upload a harmful script or other exploitable content to Documentum, thereby establishing a foothold in the environment, spreading malware, or enabling further exploitation. \
The impact of this behavior, if a true positive, can be quite significant. An attacker could compromise the Documentum application, manipulate or steal sensitive content, and potentially gain unauthorized access to other system resources. An intrusion of this nature could disrupt business operations, result in data breaches, and even damage the organization's reputation. \
However, it's important to note that false positives may occur. For example, legitimate but uncommon file uploads might match these URL patterns. It's crucial to verify any alerts generated by this analytic to ensure accurate threat detection. This analytic provides critical insights into potential attack attempts and assists in maintaining the integrity and security of enterprise content management systems like Documentum.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects a potentially malicious file upload attempt to Documentum, an enterprise content management platform, via specific suspicious URLs and the HTTP POST method. This detection occurs through pattern recognition within the datamodel=Web, focusing on URL patterns that follow "/documentum/upload.aspx?parentid=", "/documentum/upload.aspx?filename=", "/documentum/upload.aspx?uploadId=*", combined with the HTTP POST method, indicative of a file upload attempt. \
This behavior is significant for a Security Operations Center (SOC) to identify, as it can signify a potential attack vector. Malicious actors might use this method to upload a harmful script or other exploitable content to Documentum, thereby establishing a foothold in the environment, spreading malware, or enabling further exploitation. \
The impact of this behavior, if a true positive, can be quite significant. An attacker could compromise the Documentum application, manipulate or steal sensitive content, and potentially gain unauthorized access to other system resources. An intrusion of this nature could disrupt business operations, result in data breaches, and even damage the organization's reputation. \
However, it's important to note that false positives may occur. For example, legitimate but uncommon file uploads might match these URL patterns. It's crucial to verify any alerts generated by this analytic to ensure accurate threat detection. This analytic provides critical insights into potential attack attempts and assists in maintaining the integrity and security of enterprise content management systems like Documentum.
action.escu.how_to_implement = Dependent upon the placement of the ShareFile application, ensure the latest Technology Add-On is eneabled. This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. The ShareFile application is IIS based, therefore ingesting IIS logs and reviewing for the same pattern would identify this activity, successful or not.
action.escu.known_false_positives = False positives may be present, filtering may be needed. Also, restricting to known web servers running IIS or ShareFile will change this from Hunting to TTP.
action.escu.creation_date = 2023-07-26
action.escu.modification_date = 2023-07-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Citrix ShareFile Exploitation CVE-2023-24489 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Citrix ShareFile RCE CVE-2023-24489"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Citrix ShareFile Exploitation CVE-2023-24489 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Citrix ShareFile RCE CVE-2023-24489"], "cis20": ["CIS 10"], "confidence": 50, "cve": ["CVE-2023-24489"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="/documentum/upload.aspx?*" AND Web.url IN ("*parentid=*","*filename=*","*uploadId=*") AND Web.url IN ("*unzip=*", "*raw=*") Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `citrix_sharefile_exploitation_cve_2023_24489_filter`

[ESCU - Confluence CVE-2023-22515 Trigger Vulnerability - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, targeting the /server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false* and /server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0& URLs. By analyzing web logs within the Splunk 'Web' Data Model, it filters for successful accesses (HTTP status 200) to these vulnerable endpoints. Such behavior is crucial for a SOC to monitor, as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. A true positive implies a possible unauthorized access or account creation with escalated privileges. Key details captured include user-agent, HTTP methods, URL length, and source and destination IPs. These insights aid SOCs in swiftly detecting and responding to threats, ensuring vulnerabilities are mitigated before substantial compromise.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, targeting the /server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false* and /server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0& URLs. By analyzing web logs within the Splunk 'Web' Data Model, it filters for successful accesses (HTTP status 200) to these vulnerable endpoints. Such behavior is crucial for a SOC to monitor, as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. A true positive implies a possible unauthorized access or account creation with escalated privileges. Key details captured include user-agent, HTTP methods, URL length, and source and destination IPs. These insights aid SOCs in swiftly detecting and responding to threats, ensuring vulnerabilities are mitigated before substantial compromise.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. Tested with Suricata and nginx:plus:kv.
action.escu.known_false_positives = False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to Confluence servers.
action.escu.creation_date = 2023-10-23
action.escu.modification_date = 2023-10-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Confluence CVE-2023-22515 Trigger Vulnerability - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server"]
action.risk = 1
action.risk.param._risk_message = Potential exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Confluence CVE-2023-22515 Trigger Vulnerability - Rule
action.correlationsearch.annotations = {"analytic_story": ["CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server"], "cis20": ["CIS 13"], "confidence": 80, "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, targeting the /server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false* and /server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0& URLs. By analyzing web logs within the Splunk 'Web' Data Model, it filters for successful accesses (HTTP status 200) to these vulnerable endpoints. Such behavior is crucial for a SOC to monitor, as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. A true positive implies a possible unauthorized access or account creation with escalated privileges. Key details captured include user-agent, HTTP methods, URL length, and source and destination IPs. These insights aid SOCs in swiftly detecting and responding to threats, ensuring vulnerabilities are mitigated before substantial compromise.
action.notable.param.rule_title = Confluence CVE-2023-22515 Trigger Vulnerability
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false*","*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0&*") Web.http_method=GET Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_cve_2023_22515_trigger_vulnerability_filter`

[ESCU - Confluence Data Center and Server Privilege Escalation - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, targeting the /setup/*.action* URL pattern. By analyzing web logs within the Splunk 'Web' Data Model, it filters for successful accesses (HTTP status 200) to these vulnerable endpoints. Such behavior is crucial for a SOC to monitor, as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. A true positive implies a possible unauthorized access or account creation with escalated privileges. Key details captured include user-agent, HTTP methods, URL length, and source and destination IPs. These insights aid SOCs in swiftly detecting and responding to threats, ensuring vulnerabilities are mitigated before substantial compromise.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, targeting the /setup/*.action* URL pattern. By analyzing web logs within the Splunk 'Web' Data Model, it filters for successful accesses (HTTP status 200) to these vulnerable endpoints. Such behavior is crucial for a SOC to monitor, as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. A true positive implies a possible unauthorized access or account creation with escalated privileges. Key details captured include user-agent, HTTP methods, URL length, and source and destination IPs. These insights aid SOCs in swiftly detecting and responding to threats, ensuring vulnerabilities are mitigated before substantial compromise.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
action.escu.known_false_positives = False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to confluence servers.
action.escu.creation_date = 2023-10-18
action.escu.modification_date = 2023-10-18
action.escu.confidence = high
action.escu.full_search_name = ESCU - Confluence Data Center and Server Privilege Escalation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server"]
action.risk = 1
action.risk.param._risk_message = Potential exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Confluence Data Center and Server Privilege Escalation - Rule
action.correlationsearch.annotations = {"analytic_story": ["CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server"], "cis20": ["CIS 13"], "confidence": 80, "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, targeting the /setup/*.action* URL pattern. By analyzing web logs within the Splunk 'Web' Data Model, it filters for successful accesses (HTTP status 200) to these vulnerable endpoints. Such behavior is crucial for a SOC to monitor, as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. A true positive implies a possible unauthorized access or account creation with escalated privileges. Key details captured include user-agent, HTTP methods, URL length, and source and destination IPs. These insights aid SOCs in swiftly detecting and responding to threats, ensuring vulnerabilities are mitigated before substantial compromise.
action.notable.param.rule_title = Confluence Data Center and Server Privilege Escalation
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/setup/setupadministrator.action*", "*/setup/finishsetup.action*") Web.status=200  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_data_center_and_server_privilege_escalation_filter`

[ESCU - Confluence Unauthenticated Remote Code Execution CVE-2022-26134 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic assists with identifying CVE-2022-26134 based exploitation utilizing the Web datamodel to cover network and CIM compliant web logs. The parameters were captured from live scanning and the POC provided by Rapid7. This analytic is written against multiple proof of concept codes released and seen in the wild (scanning). During triage, review any endpoint based logs for further activity including writing a jsp file to disk and commands/processes spawning running as root from the Confluence process.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1505", "T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic assists with identifying CVE-2022-26134 based exploitation utilizing the Web datamodel to cover network and CIM compliant web logs. The parameters were captured from live scanning and the POC provided by Rapid7. This analytic is written against multiple proof of concept codes released and seen in the wild (scanning). During triage, review any endpoint based logs for further activity including writing a jsp file to disk and commands/processes spawning running as root from the Confluence process.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx. In addition, network based logs or event data like PAN Threat.
action.escu.known_false_positives = Tune based on assets if possible, or restrict to known Confluence servers. Remove the ${ for a more broad query. To identify more exec, remove everything up to the last parameter (Runtime().exec) for a broad query.
action.escu.creation_date = 2022-06-03
action.escu.modification_date = 2022-06-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Confluence Unauthenticated Remote Code Execution CVE-2022-26134 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Atlassian Confluence Server and Data Center CVE-2022-26134"]
action.risk = 1
action.risk.param._risk_message = A URL was requested related to CVE-2022-26134, a unauthenticated remote code execution vulnerability,  on $dest$ by $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 100}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 100}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Confluence Unauthenticated Remote Code Execution CVE-2022-26134 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Atlassian Confluence Server and Data Center CVE-2022-26134"], "cis20": ["CIS 13"], "confidence": 100, "cve": ["CVE-2022-26134"], "impact": 100, "mitre_attack": ["T1505", "T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic assists with identifying CVE-2022-26134 based exploitation utilizing the Web datamodel to cover network and CIM compliant web logs. The parameters were captured from live scanning and the POC provided by Rapid7. This analytic is written against multiple proof of concept codes released and seen in the wild (scanning). During triage, review any endpoint based logs for further activity including writing a jsp file to disk and commands/processes spawning running as root from the Confluence process.
action.notable.param.rule_title = Confluence Unauthenticated Remote Code Execution CVE-2022-26134
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*${*", "*%2F%7B*") (Web.url="*org.apache.commons.io.IOUtils*" Web.url="*java.lang.Runtime@getRuntime().exec*") OR (Web.url="*java.lang.Runtime%40getRuntime%28%29.exec*") OR (Web.url="*getEngineByName*" AND Web.url="*nashorn*" AND Web.url="*ProcessBuilder*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_unauthenticated_remote_code_execution_cve_2022_26134_filter`

[ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1082", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity.
action.escu.how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model.
action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths.
action.escu.creation_date = 2017-09-23
action.escu.modification_date = 2017-09-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule
action.correlationsearch.annotations = {"analytic_story": ["JBoss Vulnerability", "SamSam Ransomware"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1082", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity.
action.notable.param.rule_title = Detect attackers scanning for vulnerable JBoss servers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`

[ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices
action.escu.how_to_implement = To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog.  As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it.  I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;).
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-08-02
action.escu.modification_date = 2020-08-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["F5 TMUI RCE CVE-2020-5902"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "other", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule
action.correlationsearch.annotations = {"analytic_story": ["F5 TMUI RCE CVE-2020-5902"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2020-5902"], "impact": 50, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices
action.notable.param.rule_title = Detect F5 TMUI RCE CVE-2020-5902
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter`

[ESCU - Detect malicious requests to exploit JBoss servers - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL.
action.escu.how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model
action.escu.known_false_positives = No known false positives for this detection.
action.escu.creation_date = 2017-09-23
action.escu.modification_date = 2017-09-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect malicious requests to exploit JBoss servers - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect malicious requests to exploit JBoss servers - Rule
action.correlationsearch.annotations = {"analytic_story": ["JBoss Vulnerability", "SamSam Ransomware"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL.
action.notable.param.rule_title = Detect malicious requests to exploit JBoss servers
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter`

[ESCU - Exploit Public Facing Application via Apache Commons Text - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies activity related to Text4Shell, or the critical vulnerability CVE-2022-42889 in Apache Commons Text Library. Apache Commons Text versions 1.5 through 1.9 are affected, but it has been patched in version 1.10. The analytic may need to be tuned for your environment before enabling as a TTP, or direct Notable. Apache Commons Text is a Java library described as a library focused on algorithms working on strings. We can see it as a general-purpose text manipulation toolkit. This vulnerability affects the StringSubstitutor interpolator class, which is included in the Commons Text library. A default interpolator allows for string lookups that can lead to Remote Code Execution. This is due to a logic flaw that makes the script, dns, and url lookup keys interpolated by default, as opposed to what it should be, according to the documentation of the StringLookupFactory class. Those keys allow an attacker to execute arbitrary code via lookups.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1505.003", "T1505", "T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies activity related to Text4Shell, or the critical vulnerability CVE-2022-42889 in Apache Commons Text Library. Apache Commons Text versions 1.5 through 1.9 are affected, but it has been patched in version 1.10. The analytic may need to be tuned for your environment before enabling as a TTP, or direct Notable. Apache Commons Text is a Java library described as a library focused on algorithms working on strings. We can see it as a general-purpose text manipulation toolkit. This vulnerability affects the StringSubstitutor interpolator class, which is included in the Commons Text library. A default interpolator allows for string lookups that can lead to Remote Code Execution. This is due to a logic flaw that makes the script, dns, and url lookup keys interpolated by default, as opposed to what it should be, according to the documentation of the StringLookupFactory class. Those keys allow an attacker to execute arbitrary code via lookups.
action.escu.how_to_implement = To implement, one must be collecting network traffic that is normalized in CIM and able to be queried via the Web datamodel. Or, take the chunks out needed and tie to a specific network source type to hunt in. Tune as needed, or remove the other_lookups statement.
action.escu.known_false_positives = False positives are present when the values are set to 1 for utf and lookup. It's possible to raise this to TTP (direct notable) if removal of other_lookups occur and Score is raised to 2 (down from 4).
action.escu.creation_date = 2023-01-23
action.escu.modification_date = 2023-01-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Exploit Public Facing Application via Apache Commons Text - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Text4Shell CVE-2022-42889"]
action.risk = 1
action.risk.param._risk_message = A URL was requested related to Text4Shell on $dest$ by $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Exploit Public Facing Application via Apache Commons Text - Rule
action.correlationsearch.annotations = {"analytic_story": ["Text4Shell CVE-2022-42889"], "cis20": ["CIS 13"], "confidence": 70, "cve": ["CVE-2022-42889"], "impact": 70, "mitre_attack": ["T1505.003", "T1505", "T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.http_method IN (POST, GET) by Web.src Web.status Web.uri_path Web.dest Web.http_method Web.uri_query Web.http_user_agent | `drop_dm_object_name("Web")` | eval utf=if(like(lower(uri_query),"%:utf-8:http%"),2,0) | eval lookup = if(like(lower(uri_query), "%url%") OR like(lower(uri_query), "%dns%") OR like(lower(uri_query), "%script%"),2,0) | eval other_lookups = if(like(lower(uri_query), "%env%") OR like(lower(uri_query), "%file%") OR like(lower(uri_query), "%getRuntime%") OR like(lower(uri_query), "%java%") OR like(lower(uri_query), "%localhost%") OR like(lower(uri_query), "%properties%") OR like(lower(uri_query), "%resource%") OR like(lower(uri_query), "%sys%") OR like(lower(uri_query), "%xml%") OR like(lower(uri_query), "%base%"),1,0) | addtotals fieldname=Score utf lookup other_lookups | fields Score, src, dest, status, uri_query, uri_path, http_method, http_user_agent firstTime lastTime | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where Score >= 4 | `exploit_public_facing_application_via_apache_commons_text_filter`

[ESCU - Exploit Public-Facing Fortinet FortiNAC CVE-2022-39952 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies a recent CVE-2022-39952 released publicly where the URI configWizard/keyUpload.jsp recieves a POST with the payload.zip, from there the POC script will schedule a cron to run the payload and contact the remote C2.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies a recent CVE-2022-39952 released publicly where the URI configWizard/keyUpload.jsp recieves a POST with the payload.zip, from there the POC script will schedule a cron to run the payload and contact the remote C2.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
action.escu.known_false_positives = False positives may be present. Modify the query as needed to POST, or add additional filtering (based on log source).
action.escu.creation_date = 2023-02-21
action.escu.modification_date = 2023-02-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - Exploit Public-Facing Fortinet FortiNAC CVE-2022-39952 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Fortinet FortiNAC CVE-2022-39952"]
action.risk = 1
action.risk.param._risk_message = Potential CVE-2022-39952 against a Fortinet NAC may be occurring against $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Exploit Public-Facing Fortinet FortiNAC CVE-2022-39952 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Fortinet FortiNAC CVE-2022-39952"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2022-39952"], "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies a recent CVE-2022-39952 released publicly where the URI configWizard/keyUpload.jsp recieves a POST with the payload.zip, from there the POC script will schedule a cron to run the payload and contact the remote C2.
action.notable.param.rule_title = Exploit Public-Facing Fortinet FortiNAC CVE-2022-39952
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*configWizard/keyUpload.jsp*") by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exploit_public_facing_fortinet_fortinac_cve_2022_39952_filter`

[ESCU - Fortinet Appliance Auth bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = CVE-2022-40684 is a Fortinet appliance auth bypass that is actively being exploited and a POC is released publicy. The POC adds a SSH key to the appliance. Note that the exploit can be used with any HTTP method (GET, POST, PUT, DELETE, etc). The REST API request failing is not an indication that an attacker was unsuccessful. Horizon3 was able to modify the admin SSH keys though a REST API request that reportedly failed. The collection /api/v2/ endpoints can be used to configure the system and modify the administrator user. Any logs found that meet the above conditions and also have a URL containing /api/v2/ should be cause for concern. Further investigation of any matching log entries can reveal any damage an attack has done. Additionally, an attacker may perform the following actions to further compromise a system Modify the admin SSH key to enable the attacker to login to the compromised system. \
Add new local users. \
Update networking configurations to reroute traffic. \
Download the system configuration. \
Initiate packet captures to capture other sensitive system information. Reference Horizon3.ai
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = CVE-2022-40684 is a Fortinet appliance auth bypass that is actively being exploited and a POC is released publicy. The POC adds a SSH key to the appliance. Note that the exploit can be used with any HTTP method (GET, POST, PUT, DELETE, etc). The REST API request failing is not an indication that an attacker was unsuccessful. Horizon3 was able to modify the admin SSH keys though a REST API request that reportedly failed. The collection /api/v2/ endpoints can be used to configure the system and modify the administrator user. Any logs found that meet the above conditions and also have a URL containing /api/v2/ should be cause for concern. Further investigation of any matching log entries can reveal any damage an attack has done. Additionally, an attacker may perform the following actions to further compromise a system Modify the admin SSH key to enable the attacker to login to the compromised system. \
Add new local users. \
Update networking configurations to reroute traffic. \
Download the system configuration. \
Initiate packet captures to capture other sensitive system information. Reference Horizon3.ai
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache. Splunk for Nginx, or Splunk for Palo Alto.
action.escu.known_false_positives = GET requests will be noisy and need to be filtered out or removed from the query based on volume. Restrict analytic to known publically facing Fortigates, or run analytic as a Hunt until properly tuned. It is also possible the user agent may be filtered on Report Runner or Node.js only for the exploit, however, it is unknown at this if other user agents may be used.
action.escu.creation_date = 2022-10-14
action.escu.modification_date = 2022-10-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Fortinet Appliance Auth bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["CVE-2022-40684 Fortinet Appliance Auth bypass"]
action.risk = 1
action.risk.param._risk_message = Potential CVE-2022-40684 against a Fortinet appliance may be occurring against $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Fortinet Appliance Auth bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["CVE-2022-40684 Fortinet Appliance Auth bypass"], "cis20": ["CIS 13"], "confidence": 90, "cve": ["CVE-2022-40684"], "impact": 90, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = CVE-2022-40684 is a Fortinet appliance auth bypass that is actively being exploited and a POC is released publicy. The POC adds a SSH key to the appliance. Note that the exploit can be used with any HTTP method (GET, POST, PUT, DELETE, etc). The REST API request failing is not an indication that an attacker was unsuccessful. Horizon3 was able to modify the admin SSH keys though a REST API request that reportedly failed. The collection /api/v2/ endpoints can be used to configure the system and modify the administrator user. Any logs found that meet the above conditions and also have a URL containing /api/v2/ should be cause for concern. Further investigation of any matching log entries can reveal any damage an attack has done. Additionally, an attacker may perform the following actions to further compromise a system Modify the admin SSH key to enable the attacker to login to the compromised system. \
Add new local users. \
Update networking configurations to reroute traffic. \
Download the system configuration. \
Initiate packet captures to capture other sensitive system information. Reference Horizon3.ai
action.notable.param.rule_title = Fortinet Appliance Auth bypass
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/api/v2/cmdb/system/admin*")  Web.http_method IN ("GET", "PUT") by Web.http_user_agent, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `fortinet_appliance_auth_bypass_filter`

[ESCU - Hunting for Log4Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting query assists with quickly assessing CVE-2021-44228, or Log4Shell, activity mapped to the Web Datamodel. This is a combination query attempting to identify, score and dashboard. Because the Log4Shell vulnerability requires the string to be in the logs, this will work to identify the activity anywhere in the HTTP headers using _raw. Modify the first line to use the same pattern matching against other log sources. Scoring is based on a simple rubric of 0-5. 5 being the best match, and less than 5 meant to identify additional patterns that will equate to a higher total score. \
The first jndi match identifies the standard pattern of `{jndi:` \
jndi_fastmatch is meant to identify any jndi in the logs. The score is set low and is meant to be the "base" score used later. \
jndi_proto is a protocol match that identifies `jndi` and one of `ldap, ldaps, rmi, dns, nis, iiop, corba, nds, http, https.` \
all_match is a very well written regex by https://gist.github.com/Schvenn that identifies nearly all patterns of this attack behavior. \
env works to identify environment variables in the header, meant to capture `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `env`. \
uri_detect is string match looking for the common uri paths currently being scanned/abused in the wild. \
keywords matches on enumerated values that, like `$ctx:loginId`, that may be found in the header used by the adversary. \
lookup matching is meant to catch some basic obfuscation that has been identified using upper, lower and date. \
Scoring will then occur based on any findings. The base score is meant to be 2 , created by jndi_fastmatch. Everything else is meant to increase that score. \
Finally, a simple table is created to show the scoring and the _raw field. Sort based on score or columns of interest.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following hunting query assists with quickly assessing CVE-2021-44228, or Log4Shell, activity mapped to the Web Datamodel. This is a combination query attempting to identify, score and dashboard. Because the Log4Shell vulnerability requires the string to be in the logs, this will work to identify the activity anywhere in the HTTP headers using _raw. Modify the first line to use the same pattern matching against other log sources. Scoring is based on a simple rubric of 0-5. 5 being the best match, and less than 5 meant to identify additional patterns that will equate to a higher total score. \
The first jndi match identifies the standard pattern of `{jndi:` \
jndi_fastmatch is meant to identify any jndi in the logs. The score is set low and is meant to be the "base" score used later. \
jndi_proto is a protocol match that identifies `jndi` and one of `ldap, ldaps, rmi, dns, nis, iiop, corba, nds, http, https.` \
all_match is a very well written regex by https://gist.github.com/Schvenn that identifies nearly all patterns of this attack behavior. \
env works to identify environment variables in the header, meant to capture `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `env`. \
uri_detect is string match looking for the common uri paths currently being scanned/abused in the wild. \
keywords matches on enumerated values that, like `$ctx:loginId`, that may be found in the header used by the adversary. \
lookup matching is meant to catch some basic obfuscation that has been identified using upper, lower and date. \
Scoring will then occur based on any findings. The base score is meant to be 2 , created by jndi_fastmatch. Everything else is meant to increase that score. \
Finally, a simple table is created to show the scoring and the _raw field. Sort based on score or columns of interest.
action.escu.how_to_implement = Out of the box, the Web datamodel is required to be pre-filled. However, tested was performed against raw httpd access logs. Change the first line to any dataset to pass the regex's against.
action.escu.known_false_positives = It is highly possible you will find false positives, however, the base score is set to 2 for _any_ jndi found in raw logs. tune and change as needed, include any filtering.
action.escu.creation_date = 2021-12-14
action.escu.modification_date = 2021-12-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Hunting for Log4Shell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Log4Shell CVE-2021-44228", "CISA AA22-320A"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Hunting for Log4Shell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228", "CISA AA22-320A"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2021-44228"], "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | from datamodel Web.Web | eval jndi=if(match(_raw, "(\{|%7B)[jJnNdDiI]{4}:"),4,0) | eval jndi_fastmatch=if(match(_raw, "[jJnNdDiI]{4}"),2,0) | eval jndi_proto=if(match(_raw,"(?i)jndi:(ldap[s]?|rmi|dns|nis|iiop|corba|nds|http|https):"),5,0) | eval all_match = if(match(_raw, "(?i)(%(25){0,}20|\s)*(%(25){0,}24|\$)(%(25){0,}20|\s)*(%(25){0,}7B|{)(%(25){0,}20|\s)*(%(25){0,}(6A|4A)|J)(%(25){0,}(6E|4E)|N)(%(25){0,}(64|44)|D)(%(25){0,}(69|49)|I)(%(25){0,}20|\s)*(%(25){0,}3A|:)[\w\%]+(%(25){1,}3A|:)(%(25){1,}2F|\/)[^\n]+"),5,0) | eval env_var = if(match(_raw, "env:") OR match(_raw, "env:AWS_ACCESS_KEY_ID") OR match(_raw, "env:AWS_SECRET_ACCESS_KEY"),5,0) | eval uridetect = if(match(_raw, "(?i)Basic\/Command\/Base64|Basic\/ReverseShell|Basic\/TomcatMemshell|Basic\/JBossMemshell|Basic\/WebsphereMemshell|Basic\/SpringMemshell|Basic\/Command|Deserialization\/CommonsCollectionsK|Deserialization\/CommonsBeanutils|Deserialization\/Jre8u20\/TomcatMemshell|Deserialization\/CVE_2020_2555\/WeblogicMemshell|TomcatBypass|GroovyBypass|WebsphereBypass"),4,0) | eval keywords = if(match(_raw,"(?i)\$\{ctx\:loginId\}|\$\{map\:type\}|\$\{filename\}|\$\{date\:MM-dd-yyyy\}|\$\{docker\:containerId\}|\$\{docker\:containerName\}|\$\{docker\:imageName\}|\$\{env\:USER\}|\$\{event\:Marker\}|\$\{mdc\:UserId\}|\$\{java\:runtime\}|\$\{java\:vm\}|\$\{java\:os\}|\$\{jndi\:logging/context-name\}|\$\{hostName\}|\$\{docker\:containerId\}|\$\{k8s\:accountName\}|\$\{k8s\:clusterName\}|\$\{k8s\:containerId\}|\$\{k8s\:containerName\}|\$\{k8s\:host\}|\$\{k8s\:labels.app\}|\$\{k8s\:labels.podTemplateHash\}|\$\{k8s\:masterUrl\}|\$\{k8s\:namespaceId\}|\$\{k8s\:namespaceName\}|\$\{k8s\:podId\}|\$\{k8s\:podIp\}|\$\{k8s\:podName\}|\$\{k8s\:imageId\}|\$\{k8s\:imageName\}|\$\{log4j\:configLocation\}|\$\{log4j\:configParentLocation\}|\$\{spring\:spring.application.name\}|\$\{main\:myString\}|\$\{main\:0\}|\$\{main\:1\}|\$\{main\:2\}|\$\{main\:3\}|\$\{main\:4\}|\$\{main\:bar\}|\$\{name\}|\$\{marker\}|\$\{marker\:name\}|\$\{spring\:profiles.active[0]|\$\{sys\:logPath\}|\$\{web\:rootDir\}|\$\{sys\:user.name\}"),4,0) | eval obf = if(match(_raw, "(\$|%24)[^ /]*({|%7b)[^ /]*(j|%6a)[^ /]*(n|%6e)[^ /]*(d|%64)[^ /]*(i|%69)[^ /]*(:|%3a)[^ /]*(:|%3a)[^ /]*(/|%2f)"),5,0) | eval lookups = if(match(_raw, "(?i)({|%7b)(main|sys|k8s|spring|lower|upper|env|date|sd)"),4,0)  | addtotals fieldname=Score, jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, obf, lookups | where Score > 2 | stats values(Score) by  jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, lookups, obf, dest, src, http_method, _raw | `hunting_for_log4shell_filter`

[ESCU - Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35078 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The given analytic is designed to detect the exploitation of CVE-2023-35078, a vulnerability in Ivanti Endpoint Manager Mobile (EPMM) affecting versions up to 11.4. Specifically, the query searches web logs for HTTP requests to the potentially vulnerable endpoint "/mifs/aad/api/v2/authorized/users?*" with a successful status code of 200. This analytic is instrumental in detecting unauthorized remote access to restricted functionalities or resources within the application, a behavior worth identifying for a Security Operations Center (SOC). By monitoring specific patterns and successful access indicators, it reveals an active attempt to exploit the vulnerability, potentially leading to data theft, unauthorized modifications, or further system compromise. If successfully executed, the impact can be severe, necessitating immediate action.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The given analytic is designed to detect the exploitation of CVE-2023-35078, a vulnerability in Ivanti Endpoint Manager Mobile (EPMM) affecting versions up to 11.4. Specifically, the query searches web logs for HTTP requests to the potentially vulnerable endpoint "/mifs/aad/api/v2/authorized/users?*" with a successful status code of 200. This analytic is instrumental in detecting unauthorized remote access to restricted functionalities or resources within the application, a behavior worth identifying for a Security Operations Center (SOC). By monitoring specific patterns and successful access indicators, it reveals an active attempt to exploit the vulnerability, potentially leading to data theft, unauthorized modifications, or further system compromise. If successfully executed, the impact can be severe, necessitating immediate action.
action.escu.how_to_implement = To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products.
action.escu.known_false_positives = The Proof of Concept exploit script indicates that status=200 is required for successful exploitation of the vulnerability. False positives may be present if status=200 is removed from the search.  If it is removed,then the search also alert on status=301 and status=404 which indicates unsuccessful exploitation attempts.  Analysts may find it useful to hunt for these status codes as well, but it is likely to produce a significant number of alerts as this is a widespread vulnerability.
action.escu.creation_date = 2023-07-31
action.escu.modification_date = 2023-07-31
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35078 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ivanti EPMM Remote Unauthenticated Access"]
action.risk = 1
action.risk.param._risk_message = Potential CVE-2023-35078 against an Ivanti EPMM appliance on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35078 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ivanti EPMM Remote Unauthenticated Access"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2023-35078"], "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The given analytic is designed to detect the exploitation of CVE-2023-35078, a vulnerability in Ivanti Endpoint Manager Mobile (EPMM) affecting versions up to 11.4. Specifically, the query searches web logs for HTTP requests to the potentially vulnerable endpoint "/mifs/aad/api/v2/authorized/users?*" with a successful status code of 200. This analytic is instrumental in detecting unauthorized remote access to restricted functionalities or resources within the application, a behavior worth identifying for a Security Operations Center (SOC). By monitoring specific patterns and successful access indicators, it reveals an active attempt to exploit the vulnerability, potentially leading to data theft, unauthorized modifications, or further system compromise. If successfully executed, the impact can be severe, necessitating immediate action.
action.notable.param.rule_title = Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35078
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/mifs/aad/api/v2/authorized/users?*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078_filter`

[ESCU - Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35082 - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential unauthorized access attempts exploiting CVE-2023-35082 within Ivantis software products. Initially assessed to affect only MobileIron Core versions up to 11.2, further insights revealed its influence extending to Ivanti Endpoint Manager Mobile (EPMM) versions 11.10, 11.9, 11.8, and MobileIron Core 11.7 and below. The vulnerability facilitates unauthorized API access via the specific URI path /mifs/asfV3/api/v2/. The analytic identifies this behavior by monitoring web access logs for this URI pattern coupled with a HTTP 200 response code, signifying successful unauthorized access. \ Such behavior is imperative for a Security Operations Center (SOC) to recognize, as it highlights potential security breaches which, if not addressed, could lead to unauthorized data access, system modifications, or further exploitation. In the event of a true positive, the implications are severe: an attacker might have gained unbridled access to sensitive organizational data or could modify systems maliciously. Be vigilant of potential false positives; benign activities might occasionally match the pattern. During triage, closely scrutinize the source of the access request and its subsequent actions. This analytic aids analysts in early threat detection, allowing for proactive risk mitigation.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects potential unauthorized access attempts exploiting CVE-2023-35082 within Ivantis software products. Initially assessed to affect only MobileIron Core versions up to 11.2, further insights revealed its influence extending to Ivanti Endpoint Manager Mobile (EPMM) versions 11.10, 11.9, 11.8, and MobileIron Core 11.7 and below. The vulnerability facilitates unauthorized API access via the specific URI path /mifs/asfV3/api/v2/. The analytic identifies this behavior by monitoring web access logs for this URI pattern coupled with a HTTP 200 response code, signifying successful unauthorized access. \ Such behavior is imperative for a Security Operations Center (SOC) to recognize, as it highlights potential security breaches which, if not addressed, could lead to unauthorized data access, system modifications, or further exploitation. In the event of a true positive, the implications are severe: an attacker might have gained unbridled access to sensitive organizational data or could modify systems maliciously. Be vigilant of potential false positives; benign activities might occasionally match the pattern. During triage, closely scrutinize the source of the access request and its subsequent actions. This analytic aids analysts in early threat detection, allowing for proactive risk mitigation.
action.escu.how_to_implement = To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products.
action.escu.known_false_positives = Similar to CVE-2023-35078, the path for exploitation indicates that status=200 is required for successful exploitation of the vulnerability. False positives may be present if status=200 is removed from the search.  If it is removed,then the search also alert on status=301 and status=404 which indicates unsuccessful exploitation attempts.  Analysts may find it useful to hunt for these status codes as well, but it is likely to produce a significant number of alerts as this is a widespread vulnerability.
action.escu.creation_date = 2023-08-08
action.escu.modification_date = 2023-08-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35082 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ivanti EPMM Remote Unauthenticated Access"]
action.risk = 1
action.risk.param._risk_message = Potential CVE-2023-35082 against an Ivanti EPMM appliance on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 64}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35082 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ivanti EPMM Remote Unauthenticated Access"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2023-35082"], "impact": 80, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects potential unauthorized access attempts exploiting CVE-2023-35082 within Ivantis software products. Initially assessed to affect only MobileIron Core versions up to 11.2, further insights revealed its influence extending to Ivanti Endpoint Manager Mobile (EPMM) versions 11.10, 11.9, 11.8, and MobileIron Core 11.7 and below. The vulnerability facilitates unauthorized API access via the specific URI path /mifs/asfV3/api/v2/. The analytic identifies this behavior by monitoring web access logs for this URI pattern coupled with a HTTP 200 response code, signifying successful unauthorized access. \ Such behavior is imperative for a Security Operations Center (SOC) to recognize, as it highlights potential security breaches which, if not addressed, could lead to unauthorized data access, system modifications, or further exploitation. In the event of a true positive, the implications are severe: an attacker might have gained unbridled access to sensitive organizational data or could modify systems maliciously. Be vigilant of potential false positives; benign activities might occasionally match the pattern. During triage, closely scrutinize the source of the access request and its subsequent actions. This analytic aids analysts in early threat detection, allowing for proactive risk mitigation.
action.notable.param.rule_title = Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35082
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/mifs/asfV3/api/v2/*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082_filter`

[ESCU - Ivanti Sentry Authentication Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is designed to detect unauthenticated access to the System Manager Portal in Ivanti Sentry, formerly known as MobileIron Sentry. The vulnerability, designated as CVE-2023-38035, affects all supported versions 9.18, 9.17, and 9.16, as well as older versions. The analytic works by monitoring for changes in the configuration of Sentry and the underlying operating system. Such changes could indicate an attacker attempting to execute OS commands as root. This behavior is of significant concern for a Security Operations Center (SOC) as it presents a substantial security risk, particularly if port 8443, the default port for the System Manager Portal, is exposed to the internet. If the analytic returns a true positive, it suggests that an attacker has gained unauthorized access to the Sentry system, potentially leading to a significant system compromise and data breach. It is important to note that while the issue has a high CVSS score, the risk of exploitation is low for customers who do not expose port 8443 to the internet. The search specifically looks for HTTP requests to certain endpoints ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") and HTTP status code of 200. Unusual or unexpected patterns in these parameters could indicate an attack.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This analytic is designed to detect unauthenticated access to the System Manager Portal in Ivanti Sentry, formerly known as MobileIron Sentry. The vulnerability, designated as CVE-2023-38035, affects all supported versions 9.18, 9.17, and 9.16, as well as older versions. The analytic works by monitoring for changes in the configuration of Sentry and the underlying operating system. Such changes could indicate an attacker attempting to execute OS commands as root. This behavior is of significant concern for a Security Operations Center (SOC) as it presents a substantial security risk, particularly if port 8443, the default port for the System Manager Portal, is exposed to the internet. If the analytic returns a true positive, it suggests that an attacker has gained unauthorized access to the Sentry system, potentially leading to a significant system compromise and data breach. It is important to note that while the issue has a high CVSS score, the risk of exploitation is low for customers who do not expose port 8443 to the internet. The search specifically looks for HTTP requests to certain endpoints ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") and HTTP status code of 200. Unusual or unexpected patterns in these parameters could indicate an attack.
action.escu.how_to_implement = To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products.
action.escu.known_false_positives = It is important to note that false positives may occur if the search criteria are expanded beyond the HTTP status code 200. In other words, if the search includes other HTTP status codes, the likelihood of encountering false positives increases. This is due to the fact that HTTP status codes other than 200 may not necessarily indicate a successful exploitation attempt.
action.escu.creation_date = 2023-08-24
action.escu.modification_date = 2023-08-24
action.escu.confidence = high
action.escu.full_search_name = ESCU - Ivanti Sentry Authentication Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Ivanti Sentry Authentication Bypass CVE-2023-38035"]
action.risk = 1
action.risk.param._risk_message = Possible exploitation of CVE-2023-38035 against $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Ivanti Sentry Authentication Bypass - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ivanti Sentry Authentication Bypass CVE-2023-38035"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2023-38035"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This analytic is designed to detect unauthenticated access to the System Manager Portal in Ivanti Sentry, formerly known as MobileIron Sentry. The vulnerability, designated as CVE-2023-38035, affects all supported versions 9.18, 9.17, and 9.16, as well as older versions. The analytic works by monitoring for changes in the configuration of Sentry and the underlying operating system. Such changes could indicate an attacker attempting to execute OS commands as root. This behavior is of significant concern for a Security Operations Center (SOC) as it presents a substantial security risk, particularly if port 8443, the default port for the System Manager Portal, is exposed to the internet. If the analytic returns a true positive, it suggests that an attacker has gained unauthorized access to the Sentry system, potentially leading to a significant system compromise and data breach. It is important to note that while the issue has a high CVSS score, the risk of exploitation is low for customers who do not expose port 8443 to the internet. The search specifically looks for HTTP requests to certain endpoints ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") and HTTP status code of 200. Unusual or unexpected patterns in these parameters could indicate an attack.
action.notable.param.rule_title = Ivanti Sentry Authentication Bypass
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_sentry_authentication_bypass_filter`

[ESCU - JetBrains TeamCity RCE Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect attempts to exploit the CVE-2023-42793 vulnerability in TeamCity On-Premises. It focuses on identifying suspicious POST requests to /app/rest/users/id:1/tokens/RPC2, which is the initial point of exploitation. This could indicate an unauthenticated attacker trying to gain administrative access through Remote Code Execution (RCE).
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic is designed to detect attempts to exploit the CVE-2023-42793 vulnerability in TeamCity On-Premises. It focuses on identifying suspicious POST requests to /app/rest/users/id:1/tokens/RPC2, which is the initial point of exploitation. This could indicate an unauthenticated attacker trying to gain administrative access through Remote Code Execution (RCE).
action.escu.how_to_implement = The following analytic requires the Web datamodel. Ensure data source is mapped correctly or modify and tune for your data source.
action.escu.known_false_positives = If TeamCity is not in use, this analytic will not return results. Monitor and tune for your environment.
action.escu.creation_date = 2023-10-01
action.escu.modification_date = 2023-10-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - JetBrains TeamCity RCE Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["JetBrains TeamCity Unauthenticated RCE"]
action.risk = 1
action.risk.param._risk_message = Potential JetBrains TeamCity RCE Attempt detected against URL $url$ on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "url", "risk_object_type": "other", "risk_score": 81}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 81}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - JetBrains TeamCity RCE Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["JetBrains TeamCity Unauthenticated RCE"], "cis20": ["CIS 13"], "confidence": 90, "cve": ["CVE-2023-42793"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect attempts to exploit the CVE-2023-42793 vulnerability in TeamCity On-Premises. It focuses on identifying suspicious POST requests to /app/rest/users/id:1/tokens/RPC2, which is the initial point of exploitation. This could indicate an unauthenticated attacker trying to gain administrative access through Remote Code Execution (RCE).
action.notable.param.rule_title = JetBrains TeamCity RCE Attempt
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/app/rest/users/id:1/tokens/RPC2*") Web.status=200 Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jetbrains_teamcity_rce_attempt_filter`

[ESCU - Juniper Networks Remote Code Execution Exploit Detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the exploitation of a remote code execution vulnerability in Juniper Networks devices. The vulnerability involves multiple steps, including uploading a malicious PHP file and an INI file to the target server, and then executing the PHP code by manipulating the PHP configuration via the uploaded INI file. The analytic specifically looks for requests to /webauth_operation.php?PHPRC=*, which are used to upload the files and execute the code, respectively. This behavior is worth identifying for a SOC because it indicates that an attacker is attempting to exploit the vulnerability to gain unauthorized access to the device and execute arbitrary code. If a true positive is found, it suggests that an attacker has successfully exploited the vulnerability and may have gained control over the device, leading to data theft, network compromise, or other damaging outcomes. Upon triage, review the request parameters and the response to determine if the exploitation was successful. Capture and inspect any relevant network traffic and server logs to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1105", "T1059"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects the exploitation of a remote code execution vulnerability in Juniper Networks devices. The vulnerability involves multiple steps, including uploading a malicious PHP file and an INI file to the target server, and then executing the PHP code by manipulating the PHP configuration via the uploaded INI file. The analytic specifically looks for requests to /webauth_operation.php?PHPRC=*, which are used to upload the files and execute the code, respectively. This behavior is worth identifying for a SOC because it indicates that an attacker is attempting to exploit the vulnerability to gain unauthorized access to the device and execute arbitrary code. If a true positive is found, it suggests that an attacker has successfully exploited the vulnerability and may have gained control over the device, leading to data theft, network compromise, or other damaging outcomes. Upon triage, review the request parameters and the response to determine if the exploitation was successful. Capture and inspect any relevant network traffic and server logs to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.escu.how_to_implement = To implement this search, ensure that the Web data model is populated. The search is activated when the Web data model is accelerated. Network products, such as Suricata or Palo Alto, need to be mapped to the Web data model. Adjust the mapping as necessary to suit your specific products.
action.escu.known_false_positives = Be aware of potential false positives - legitimate uses of the /webauth_operation.php endpoint may cause benign activities to be flagged.The URL in the analytic is specific to a successful attempt to exploit the vulnerability. Review contents of the HTTP body to determine if the request is malicious. If the request is benign, add the URL to the whitelist or continue to monitor.
action.escu.creation_date = 2023-08-29
action.escu.modification_date = 2023-08-29
action.escu.confidence = high
action.escu.full_search_name = ESCU - Juniper Networks Remote Code Execution Exploit Detection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Juniper JunOS Remote Code Execution"]
action.risk = 1
action.risk.param._risk_message = This analytic has identified a potential exploitation of a remote code execution vulnerability in Juniper Networks devices on $dest$ on the URL $url$ used for the exploit.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "url", "threat_object_type": "url string"}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Juniper Networks Remote Code Execution Exploit Detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Juniper JunOS Remote Code Execution"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2023-36844", "CVE-2023-36845", "CVE-2023-36846", "CVE-2023-36847"], "impact": 90, "mitre_attack": ["T1190", "T1105", "T1059"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the exploitation of a remote code execution vulnerability in Juniper Networks devices. The vulnerability involves multiple steps, including uploading a malicious PHP file and an INI file to the target server, and then executing the PHP code by manipulating the PHP configuration via the uploaded INI file. The analytic specifically looks for requests to /webauth_operation.php?PHPRC=*, which are used to upload the files and execute the code, respectively. This behavior is worth identifying for a SOC because it indicates that an attacker is attempting to exploit the vulnerability to gain unauthorized access to the device and execute arbitrary code. If a true positive is found, it suggests that an attacker has successfully exploited the vulnerability and may have gained control over the device, leading to data theft, network compromise, or other damaging outcomes. Upon triage, review the request parameters and the response to determine if the exploitation was successful. Capture and inspect any relevant network traffic and server logs to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
action.notable.param.rule_title = Juniper Networks Remote Code Execution Exploit Detection
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/webauth_operation.php?PHPRC=*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `juniper_networks_remote_code_execution_exploit_detection_filter`

[ESCU - Log4Shell JNDI Payload Injection Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = CVE-2021-44228 Log4Shell payloads can be injected via various methods, but on of the most common vectors injection is via Web calls. Many of the vulnerable java web applications that are using log4j have a web component to them are specially targets of this injection, specifically projects like Apache Struts, Flink, Druid, and Solr. The exploit is triggered by a LDAP lookup function in the log4j package, its invocation is similar to `${jndi:ldap://PAYLOAD_INJECTED}`, when executed against vulnerable web applications the invocation can be seen in various part of web logs. Specifically it has been successfully exploited via headers like X-Forwarded-For, User-Agent, Referer, and X-Api-Version. In this detection we first limit the scope of our search to the Web Datamodel and use the `| from datamodel` function to benefit from schema accelerated searching capabilities, mainly because the second part of the detection is pretty heavy, it runs a regex across all _raw events that looks for `${jndi:ldap://` pattern across all potential web fields available to the raw data, like http headers for example. If you see results for this detection, it means that there was a attempt at a injection, which could be a reconnaissance activity or a valid expliotation attempt, but this does not exactly mean that the host was indeed successfully exploited.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = CVE-2021-44228 Log4Shell payloads can be injected via various methods, but on of the most common vectors injection is via Web calls. Many of the vulnerable java web applications that are using log4j have a web component to them are specially targets of this injection, specifically projects like Apache Struts, Flink, Druid, and Solr. The exploit is triggered by a LDAP lookup function in the log4j package, its invocation is similar to `${jndi:ldap://PAYLOAD_INJECTED}`, when executed against vulnerable web applications the invocation can be seen in various part of web logs. Specifically it has been successfully exploited via headers like X-Forwarded-For, User-Agent, Referer, and X-Api-Version. In this detection we first limit the scope of our search to the Web Datamodel and use the `| from datamodel` function to benefit from schema accelerated searching capabilities, mainly because the second part of the detection is pretty heavy, it runs a regex across all _raw events that looks for `${jndi:ldap://` pattern across all potential web fields available to the raw data, like http headers for example. If you see results for this detection, it means that there was a attempt at a injection, which could be a reconnaissance activity or a valid expliotation attempt, but this does not exactly mean that the host was indeed successfully exploited.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx.
action.escu.known_false_positives = If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives.
action.escu.creation_date = 2021-12-13
action.escu.modification_date = 2021-12-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Log4Shell JNDI Payload Injection Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Log4Shell CVE-2021-44228", "CISA AA22-257A", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = CVE-2021-44228 Log4Shell triggered for host $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Log4Shell JNDI Payload Injection Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228", "CISA AA22-257A", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 30, "cve": ["CVE-2021-44228"], "impact": 50, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | from datamodel Web.Web | regex _raw="[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)\w+(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?" | fillnull | stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user | `log4shell_jndi_payload_injection_attempt_filter`

[ESCU - Log4Shell JNDI Payload Injection with Outbound Connection - Rule]
action.escu = 0
action.escu.enabled = 1
description = CVE-2021-44228 Log4Shell payloads can be injected via various methods, but on of the most common vectors injection is via Web calls. Many of the vulnerable java web applications that are using log4j have a web component to them are specially targets of this injection, specifically projects like Apache Struts, Flink, Druid, and Solr. The exploit is triggered by a LDAP lookup function in the log4j package, its invocation is similar to `${jndi:ldap://PAYLOAD_INJECTED}`, when executed against vulnerable web applications the invocation can be seen in various part of web logs. Specifically it has been successfully exploited via headers like X-Forwarded-For, User-Agent, Referer, and X-Api-Version. In this detection we match the invocation function with a network connection to a malicious ip address.
action.escu.mappings = {"cis20": ["CIS 10"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic", "Web"]
action.escu.eli5 = CVE-2021-44228 Log4Shell payloads can be injected via various methods, but on of the most common vectors injection is via Web calls. Many of the vulnerable java web applications that are using log4j have a web component to them are specially targets of this injection, specifically projects like Apache Struts, Flink, Druid, and Solr. The exploit is triggered by a LDAP lookup function in the log4j package, its invocation is similar to `${jndi:ldap://PAYLOAD_INJECTED}`, when executed against vulnerable web applications the invocation can be seen in various part of web logs. Specifically it has been successfully exploited via headers like X-Forwarded-For, User-Agent, Referer, and X-Api-Version. In this detection we match the invocation function with a network connection to a malicious ip address.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx.
action.escu.known_false_positives = If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives.
action.escu.creation_date = 2021-12-13
action.escu.modification_date = 2021-12-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Log4Shell JNDI Payload Injection with Outbound Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Log4Shell CVE-2021-44228", "CISA AA22-320A"]
action.risk = 1
action.risk.param._risk_message = CVE-2021-44228 Log4Shell triggered for host $dest$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 15}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Log4Shell JNDI Payload Injection with Outbound Connection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228", "CISA AA22-320A"], "cis20": ["CIS 10"], "confidence": 30, "cve": ["CVE-2021-44228"], "impact": 50, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | from datamodel Web.Web | rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)(?<proto>\w+)(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?<affected_host>[a-zA-Z0-9\.\-\_\$]+)" | join affected_host type=inner [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic by All_Traffic.dest | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename dest AS affected_host] | fillnull | stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user | `log4shell_jndi_payload_injection_with_outbound_connection_filter`

[ESCU - Microsoft SharePoint Server Elevation of Privilege - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2023-29357. This vulnerability pertains to an elevation of privilege due to improper handling of authentication tokens. By monitoring for suspicious activities related to SharePoint Server, the analytic identifies attempts to exploit this vulnerability. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the SharePoint environment, potentially leading to data theft or other malicious activities.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2023-29357. This vulnerability pertains to an elevation of privilege due to improper handling of authentication tokens. By monitoring for suspicious activities related to SharePoint Server, the analytic identifies attempts to exploit this vulnerability. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the SharePoint environment, potentially leading to data theft or other malicious activities.
action.escu.how_to_implement = This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Microsoft SharePoint.
action.escu.known_false_positives = False positives may occur if there are legitimate activities that mimic the exploitation pattern. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
action.escu.creation_date = 2023-09-27
action.escu.modification_date = 2023-09-27
action.escu.confidence = high
action.escu.full_search_name = ESCU - Microsoft SharePoint Server Elevation of Privilege - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Microsoft SharePoint Server Elevation of Privilege CVE-2023-29357"]
action.risk = 1
action.risk.param._risk_message = Possible exploitation of CVE-2023-29357 against $dest$ from $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 45}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Microsoft SharePoint Server Elevation of Privilege - Rule
action.correlationsearch.annotations = {"analytic_story": ["Microsoft SharePoint Server Elevation of Privilege CVE-2023-29357"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2023-29357"], "impact": 90, "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2023-29357. This vulnerability pertains to an elevation of privilege due to improper handling of authentication tokens. By monitoring for suspicious activities related to SharePoint Server, the analytic identifies attempts to exploit this vulnerability. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the SharePoint environment, potentially leading to data theft or other malicious activities.
action.notable.param.rule_title = Microsoft SharePoint Server Elevation of Privilege
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/_api/web/siteusers*","/_api/web/currentuser*") Web.status=200 Web.http_method=GET by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `microsoft_sharepoint_server_elevation_of_privilege_filter`

[ESCU - Monitor Web Traffic For Brand Abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse.
action.escu.mappings = {"cis20": ["CIS 13"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse.
action.escu.how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
action.escu.known_false_positives = None at this time
action.escu.creation_date = 2017-09-23
action.escu.modification_date = 2017-09-23
action.escu.confidence = high
action.escu.full_search_name = ESCU - Monitor Web Traffic For Brand Abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Brand Monitoring"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Monitor Web Traffic For Brand Abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brand Monitoring"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse.
action.notable.param.rule_title = Monitor Web Traffic For Brand Abuse
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter`

[ESCU - PaperCut NG Remote Web Access Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect potential exploitation attempts on publicly accessible PaperCut NG servers. It identifies connections from public IP addresses to the server and specifically monitors for URI paths commonly found in proof-of-concept (POC) scripts for exploiting PaperCut NG vulnerabilities. These URI paths have been observed in both Metasploit modules and standalone scripts used for attacking PaperCut NG servers. When a public IP address is detected accessing one or more of these suspicious URI paths, an alert may be generated to notify the security team of the potential threat. The team can then investigate the source IP address, the targeted PaperCut NG server, and any other relevant information to determine the nature of the activity and take appropriate actions to mitigate the risk.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic is designed to detect potential exploitation attempts on publicly accessible PaperCut NG servers. It identifies connections from public IP addresses to the server and specifically monitors for URI paths commonly found in proof-of-concept (POC) scripts for exploiting PaperCut NG vulnerabilities. These URI paths have been observed in both Metasploit modules and standalone scripts used for attacking PaperCut NG servers. When a public IP address is detected accessing one or more of these suspicious URI paths, an alert may be generated to notify the security team of the potential threat. The team can then investigate the source IP address, the targeted PaperCut NG server, and any other relevant information to determine the nature of the activity and take appropriate actions to mitigate the risk.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
action.escu.known_false_positives = False positives may be present, filter as needed.
action.escu.creation_date = 2023-05-15
action.escu.modification_date = 2023-05-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - PaperCut NG Remote Web Access Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["PaperCut MF NG Vulnerability"]
action.risk = 1
action.risk.param._risk_message = URIs specific to PaperCut NG have been access by a public IP against $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - PaperCut NG Remote Web Access Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["PaperCut MF NG Vulnerability"], "cis20": ["CIS 13"], "confidence": 70, "impact": 90, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect potential exploitation attempts on publicly accessible PaperCut NG servers. It identifies connections from public IP addresses to the server and specifically monitors for URI paths commonly found in proof-of-concept (POC) scripts for exploiting PaperCut NG vulnerabilities. These URI paths have been observed in both Metasploit modules and standalone scripts used for attacking PaperCut NG servers. When a public IP address is detected accessing one or more of these suspicious URI paths, an alert may be generated to notify the security team of the potential threat. The team can then investigate the source IP address, the targeted PaperCut NG server, and any other relevant information to determine the nature of the activity and take appropriate actions to mitigate the risk.
action.notable.param.rule_title = PaperCut NG Remote Web Access Attempt
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.url IN ("/app?service=page/SetupCompleted", "/app", "/app?service=page/PrinterList", "/app?service=direct/1/PrinterList/selectPrinter&sp=*", "/app?service=direct/1/PrinterDetails/printerOptionsTab.tab") NOT (src IN ("10.*.*.*","172.16.*.*", "192.168.*.*", "169.254.*.*", "127.*.*.*", "fc00::*", "fd00::*", "fe80::*")) by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest Web.dest_port sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `papercut_ng_remote_web_access_attempt_filter`

[ESCU - ProxyShell ProxyNotShell Behavior Detected - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following correlation will identify activity related to Windows Exchange being actively exploited by adversaries related to ProxyShell or ProxyNotShell. In addition, the analytic correlates post-exploitation Cobalt Strike analytic story. Common post-exploitation behavior has been seen in the wild includes adversaries running nltest, Cobalt Strike, Mimikatz and adding a new user. The correlation specifically looks for 5 distinct analyticstories to trigger. Modify or tune as needed for your organization. 5 analytics is an arbitrary number but was chosen to reduce the amount of noise but also require the 2 analytic stories or a ProxyShell and CobaltStrike to fire. Adversaries will exploit the vulnerable Exchange server, abuse SSRF, drop a web shell, utilize the PowerShell Exchange modules and begin post-exploitation.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Risk"]
action.escu.eli5 = The following correlation will identify activity related to Windows Exchange being actively exploited by adversaries related to ProxyShell or ProxyNotShell. In addition, the analytic correlates post-exploitation Cobalt Strike analytic story. Common post-exploitation behavior has been seen in the wild includes adversaries running nltest, Cobalt Strike, Mimikatz and adding a new user. The correlation specifically looks for 5 distinct analyticstories to trigger. Modify or tune as needed for your organization. 5 analytics is an arbitrary number but was chosen to reduce the amount of noise but also require the 2 analytic stories or a ProxyShell and CobaltStrike to fire. Adversaries will exploit the vulnerable Exchange server, abuse SSRF, drop a web shell, utilize the PowerShell Exchange modules and begin post-exploitation.
action.escu.how_to_implement = To implement this correlation, you will need to enable ProxyShell, ProxyNotShell and Cobalt Strike analytic stories (the anaytics themselves) and ensure proper data is being collected for Web and Endpoint datamodels. Run the correlation rule seperately to validate it is not triggering too much or generating incorrectly. Validate by running ProxyShell POC code and Cobalt Strike behavior.
action.escu.known_false_positives = False positives will be limited, however tune or modify the query as needed.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - ProxyShell ProxyNotShell Behavior Detected - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - RIR - ProxyShell ProxyNotShell Behavior Detected - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"], "cis20": ["CIS 13"], "confidence": 90, "impact": 90, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following correlation will identify activity related to Windows Exchange being actively exploited by adversaries related to ProxyShell or ProxyNotShell. In addition, the analytic correlates post-exploitation Cobalt Strike analytic story. Common post-exploitation behavior has been seen in the wild includes adversaries running nltest, Cobalt Strike, Mimikatz and adding a new user. The correlation specifically looks for 5 distinct analyticstories to trigger. Modify or tune as needed for your organization. 5 analytics is an arbitrary number but was chosen to reduce the amount of noise but also require the 2 analytic stories or a ProxyShell and CobaltStrike to fire. Adversaries will exploit the vulnerable Exchange server, abuse SSRF, drop a web shell, utilize the PowerShell Exchange modules and begin post-exploitation.
action.notable.param.rule_title = RBA: ProxyShell ProxyNotShell Behavior Detected
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.analyticstories) as analyticstories values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count dc(All_Risk.analyticstories) as dc_analyticstories from datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") OR (All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") AND All_Risk.analyticstories="Cobalt Strike") All_Risk.risk_object_type="system" by _time span=1h All_Risk.risk_object All_Risk.risk_object_type | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| where source_count >=5 | `proxyshell_proxynotshell_behavior_detected_filter`

[ESCU - Spring4Shell Payload URL Request - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is static indicators related to CVE-2022-22963, Spring4Shell. The 3 indicators provide an amount of fidelity that source IP is attemping to exploit a web shell on the destination. The filename and cmd are arbitrary in this exploitation. Java will write a JSP to disk and a process will spawn from Java based on the cmd passed. This is indicative of typical web shell activity.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1505.003", "T1505", "T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic is static indicators related to CVE-2022-22963, Spring4Shell. The 3 indicators provide an amount of fidelity that source IP is attemping to exploit a web shell on the destination. The filename and cmd are arbitrary in this exploitation. Java will write a JSP to disk and a process will spawn from Java based on the cmd passed. This is indicative of typical web shell activity.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
action.escu.known_false_positives = The jsp file names are static names used in current proof of concept code. =
action.escu.creation_date = 2022-07-12
action.escu.modification_date = 2022-07-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Spring4Shell Payload URL Request - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spring4Shell CVE-2022-22965"]
action.risk = 1
action.risk.param._risk_message = A URL was requested related to Spring4Shell POC code on $dest$ by $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 36}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 36}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Spring4Shell Payload URL Request - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spring4Shell CVE-2022-22965"], "cis20": ["CIS 13"], "confidence": 60, "cve": ["CVE-2022-22965"], "impact": 60, "mitre_attack": ["T1505.003", "T1505", "T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is static indicators related to CVE-2022-22963, Spring4Shell. The 3 indicators provide an amount of fidelity that source IP is attemping to exploit a web shell on the destination. The filename and cmd are arbitrary in this exploitation. Java will write a JSP to disk and a process will spawn from Java based on the cmd passed. This is indicative of typical web shell activity.
action.notable.param.rule_title = Spring4Shell Payload URL Request
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url IN ("*tomcatwar.jsp*","*poc.jsp*","*shell.jsp*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spring4shell_payload_url_request_filter`

[ESCU - SQL Injection with Long URLs - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects long URLs that contain multiple SQL commands. A proactive approach helps to detect and respond to potential threats earlier, mitigating the risks associated with SQL injection attacks. This detection is made by a Splunk query that searches for web traffic data where the destination category is a web server and the URL length is greater than 1024 characters or the HTTP user agent length is greater than 200 characters. This detection is important because it suggests that an attacker is attempting to exploit a web application through SQL injection. SQL injection is a common technique used by attackers to exploit vulnerabilities in web applications and gain unauthorized access to databases. Attackers can insert malicious SQL commands into a URL to manipulate the application's database and retrieve sensitive information or modify data. The impact of a successful SQL injection attack can be severe, potentially leading to data breaches, unauthorized access, and even complete compromise of the affected system. False positives might occur since the legitimate use of web applications or specific URLs in your environment can trigger the detection. Therefore, you must review and validate any alerts generated by this analytic before taking any action. Next steps include reviewing the source and destination of the web traffic, as well as the specific URL and HTTP user agent. Additionally, capture and analyze any relevant on-disk artifacts and review concurrent processes to determine the source of the attack.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects long URLs that contain multiple SQL commands. A proactive approach helps to detect and respond to potential threats earlier, mitigating the risks associated with SQL injection attacks. This detection is made by a Splunk query that searches for web traffic data where the destination category is a web server and the URL length is greater than 1024 characters or the HTTP user agent length is greater than 200 characters. This detection is important because it suggests that an attacker is attempting to exploit a web application through SQL injection. SQL injection is a common technique used by attackers to exploit vulnerabilities in web applications and gain unauthorized access to databases. Attackers can insert malicious SQL commands into a URL to manipulate the application's database and retrieve sensitive information or modify data. The impact of a successful SQL injection attack can be severe, potentially leading to data breaches, unauthorized access, and even complete compromise of the affected system. False positives might occur since the legitimate use of web applications or specific URLs in your environment can trigger the detection. Therefore, you must review and validate any alerts generated by this analytic before taking any action. Next steps include reviewing the source and destination of the web traffic, as well as the specific URL and HTTP user agent. Additionally, capture and analyze any relevant on-disk artifacts and review concurrent processes to determine the source of the attack.
action.escu.how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table.
action.escu.known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate.
action.escu.creation_date = 2022-03-28
action.escu.modification_date = 2022-03-28
action.escu.confidence = high
action.escu.full_search_name = ESCU - SQL Injection with Long URLs - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["SQL Injection"]
action.risk = 1
action.risk.param._risk_message = SQL injection attempt with url $url$ detected on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - SQL Injection with Long URLs - Rule
action.correlationsearch.annotations = {"analytic_story": ["SQL Injection"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects long URLs that contain multiple SQL commands. A proactive approach helps to detect and respond to potential threats earlier, mitigating the risks associated with SQL injection attacks. This detection is made by a Splunk query that searches for web traffic data where the destination category is a web server and the URL length is greater than 1024 characters or the HTTP user agent length is greater than 200 characters. This detection is important because it suggests that an attacker is attempting to exploit a web application through SQL injection. SQL injection is a common technique used by attackers to exploit vulnerabilities in web applications and gain unauthorized access to databases. Attackers can insert malicious SQL commands into a URL to manipulate the application's database and retrieve sensitive information or modify data. The impact of a successful SQL injection attack can be severe, potentially leading to data breaches, unauthorized access, and even complete compromise of the affected system. False positives might occur since the legitimate use of web applications or specific URLs in your environment can trigger the detection. Therefore, you must review and validate any alerts generated by this analytic before taking any action. Next steps include reviewing the source and destination of the web traffic, as well as the specific URL and HTTP user agent. Additionally, capture and analyze any relevant on-disk artifacts and review concurrent processes to determine the source of the attack.
action.notable.param.rule_title = SQL Injection with Long URLs
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval url=lower(url) | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter`

[ESCU - Supernova Webshell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic detects the presence of the Supernova webshell, which was used in the SUNBURST attack. This webshell can be used by attackers to gain unauthorized access to a compromised system and run arbitrary code. This detection is made by a Splunk query that searches for specific patterns in web URLs, including "*logoimagehandler.ashx*codes*", "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". These patterns are commonly used by the Supernova webshell to communicate with its command and control server. This detection is important because it indicates a potential compromise and unauthorized access to the system to run arbitrary code, which can lead to data theft, ransomware, or other damaging outcomes. False positives might occur since the patterns used by the webshell can also be present in legitimate web traffic. In such cases, tune the search to the specific environment and monitor it closely for any suspicious activity. Next steps include reviewing the web URLs and inspecting any relevant on-disk artifacts. Additionally, review concurrent processes and network connections to identify the source of the attack.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1505.003", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic detects the presence of the Supernova webshell, which was used in the SUNBURST attack. This webshell can be used by attackers to gain unauthorized access to a compromised system and run arbitrary code. This detection is made by a Splunk query that searches for specific patterns in web URLs, including "*logoimagehandler.ashx*codes*", "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". These patterns are commonly used by the Supernova webshell to communicate with its command and control server. This detection is important because it indicates a potential compromise and unauthorized access to the system to run arbitrary code, which can lead to data theft, ransomware, or other damaging outcomes. False positives might occur since the patterns used by the webshell can also be present in legitimate web traffic. In such cases, tune the search to the specific environment and monitor it closely for any suspicious activity. Next steps include reviewing the web URLs and inspecting any relevant on-disk artifacts. Additionally, review concurrent processes and network connections to identify the source of the attack.
action.escu.how_to_implement = To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.
action.escu.known_false_positives = There might be false positives associted with this detection since items like args as a web argument is pretty generic.
action.escu.creation_date = 2021-01-06
action.escu.modification_date = 2021-01-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Supernova Webshell - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["NOBELIUM Group"]
action.risk = 1
action.risk.param._risk_message = tbd
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 25}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Supernova Webshell - Rule
action.correlationsearch.annotations = {"analytic_story": ["NOBELIUM Group"], "cis20": ["CIS 13"], "confidence": 50, "impact": 50, "mitre_attack": ["T1505.003", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic detects the presence of the Supernova webshell, which was used in the SUNBURST attack. This webshell can be used by attackers to gain unauthorized access to a compromised system and run arbitrary code. This detection is made by a Splunk query that searches for specific patterns in web URLs, including "*logoimagehandler.ashx*codes*", "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". These patterns are commonly used by the Supernova webshell to communicate with its command and control server. This detection is important because it indicates a potential compromise and unauthorized access to the system to run arbitrary code, which can lead to data theft, ransomware, or other damaging outcomes. False positives might occur since the patterns used by the webshell can also be present in legitimate web traffic. In such cases, tune the search to the specific environment and monitor it closely for any suspicious activity. Next steps include reviewing the web URLs and inspecting any relevant on-disk artifacts. Additionally, review concurrent processes and network connections to identify the source of the attack.
action.notable.param.rule_title = Supernova Webshell
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s | `supernova_webshell_filter`

[ESCU - VMWare Aria Operations Exploit Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect potential exploitation attempts against VMWare vRealize Network Insight that align with the characteristics of CVE-2023-20887. This specific vulnerability is a critical security flaw that, if exploited, could allow an attacker to execute arbitrary code on the affected system.\
The analytic operates by monitoring web traffic, specifically HTTP POST requests, directed towards a specific URL endpoint ("/saas./resttosaasservlet"). This endpoint is known to be vulnerable and is a common target for attackers exploiting this vulnerability.\
The behavior this analytic detects is the sending of HTTP POST requests to the vulnerable endpoint. This is a significant indicator of an attempted exploit as it is the primary method used to trigger the vulnerability. The analytic detects this behavior by analyzing web traffic data and identifying HTTP POST requests directed at the vulnerable endpoint.\
Identifying this behavior is crucial for a Security Operations Center (SOC) as it can indicate an active attempt to exploit a known vulnerability within the network. If the identified behavior is a true positive, it suggests an attacker is attempting to exploit the CVE-2023-20887 vulnerability in VMWare vRealize Network Insight. The impact of such an attack could be severe, potentially allowing the attacker to execute arbitrary code on the affected system, leading to unauthorized access, data theft, or further propagation within the network.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1133", "T1190", "T1210", "T1068"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic is designed to detect potential exploitation attempts against VMWare vRealize Network Insight that align with the characteristics of CVE-2023-20887. This specific vulnerability is a critical security flaw that, if exploited, could allow an attacker to execute arbitrary code on the affected system.\
The analytic operates by monitoring web traffic, specifically HTTP POST requests, directed towards a specific URL endpoint ("/saas./resttosaasservlet"). This endpoint is known to be vulnerable and is a common target for attackers exploiting this vulnerability.\
The behavior this analytic detects is the sending of HTTP POST requests to the vulnerable endpoint. This is a significant indicator of an attempted exploit as it is the primary method used to trigger the vulnerability. The analytic detects this behavior by analyzing web traffic data and identifying HTTP POST requests directed at the vulnerable endpoint.\
Identifying this behavior is crucial for a Security Operations Center (SOC) as it can indicate an active attempt to exploit a known vulnerability within the network. If the identified behavior is a true positive, it suggests an attacker is attempting to exploit the CVE-2023-20887 vulnerability in VMWare vRealize Network Insight. The impact of such an attack could be severe, potentially allowing the attacker to execute arbitrary code on the affected system, leading to unauthorized access, data theft, or further propagation within the network.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. Restrict to specific dest assets to reduce false positives.
action.escu.known_false_positives = False positives will be present based on gateways in use, modify the status field as needed.
action.escu.creation_date = 2023-06-21
action.escu.modification_date = 2023-06-21
action.escu.confidence = high
action.escu.full_search_name = ESCU - VMWare Aria Operations Exploit Attempt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["VMware Aria Operations vRealize CVE-2023-20887"]
action.risk = 1
action.risk.param._risk_message = An exploitation attempt has occurred against $dest$ from $src$ related to CVE-2023-20887
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - VMWare Aria Operations Exploit Attempt - Rule
action.correlationsearch.annotations = {"analytic_story": ["VMware Aria Operations vRealize CVE-2023-20887"], "cis20": ["CIS 13"], "confidence": 80, "impact": 90, "mitre_attack": ["T1133", "T1190", "T1210", "T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect potential exploitation attempts against VMWare vRealize Network Insight that align with the characteristics of CVE-2023-20887. This specific vulnerability is a critical security flaw that, if exploited, could allow an attacker to execute arbitrary code on the affected system.\
The analytic operates by monitoring web traffic, specifically HTTP POST requests, directed towards a specific URL endpoint ("/saas./resttosaasservlet"). This endpoint is known to be vulnerable and is a common target for attackers exploiting this vulnerability.\
The behavior this analytic detects is the sending of HTTP POST requests to the vulnerable endpoint. This is a significant indicator of an attempted exploit as it is the primary method used to trigger the vulnerability. The analytic detects this behavior by analyzing web traffic data and identifying HTTP POST requests directed at the vulnerable endpoint.\
Identifying this behavior is crucial for a Security Operations Center (SOC) as it can indicate an active attempt to exploit a known vulnerability within the network. If the identified behavior is a true positive, it suggests an attacker is attempting to exploit the CVE-2023-20887 vulnerability in VMWare vRealize Network Insight. The impact of such an attack could be severe, potentially allowing the attacker to execute arbitrary code on the affected system, leading to unauthorized access, data theft, or further propagation within the network.
action.notable.param.rule_title = VMWare Aria Operations Exploit Attempt
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/saas./resttosaasservlet*")  Web.http_method=POST Web.status IN ("unknown", "200") by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_aria_operations_exploit_attempt_filter`

[ESCU - VMware Server Side Template Injection Hunt - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic identifies the server side template injection related to CVE-2022-22954, however is a variation found within the same endpoint of the URL scheme.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following hunting analytic identifies the server side template injection related to CVE-2022-22954, however is a variation found within the same endpoint of the URL scheme.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
action.escu.known_false_positives = False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed.
action.escu.creation_date = 2022-05-19
action.escu.modification_date = 2022-05-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - VMware Server Side Template Injection Hunt - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["VMware Server Side Injection and Privilege Escalation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - VMware Server Side Template Injection Hunt - Rule
action.correlationsearch.annotations = {"analytic_story": ["VMware Server Side Injection and Privilege Escalation"], "cis20": ["CIS 13"], "confidence": 50, "cve": ["CVE-2022-22954"], "impact": 70, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*deviceudid=*" AND Web.url IN ("*java.lang.ProcessBuilder*","*freemarker.template.utility.ObjectConstructor*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_server_side_template_injection_hunt_filter`

[ESCU - VMware Workspace ONE Freemarker Server-side Template Injection - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the server side template injection related to CVE-2022-22954. Based on the scanning activity across the internet and proof of concept code available the template injection occurs at catalog-portal/ui/oauth/verify?error=&deviceudid=. Upon triage, review parallel processes and VMware logs. Following the deviceudid= may be a command to be executed. Capture any file creates and review modified files on disk.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies the server side template injection related to CVE-2022-22954. Based on the scanning activity across the internet and proof of concept code available the template injection occurs at catalog-portal/ui/oauth/verify?error=&deviceudid=. Upon triage, review parallel processes and VMware logs. Following the deviceudid= may be a command to be executed. Capture any file creates and review modified files on disk.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
action.escu.known_false_positives = False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed.
action.escu.creation_date = 2022-05-19
action.escu.modification_date = 2022-05-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - VMware Workspace ONE Freemarker Server-side Template Injection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["VMware Server Side Injection and Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on $dest$ has occurred.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - VMware Workspace ONE Freemarker Server-side Template Injection - Rule
action.correlationsearch.annotations = {"analytic_story": ["VMware Server Side Injection and Privilege Escalation"], "cis20": ["CIS 13"], "confidence": 70, "cve": ["CVE-2022-22954"], "impact": 70, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*/catalog-portal/ui/oauth/verify?error=&deviceudid=*" AND Web.url="*freemarker.template.utility.Execute*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_workspace_one_freemarker_server_side_template_injection_filter`

[ESCU - Web JSP Request via URL - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the common URL requests used by a recent CVE - CVE-2022-22965, or Spring4Shell, to access a webshell on the remote webserver. The filename and cmd are arbitrary in this exploitation. Java will write a JSP to disk and a process will spawn from Java based on the cmd passed. This is indicative of typical web shell activity.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1505.003", "T1505", "T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies the common URL requests used by a recent CVE - CVE-2022-22965, or Spring4Shell, to access a webshell on the remote webserver. The filename and cmd are arbitrary in this exploitation. Java will write a JSP to disk and a process will spawn from Java based on the cmd passed. This is indicative of typical web shell activity.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
action.escu.known_false_positives = False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to servers.
action.escu.creation_date = 2022-04-05
action.escu.modification_date = 2022-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web JSP Request via URL - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spring4Shell CVE-2022-22965"]
action.risk = 1
action.risk.param._risk_message = A suspicious URL has been requested against $dest$ by $src$, related to web shell activity.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Web JSP Request via URL - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spring4Shell CVE-2022-22965"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2022-22965"], "impact": 90, "mitre_attack": ["T1505.003", "T1505", "T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the common URL requests used by a recent CVE - CVE-2022-22965, or Spring4Shell, to access a webshell on the remote webserver. The filename and cmd are arbitrary in this exploitation. Java will write a JSP to disk and a process will spawn from Java based on the cmd passed. This is indicative of typical web shell activity.
action.notable.param.rule_title = Web JSP Request via URL
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url IN ("*.jsp?cmd=*","*j&cmd=*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_jsp_request_via_url_filter`

[ESCU - Web Spring4Shell HTTP Request Class Module - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies the payload related to Spring4Shell, CVE-2022-22965. This analytic uses Splunk Stream HTTP to view the http request body, form data. STRT reviewed all the current proof of concept code and determined the commonality with the payloads being passed used the same fields "class.module.classLoader.resources.context.parent.pipeline.first".
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies the payload related to Spring4Shell, CVE-2022-22965. This analytic uses Splunk Stream HTTP to view the http request body, form data. STRT reviewed all the current proof of concept code and determined the commonality with the payloads being passed used the same fields "class.module.classLoader.resources.context.parent.pipeline.first".
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled.
action.escu.known_false_positives = False positives may occur and filtering may be required. Restrict analytic to asset type.
action.escu.creation_date = 2022-04-06
action.escu.modification_date = 2022-04-06
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web Spring4Shell HTTP Request Class Module - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spring4Shell CVE-2022-22965"]
action.risk = 1
action.risk.param._risk_message = A http body request related to Spring4Shell has been sent to $dest$ by $src$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Web Spring4Shell HTTP Request Class Module - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spring4Shell CVE-2022-22965"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2022-22965"], "impact": 90, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies the payload related to Spring4Shell, CVE-2022-22965. This analytic uses Splunk Stream HTTP to view the http request body, form data. STRT reviewed all the current proof of concept code and determined the commonality with the payloads being passed used the same fields "class.module.classLoader.resources.context.parent.pipeline.first".
action.notable.param.rule_title = Web Spring4Shell HTTP Request Class Module
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `stream_http` http_method IN ("POST") | stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by src dest http_method http_user_agent uri_path url bytes_in bytes_out | search http_request_body IN ("*class.module.classLoader.resources.context.parent.pipeline.first.fileDateFormat=_*", "*class.module.classLoader.resources.context.parent.pipeline.first.pattern*","*suffix=.jsp*") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_spring4shell_http_request_class_module_filter`

[ESCU - Web Spring Cloud Function FunctionRouter - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic identifies activity related to the web application Spring Cloud Function that was recently idenfied as vulnerable. This is CVE-2022-22963. Multiple proof of concept code was released. The URI that is hit includes `functionrouter`. The specifics of the exploit include a status of 500. In this query we did not include it, but for filtering you can add Web.status=500. The exploit data itself (based on all the POCs) is located in the form_data field. This field will include all class.modules being called.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic identifies activity related to the web application Spring Cloud Function that was recently idenfied as vulnerable. This is CVE-2022-22963. Multiple proof of concept code was released. The URI that is hit includes `functionrouter`. The specifics of the exploit include a status of 500. In this query we did not include it, but for filtering you can add Web.status=500. The exploit data itself (based on all the POCs) is located in the form_data field. This field will include all class.modules being called.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
action.escu.known_false_positives = False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to servers.
action.escu.creation_date = 2022-04-05
action.escu.modification_date = 2022-04-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Web Spring Cloud Function FunctionRouter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["Spring4Shell CVE-2022-22965"]
action.risk = 1
action.risk.param._risk_message = A suspicious URL has been requested against $dest$ by $src$, related to a vulnerability in Spring Cloud.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 42}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Web Spring Cloud Function FunctionRouter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spring4Shell CVE-2022-22965"], "cis20": ["CIS 13"], "confidence": 60, "cve": ["CVE-2022-22963"], "impact": 70, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic identifies activity related to the web application Spring Cloud Function that was recently idenfied as vulnerable. This is CVE-2022-22963. Multiple proof of concept code was released. The URI that is hit includes `functionrouter`. The specifics of the exploit include a status of 500. In this query we did not include it, but for filtering you can add Web.status=500. The exploit data itself (based on all the POCs) is located in the form_data field. This field will include all class.modules being called.
action.notable.param.rule_title = Web Spring Cloud Function FunctionRouter
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count from datamodel=Web where Web.http_method IN ("POST") Web.url="*/functionRouter*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest Web.status sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_spring_cloud_function_functionrouter_filter`

[ESCU - Windows Exchange Autodiscover SSRF Abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic utilizes the Web datamodel and identifies the ProxyShell or ProxyNotShell abuse. This vulnerability is a Server Side Request Forgery (SSRF) vulnerability, which is a web vulnerability that allows an adversary to exploit vulnerable functionality to access server side or local network services by affectively traversing the external firewall using vulnerable web functionality. This analytic looks for the URI path and query of autodiscover, powershell and mapi along with a POST occurring. It will tally a simple score and show the output of the events that match. This analytic may be added to by simply creating a new eval statement and modifying the hardcode digit for Score.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic utilizes the Web datamodel and identifies the ProxyShell or ProxyNotShell abuse. This vulnerability is a Server Side Request Forgery (SSRF) vulnerability, which is a web vulnerability that allows an adversary to exploit vulnerable functionality to access server side or local network services by affectively traversing the external firewall using vulnerable web functionality. This analytic looks for the URI path and query of autodiscover, powershell and mapi along with a POST occurring. It will tally a simple score and show the output of the events that match. This analytic may be added to by simply creating a new eval statement and modifying the hardcode digit for Score.
action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node. In addition, confirm the latest CIM App 4.20 or higher is installed.
action.escu.known_false_positives = False positives are limited.
action.escu.creation_date = 2023-07-10
action.escu.modification_date = 2023-07-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Windows Exchange Autodiscover SSRF Abuse - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"]
action.risk = 1
action.risk.param._risk_message = Activity related to ProxyShell or ProxyNotShell has been identified on $dest$. Review events and take action accordingly.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Windows Exchange Autodiscover SSRF Abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["ProxyShell", "BlackByte Ransomware", "ProxyNotShell"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2021-34523", "CVE-2021-34473", "CVE-2021-31207", "CVE-2022-41040", "CVE-2022-41082"], "impact": 90, "mitre_attack": ["T1190", "T1133"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic utilizes the Web datamodel and identifies the ProxyShell or ProxyNotShell abuse. This vulnerability is a Server Side Request Forgery (SSRF) vulnerability, which is a web vulnerability that allows an adversary to exploit vulnerable functionality to access server side or local network services by affectively traversing the external firewall using vulnerable web functionality. This analytic looks for the URI path and query of autodiscover, powershell and mapi along with a POST occurring. It will tally a simple score and show the output of the events that match. This analytic may be added to by simply creating a new eval statement and modifying the hardcode digit for Score.
action.notable.param.rule_title = Windows Exchange Autodiscover SSRF Abuse
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.status=200 OR Web.status=302 OR Web.status=401) AND Web.http_method=POST by Web.src Web.status Web.uri_path Web.dest Web.http_method Web.uri_query | `drop_dm_object_name("Web")` | eval is_autodiscover=if(like(lower(uri_path),"%autodiscover%"),1,0) | eval powershell = if(match(lower(uri_query),"powershell"), "1",0) | eval mapi=if(like(uri_query,"%/mapi/%"),1,0) | addtotals fieldname=Score is_autodiscover, powershell, mapi | fields Score, src,dest, status, uri_query,uri_path,http_method | where Score >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_exchange_autodiscover_ssrf_abuse_filter`

[ESCU - WS FTP Remote Code Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic is designed to detect a Remote Code Execution (RCE) vulnerability (CVE-2023-40044) in WS_FTP, a managed file transfer software by Progress. The search specifically looks for HTTP requests to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status of 200, which could indicate an exploitation attempt.
action.escu.mappings = {"cis20": ["CIS 13"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = The following analytic is designed to detect a Remote Code Execution (RCE) vulnerability (CVE-2023-40044) in WS_FTP, a managed file transfer software by Progress. The search specifically looks for HTTP requests to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status of 200, which could indicate an exploitation attempt.
action.escu.how_to_implement = The following analytic requires the Web datamodel. Ensure data source is mapped correctly or modify and tune for your data source.
action.escu.known_false_positives = If WS_FTP Server is not in use, this analytic will not return results. Monitor and tune for your environment. Note the MetaSploit module is focused on only hitting /AHT/ and not the full /AHT/AhtApiService.asmx/AuthUser URL.
action.escu.creation_date = 2023-10-01
action.escu.modification_date = 2023-10-01
action.escu.confidence = high
action.escu.full_search_name = ESCU - WS FTP Remote Code Execution - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = null
action.escu.analytic_story = ["WS FTP Server Critical Vulnerabilities"]
action.risk = 1
action.risk.param._risk_message = Potential WS FTP Remote Code Execution detected against URL $url$ on $dest$ from $src$
action.risk.param._risk = [{"risk_object_field": "url", "risk_object_type": "other", "risk_score": 72}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 72}, {"risk_object_field": "src", "risk_object_type": "system", "risk_score": 72}]
action.risk.param._risk_score = 0
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - WS FTP Remote Code Execution - Rule
action.correlationsearch.annotations = {"analytic_story": ["WS FTP Server Critical Vulnerabilities"], "cis20": ["CIS 13"], "confidence": 80, "cve": ["CVE-2023-40044"], "impact": 90, "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = user,dest
action.notable.param.rule_description = The following analytic is designed to detect a Remote Code Execution (RCE) vulnerability (CVE-2023-40044) in WS_FTP, a managed file transfer software by Progress. The search specifically looks for HTTP requests to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status of 200, which could indicate an exploitation attempt.
action.notable.param.rule_title = WS FTP Remote Code Execution
action.notable.param.security_domain = network
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/AHT/AhtApiService.asmx/AuthUser") Web.status=200 Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ws_ftp_remote_code_execution_filter`

### END ESCU DETECTIONS ###

### ESCU BASELINES ###

[ESCU - Baseline of blocked outbound traffic from AWS]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of blocked outbound traffic from AWS
description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.
action.escu.creation_date = 2018-05-07
action.escu.modification_date = 2018-05-07
action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command And Control"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`.
disabled = true
is_visible = false
search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count

[ESCU - Baseline Of Cloud Infrastructure API Calls Per User]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline Of Cloud Infrastructure API Calls Per User
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.
action.escu.creation_date = 2020-09-07
action.escu.modification_date = 2020-09-07
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.
action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.
disabled = true
is_visible = false
search = | tstats count as api_calls from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time api_calls, user, HourOfDay, isWeekend | eventstats dc(api_calls) as api_calls by user, HourOfDay, isWeekend | where api_calls >= 1 | fit DensityFunction api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_api_calls_v1 dist=norm show_density=true

[ESCU - Baseline Of Cloud Instances Destroyed]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline Of Cloud Instances Destroyed
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are destroyed in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances destroyed in a small time window.
action.escu.creation_date = 2020-08-25
action.escu.modification_date = 2020-08-25
action.escu.analytic_story = ["Suspicious Cloud Instance Activities", "Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are destroyed in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances destroyed in a small time window.
action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = | tstats count as instances_destroyed from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h | makecontinuous span=1h _time | eval instances_destroyed=coalesce(instances_destroyed, (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1 dist=expon show_density=true

[ESCU - Baseline Of Cloud Instances Launched]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline Of Cloud Instances Launched
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are created in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.
action.escu.creation_date = 2020-08-14
action.escu.modification_date = 2020-08-14
action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious Cloud Instance Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are created in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.
action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = | tstats count as instances_launched from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h | makecontinuous span=1h _time | eval instances_launched=coalesce(instances_launched, (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1 dist=expon show_density=true

[ESCU - Baseline Of Cloud Security Group API Calls Per User]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline Of Cloud Security Group API Calls Per User
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls for security groups are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly.
action.escu.creation_date = 2020-09-07
action.escu.modification_date = 2020-09-07
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls for security groups are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly.
action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.
disabled = true
is_visible = false
search = | tstats count as security_group_api_calls from datamodel=Change where All_Changes.object_category=firewall All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time security_group_api_calls, user, HourOfDay, isWeekend | eventstats dc(security_group_api_calls) as security_group_api_calls by user, HourOfDay, isWeekend | where security_group_api_calls >= 1 | fit DensityFunction security_group_api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_security_group_api_calls_v1 dist=norm show_density=true

[ESCU - Baseline of Command Line Length - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Command Line Length - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.
action.escu.creation_date = 2019-05-08
action.escu.modification_date = 2019-05-08
action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Unusual Processes"]
action.escu.data_models = []
cron_schedule = 0 0 1 * *
enableSched = 1
dispatch.earliest_time = -30d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.
action.escu.how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel

[ESCU - Baseline of DNS Query Length - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of DNS Query Length - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query.
action.escu.creation_date = 2019-05-08
action.escu.modification_date = 2019-05-08
action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command And Control"]
action.escu.data_models = ["Network_Resolution"]
cron_schedule = 0 0 */30 * *
enableSched = 1
dispatch.earliest_time = -30d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query.
action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel

[ESCU - Baseline of Network ACL Activity by ARN]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN
description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.creation_date = 2018-05-21
action.escu.modification_date = 2018-05-21
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`.
disabled = true
is_visible = false
search = `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count

[ESCU - Baseline of S3 Bucket deletion activity by ARN]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of S3 Bucket deletion activity by ARN
description = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.creation_date = 2018-07-17
action.escu.modification_date = 2018-07-17
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count

[ESCU - Baseline of Security Group Activity by ARN]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Security Group Activity by ARN
description = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.creation_date = 2018-04-17
action.escu.modification_date = 2018-04-17
action.escu.analytic_story = ["AWS User Monitoring"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
disabled = true
is_visible = false
search = `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count

[ESCU - Baseline of SMB Traffic - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of SMB Traffic - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.
action.escu.creation_date = 2019-05-08
action.escu.modification_date = 2019-05-08
action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Netsh Abuse", "Ransomware"]
action.escu.data_models = ["Network_Traffic"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.
action.escu.how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel

[ESCU - Count of assets by category]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Count of assets by category
description = This search shows you every asset category you have and the assets that belong to those categories.
action.escu.creation_date = 2017-09-13
action.escu.modification_date = 2017-09-13
action.escu.analytic_story = ["Asset Tracking"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search shows you every asset category you have and the assets that belong to those categories.
action.escu.how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated.
disabled = true
is_visible = false
search = | from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count

[ESCU - Count of Unique IPs Connecting to Ports]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports
description = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.
action.escu.creation_date = 2017-09-13
action.escu.modification_date = 2017-09-13
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command And Control"]
action.escu.data_models = ["Network_Traffic"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count

[ESCU - Create a list of approved AWS service accounts]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Create a list of approved AWS service accounts
description = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file.
action.escu.creation_date = 2018-12-03
action.escu.modification_date = 2018-12-03
action.escu.analytic_story = ["AWS User Monitoring"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate.
disabled = true
is_visible = false
search = `cloudtrail` errorCode=success | rename userName as identity | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats count by identity | table identity | outputlookup aws_service_accounts | stats count

[ESCU - Add Prohibited Processes to Enterprise Security]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Add Prohibited Processes to Enterprise Security
description = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints.
action.escu.creation_date = 2017-09-15
action.escu.modification_date = 2017-09-15
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Monitor for Unauthorized Software", "SamSam Ransomware"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints.
action.escu.how_to_implement = This search should be run on each new install of ESCU.
disabled = true
is_visible = false
search = | inputlookup prohibited_processes | search note!=ESCU* | inputlookup append=T prohibited_processes | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup prohibited_processes | stats count

[ESCU - Baseline of API Calls per User ARN]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of API Calls per User ARN
description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.creation_date = 2018-04-09
action.escu.modification_date = 2018-04-09
action.escu.analytic_story = ["AWS User Monitoring"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count

[ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window.
action.escu.creation_date = 2019-11-14
action.escu.modification_date = 2019-11-14
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1

[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window.
action.escu.creation_date = 2019-11-14
action.escu.modification_date = 2019-11-14
action.escu.analytic_story = ["Suspicious AWS EC2 Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = `cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1

[ESCU - Previously seen API call per user roles in CloudTrail]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously seen API call per user roles in CloudTrail
description = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.
action.escu.creation_date = 2018-04-16
action.escu.modification_date = 2018-04-16
action.escu.analytic_story = ["AWS User Monitoring"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count

[ESCU - Previously Seen AWS Provisioning Activity Sources]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen AWS Provisioning Activity Sources
description = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.
action.escu.creation_date = 2018-03-16
action.escu.modification_date = 2018-03-16
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src | stats count

[ESCU - Previously Seen EC2 AMIs]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen EC2 AMIs
description = This search builds a table of previously seen AMIs used to launch EC2 instances
action.escu.creation_date = 2018-03-12
action.escu.modification_date = 2018-03-12
action.escu.analytic_story = ["AWS Cryptomining"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen AMIs used to launch EC2 instances
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as firstTime latest(_time) as lastTime by amiID | outputlookup previously_seen_ec2_amis | stats count

[ESCU - Previously Seen EC2 Instance Types]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen EC2 Instance Types
description = This search builds a table of previously seen EC2 instance types
action.escu.creation_date = 2018-03-08
action.escu.modification_date = 2018-03-08
action.escu.analytic_story = ["AWS Cryptomining"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen EC2 instance types
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types | stats count

[ESCU - Previously Seen EC2 Launches By User]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen EC2 Launches By User
description = This search builds a table of previously seen ARNs that have launched a EC2 instance.
action.escu.creation_date = 2018-03-15
action.escu.modification_date = 2018-03-15
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen ARNs that have launched a EC2 instance.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user | stats count

[ESCU - Previously seen users in CloudTrail]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously seen users in CloudTrail
description = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
action.escu.creation_date = 2018-04-30
action.escu.modification_date = 2018-04-30
action.escu.analytic_story = ["Suspicious AWS Login Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail | stats count

[ESCU - Update previously seen users in CloudTrail]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Update previously seen users in CloudTrail
description = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
action.escu.creation_date = 2018-04-30
action.escu.modification_date = 2018-04-30
action.escu.analytic_story = ["Suspicious AWS Login Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail

[ESCU - Discover DNS records]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Discover DNS records
description = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup
action.escu.creation_date = 2019-02-14
action.escu.modification_date = 2019-02-14
action.escu.analytic_story = ["DNS Hijacking"]
action.escu.data_models = ["Network_Resolution"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup
action.escu.how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation
disabled = true
is_visible = false
search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer |  makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records

[ESCU - DNSTwist Domain Names]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - DNSTwist Domain Names
description = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.
action.escu.creation_date = 2018-10-08
action.escu.modification_date = 2018-10-08
action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.
action.escu.how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**.
disabled = true
is_visible = false
search = | dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count

[ESCU - Identify Systems Creating Remote Desktop Traffic]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Identify Systems Creating Remote Desktop Traffic
description = This search counts the numbers of times the system has generated remote desktop traffic.
action.escu.creation_date = 2017-09-15
action.escu.modification_date = 2017-09-15
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Active Directory Lateral Movement"]
action.escu.data_models = ["Network_Traffic"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search counts the numbers of times the system has generated remote desktop traffic.
action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count

[ESCU - Identify Systems Receiving Remote Desktop Traffic]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic
description = This search counts the numbers of times the system has created remote desktop traffic
action.escu.creation_date = 2017-09-15
action.escu.modification_date = 2017-09-15
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Active Directory Lateral Movement"]
action.escu.data_models = ["Network_Traffic"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search counts the numbers of times the system has created remote desktop traffic
action.escu.how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count

[ESCU - Identify Systems Using Remote Desktop]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop
description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.
action.escu.creation_date = 2019-04-01
action.escu.modification_date = 2019-04-01
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Active Directory Lateral Movement"]
action.escu.data_models = ["Endpoint"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.
action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count

[ESCU - Monitor Successful Backups]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Monitor Successful Backups
description = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.analytic_story = ["Monitor Backup Solution"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.
action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs.
disabled = true
is_visible = false
search = `netbackup` "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE

[ESCU - Monitor Unsuccessful Backups]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Monitor Unsuccessful Backups
description = This search is intended to give you a feel for how often backup failures happen in your environments.  Fluctuations in these numbers will allow you to determine when you should investigate.
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.analytic_story = ["Monitor Backup Solution"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is intended to give you a feel for how often backup failures happen in your environments.  Fluctuations in these numbers will allow you to determine when you should investigate.
action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs.
disabled = true
is_visible = false
search = `netbackup` "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE

[ESCU - Previously Seen AWS Cross Account Activity]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity
description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
action.escu.creation_date = 2018-06-04
action.escu.modification_date = 2018-06-04
action.escu.analytic_story = ["AWS Cross Account Activity"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count

[ESCU - Previously Seen AWS Cross Account Activity - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity - Initial
description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
action.escu.creation_date = 2020-08-15
action.escu.modification_date = 2020-08-15
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"]
action.escu.data_models = ["Authentication"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later)and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role |  `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" |  where  vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | table requestingAccountId requestedAccountId firstTime lastTime | outputlookup previously_seen_aws_cross_account_activity

[ESCU - Previously Seen AWS Cross Account Activity - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity - Update
description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
action.escu.creation_date = 2020-08-15
action.escu.modification_date = 2020-08-15
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"]
action.escu.data_models = ["Authentication"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_aws_cross_account_activity` kvstore
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | stats min(firstTime) as firstTime max(lastTime) as lastTime by requestingAccountId requestedAccountId | outputlookup previously_seen_aws_cross_account_activity

[ESCU - Previously Seen AWS Regions]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen AWS Regions
description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days
action.escu.creation_date = 2018-01-08
action.escu.modification_date = 2018-01-08
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions| stats count

[ESCU - Previously Seen Cloud API Calls Per User Role - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud API Calls Per User Role - Initial
description = This search builds a table of the first and last times seen for every user role and command combination. This is broadly defined as any event that runs or creates something. This table is then cached.
action.escu.creation_date = 2020-09-03
action.escu.modification_date = 2020-09-03
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of the first and last times seen for every user role and command combination. This is broadly defined as any event that runs or creates something. This table is then cached.
action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role

[ESCU - Previously Seen Cloud API Calls Per User Role - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud API Calls Per User Role - Update
description = This search updates the table of the first and last times seen for every user role and command combination.
action.escu.creation_date = 2020-09-03
action.escu.modification_date = 2020-09-03
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search updates the table of the first and last times seen for every user role and command combination.
action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | table user, command, firstTimeSeen, lastTimeSeen | inputlookup previously_seen_cloud_api_calls_per_user_role append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by user, command | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_api_calls_per_user_role_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role

[ESCU - Previously Seen Cloud Compute Creations By User - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Creations By User - Initial
description = This search builds a table of previously seen users that have launched a cloud compute instance.
action.escu.creation_date = 2020-08-15
action.escu.modification_date = 2020-08-15
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen users that have launched a cloud compute instance.
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user | `drop_dm_object_name("All_Changes")` | outputlookup previously_seen_cloud_compute_creations_by_user | stats count

[ESCU - Previously Seen Cloud Compute Creations By User - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Creations By User - Update
description = This search builds a table of previously seen users that have launched a cloud compute instance.
action.escu.creation_date = 2020-08-15
action.escu.modification_date = 2020-08-15
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen users that have launched a cloud compute instance.
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user| `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_compute_creations_by_user | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(), "-90d@d") | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_creations_by_user

[ESCU - Previously Seen Cloud Compute Images - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Images - Initial
description = This search builds a table of previously seen images used to launch cloud compute instances
action.escu.creation_date = 2020-10-08
action.escu.modification_date = 2020-10-08
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen images used to launch cloud compute instances
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created  by All_Changes.Instance_Changes.image_id | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images

[ESCU - Previously Seen Cloud Compute Images - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Images - Update
description = This search builds a table of previously seen images used to launch cloud compute instances
action.escu.creation_date = 2020-08-12
action.escu.modification_date = 2020-08-12
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen images used to launch cloud compute instances
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | inputlookup append=t previously_seen_cloud_compute_images | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by image_id | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images

[ESCU - Previously Seen Cloud Compute Instance Types - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Instance Types - Initial
description = This search builds a table of previously seen cloud compute instance types
action.escu.creation_date = 2020-9-03
action.escu.modification_date = 2020-9-03
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen cloud compute instance types
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types

[ESCU - Previously Seen Cloud Compute Instance Types - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Instance Types - Update
description = This search builds a table of previously seen cloud compute instance types
action.escu.creation_date = 2020-9-03
action.escu.modification_date = 2020-9-03
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen cloud compute instance types
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type != "unknown" | inputlookup append=t previously_seen_cloud_compute_instance_types | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by instance_type | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_instance_type_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types

[ESCU - Previously Seen Cloud Instance Modifications By User - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Instance Modifications By User - Initial
description = This search builds a table of previously seen users that have modified a cloud instance.
action.escu.creation_date = 2020-07-29
action.escu.modification_date = 2020-07-29
action.escu.analytic_story = ["Suspicious Cloud Instance Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen users that have modified a cloud instance.
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 c=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user

[ESCU - Previously Seen Cloud Instance Modifications By User - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Instance Modifications By User - Update
description = This search updates a table of previously seen Cloud Instance modifications that have been made by a user
action.escu.creation_date = 2020-07-29
action.escu.modification_date = 2020-07-29
action.escu.analytic_story = ["Suspicious Cloud Instance Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search updates a table of previously seen Cloud Instance modifications that have been made by a user
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_instance_modifications_by_user | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user

[ESCU - Previously Seen Cloud Provisioning Activity Sources - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Provisioning Activity Sources - Initial
description = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. This table is then cached.
action.escu.creation_date = 2020-08-19
action.escu.modification_date = 2020-08-19
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. This table is then cached.
action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources

[ESCU - Previously Seen Cloud Provisioning Activity Sources - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Provisioning Activity Sources - Update
description = This returns the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity within the last day. Cloud provisioning is broadly defined as any event that runs or creates something.  It then updates this information with historical data and filters out locations that have not been seen within the specified time window. This updated table is then cached.
action.escu.creation_date = 2020-08-20
action.escu.modification_date = 2020-08-20
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This returns the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity within the last day. Cloud provisioning is broadly defined as any event that runs or creates something.  It then updates this information with historical data and filters out locations that have not been seen within the specified time window. This updated table is then cached.
action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | table src, firstTimeSeen, lastTimeSeen, City, Country, Region | inputlookup previously_seen_cloud_provisioning_activity_sources append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by src, City, Country, Region | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_provisioning_activity_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources

[ESCU - Previously Seen Cloud Regions - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Regions - Initial
description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days
action.escu.creation_date = 2020-09-02
action.escu.modification_date = 2020-09-02
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions

[ESCU - Previously Seen Cloud Regions - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Cloud Regions - Update
description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days
action.escu.creation_date = 2020-09-02
action.escu.modification_date = 2020-09-02
action.escu.analytic_story = ["Cloud Cryptomining"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days
action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_regions | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by vendor_region | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_region_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions | stats count

[ESCU - Previously seen command line arguments]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously seen command line arguments
description = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days.
action.escu.creation_date = 2019-03-01
action.escu.modification_date = 2019-03-01
action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Hidden Cobra Malware", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "IcedID"]
action.escu.data_models = ["Endpoint"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days.
action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)`

[ESCU - Previously Seen EC2 Modifications By User]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen EC2 Modifications By User
description = This search builds a table of previously seen ARNs that have launched a EC2 instance.
action.escu.creation_date = 2018-04-05
action.escu.modification_date = 2018-04-05
action.escu.analytic_story = ["Unusual AWS EC2 Modifications"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search builds a table of previously seen ARNs that have launched a EC2 instance.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
disabled = true
is_visible = false
search = `cloudtrail` `ec2_modification_api_calls` errorCode=success | spath output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count

[ESCU - Previously Seen Running Windows Services - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Running Windows Services - Initial
description = This collects the services that have been started across your entire enterprise.
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse", "NOBELIUM Group"]
action.escu.data_models = []
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This collects the services that have been started across your entire enterprise.
action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
disabled = true
is_visible = false
search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | outputlookup previously_seen_running_windows_services

[ESCU - Previously Seen Running Windows Services - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Running Windows Services - Update
description = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached.
action.escu.creation_date = 2020-06-23
action.escu.modification_date = 2020-06-23
action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse", "NOBELIUM Group"]
action.escu.data_models = []
cron_schedule = 55 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached.
action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
disabled = true
is_visible = false
search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | inputlookup previously_seen_running_windows_services append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by service | where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`") | outputlookup previously_seen_running_windows_services

[ESCU - Previously seen S3 bucket access by remote IP]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously seen S3 bucket access by remote IP
description = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200"
action.escu.creation_date = 2018-06-28
action.escu.modification_date = 2018-06-28
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200"
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `aws_s3_accesslogs` http_status=200  | stats  earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count

[ESCU - Previously Seen Users in CloudTrail - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Users in CloudTrail - Initial
description = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by username, within the last 30 days.
action.escu.creation_date = 2020-05-28
action.escu.modification_date = 2020-05-28
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"]
action.escu.data_models = ["Authentication"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by username, within the last 30 days.
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins`, which is a lookup file created by this support search.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | rename Authentication.user as user Authentication.src as src | table user src City Region Country firstTime lastTime | outputlookup previously_seen_users_console_logins | stats count

[ESCU - Previously Seen Users In CloudTrail - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Users In CloudTrail - Update
description = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by user, within the last hour.
action.escu.creation_date = 2020-05-28
action.escu.modification_date = 2020-05-28
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"]
action.escu.data_models = ["Authentication"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by user, within the last hour.
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins`, which is a lookup file created by this support search.
disabled = true
is_visible = false
search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | rename Authentication.user as user Authentication.src as src | table user src City Region Country firstTime lastTime | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins

[ESCU - Previously Seen Zoom Child Processes - Initial]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Zoom Child Processes - Initial
description = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached.
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.analytic_story = ["Suspicious Zoom Child Processes"]
action.escu.data_models = ["Endpoint"]
cron_schedule = 0 2 * * 0
enableSched = 1
dispatch.earliest_time = -90d@d
dispatch.latest_time = -1d@d
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached.
action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table dest, process_name, firstTimeSeen, lastTimeSeen | outputlookup zoom_first_time_child_process

[ESCU - Previously Seen Zoom Child Processes - Update]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Previously Seen Zoom Child Processes - Update
description = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk.
action.escu.creation_date = 2020-05-20
action.escu.modification_date = 2020-05-20
action.escu.analytic_story = ["Suspicious Zoom Child Processes"]
action.escu.data_models = ["Endpoint"]
cron_schedule = 55 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk.
action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table firstTimeSeen, lastTimeSeen, process_name, dest | inputlookup zoom_first_time_child_process append=t | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") | outputlookup zoom_first_time_child_process

[ESCU - Splunk Command and Scripting Interpreter Risky SPL MLTK Baseline]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Splunk Command and Scripting Interpreter Risky SPL MLTK Baseline
description = This search supports an analyst looking for abuse or misuse of the risky commands listed here: https://docs.splunk.com/Documentation/Splunk/latest/Security/SPLsafeguards#Commands_that_trigger_the_warning This is accomplished by using the time spent executing one of these risky commands as a proxy for misuse/abuse of interest during investigation and/or hunting. The search builds a model utilizes the MLTK DensityFunction algorithm on Splunk app audit log data. The model uses the past 7 days of user history executing the above referenced commands then aggregates the total search run time for each hour as indicator of user behavior. The model identifies the top 0.1% of user search run time, indicating a risky use of these commands. Users can adjust this threshold 0.1% as interested however this will correlate to missed/false positive rates. This search should be scheduled to run at least every 7 days. The name of machine learning model generated is "risky_command_abuse" and should be configured to be globally shared (not private) in MLTK app as documented here: https://docs.splunk.com/Documentation/MLApp/5.3.1/User/Models#Sharing_models_from_other_Splunk_apps unless the same account of training this model will be used to perform inference using this model for anomaly detection.
action.escu.creation_date = 2022-05-27
action.escu.modification_date = 2022-05-27
action.escu.analytic_story = ["Splunk Vulnerabilities"]
action.escu.data_models = ["Splunk_Audit"]
cron_schedule = 55 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search supports an analyst looking for abuse or misuse of the risky commands listed here: https://docs.splunk.com/Documentation/Splunk/latest/Security/SPLsafeguards#Commands_that_trigger_the_warning This is accomplished by using the time spent executing one of these risky commands as a proxy for misuse/abuse of interest during investigation and/or hunting. The search builds a model utilizes the MLTK DensityFunction algorithm on Splunk app audit log data. The model uses the past 7 days of user history executing the above referenced commands then aggregates the total search run time for each hour as indicator of user behavior. The model identifies the top 0.1% of user search run time, indicating a risky use of these commands. Users can adjust this threshold 0.1% as interested however this will correlate to missed/false positive rates. This search should be scheduled to run at least every 7 days. The name of machine learning model generated is "risky_command_abuse" and should be configured to be globally shared (not private) in MLTK app as documented here: https://docs.splunk.com/Documentation/MLApp/5.3.1/User/Models#Sharing_models_from_other_Splunk_apps unless the same account of training this model will be used to perform inference using this model for anomaly detection.
action.escu.how_to_implement = The corresponding detection of using this model is "Splunk Command and Scripting Interpreter Risky SPL MLTK". This detection depends on MLTK app which can be found here - https://splunkbase.splunk.com/app/2890/ and it assumes Splunk accelerated audit data model is available. For large enterprises, training the model might take significant computing resources. It might require dedicated search head. The underlined machine learning algorithm this detection used is DensityFunction. It might need to increase its settings default values, such as max_fit_time, max_groups, etc. More details of achieving optimal performance and configuring DensityFunction parameters can be found here - https://docs.splunk.com/Documentation/MLApp/5.3.1/User/Configurefitandapply Users can modify earliest=-7d@d in the search to other value so that the search can collect enough data points to build a good baseline model. Users can also modify list of risky commands in "Search_Activity.search IN" to better suit users' violation policy and their usage environment.
disabled = true
is_visible = false
search = | tstats sum(Search_Activity.total_run_time) as run_time, count FROM datamodel=Splunk_Audit.Search_Activity WHERE (Search_Activity.user!="") AND (Search_Activity.total_run_time>1) AND (earliest=-7d@d latest=now) AND (Search_Activity.search IN ("*| runshellscript *", "*| collect *","*| delete *", "*| fit *", "*| outputcsv *", "*| outputlookup *", "*| run *", "*| script *", "*| sendalert *", "*| sendemail *", "*| tscolle*")) AND (Search_Activity.search_type=adhoc) AND (Search_Activity.user!=splunk-system-user) BY _time, Search_Activity.user span=1h | fit DensityFunction "run_time" dist=auto lower_threshold=0.000001 upper_threshold=0.001 show_density=true by Search_Activity.user into "risky_command_abuse" 

[ESCU - Systems Ready for Spectre-Meltdown Windows Patch]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch
description = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.
action.escu.creation_date = 2018-01-08
action.escu.modification_date = 2018-01-08
action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"]
action.escu.data_models = ["Change"]
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.
action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")`

[ESCU - Windows Updates Install Failures]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Windows Updates Install Failures
description = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.
action.escu.creation_date = 2017-09-14
action.escu.modification_date = 2017-09-14
action.escu.analytic_story = ["Monitor for Updates"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.
action.escu.how_to_implement = You must be ingesting your Windows Update Logs
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d

[ESCU - Windows Updates Install Successes]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Windows Updates Install Successes
description = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.
action.escu.creation_date = 2017-09-14
action.escu.modification_date = 2017-09-14
action.escu.analytic_story = ["Monitor for Updates"]
action.escu.data_models = []
cron_schedule = 10 0 * * *
enableSched = 1
dispatch.earliest_time = -1450m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.
action.escu.how_to_implement = You must be ingesting your Windows Update Logs
disabled = true
is_visible = false
search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed by _time span=1d



### ESCU RESPONSE TASKS ###

[ESCU - All backup logs for host - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - All backup logs for host - Response Task
description = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully.
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.analytic_story = ["Monitor Backup Solution"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | search `netbackup` dest=$dest$

[ESCU - Amazon EKS Kubernetes activity by src ip - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Amazon EKS Kubernetes activity by src ip - Response Task
description = This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address
action.escu.creation_date = 2020-04-13
action.escu.modification_date = 2020-04-13
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `aws_cloudwatchlogs_eks` |rename sourceIPs{} as src_ip |search src_ip=$src_ip$ | stats count min(_time) as firstTime max(_time) as lastTime values(user.username) values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip

[ESCU - AWS Investigate Security Hub alerts by dest - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - AWS Investigate Security Hub alerts by dest - Response Task
description = This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id).
action.escu.creation_date = 2020-06-08
action.escu.modification_date = 2020-06-08
action.escu.analytic_story = ["Cloud Compute Instance", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "AWS Suspicious Provisioning Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id).
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `aws_securityhub_firehose` "findings{}.Resources{}.Type"=AWSEC2Instance | rex field=findings{}.Resources{}.Id .*instance/(?<instance>.*)| rename instance as dest| search dest = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation |  table dest Title ProductArn Description FirstObservedAt RecordState Remediation

[ESCU - AWS Investigate User Activities By AccessKeyId - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - AWS Investigate User Activities By AccessKeyId - Response Task
description = This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific credentials.
action.escu.creation_date = 2018-06-08
action.escu.modification_date = 2018-06-08
action.escu.analytic_story = ["AWS Cross Account Activity"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific credentials.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` | rename userIdentity.accessKeyId as accessKeyId| search accessKeyId=$accessKeyId$ | spath output=user path=userIdentity.arn  | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage

[ESCU - AWS Investigate User Activities By ARN - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - AWS Investigate User Activities By ARN - Response Task
description = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information.
action.escu.creation_date = 2019-04-30
action.escu.modification_date = 2019-04-30
action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Unusual AWS EC2 Modifications", "Suspicious Cloud User Activities", "AWS Suspicious Provisioning Activities", "Suspicious Cloud Instance Activities", "AWS Security Hub Alerts", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` | search user=$user$| table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType

[ESCU - AWS Network ACL Details from ID - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - AWS Network ACL Details from ID - Response Task
description = This search queries AWS description logs and returns all the information about a specific network ACL via network ACL ID
action.escu.creation_date = 2017-01-22
action.escu.modification_date = 2017-01-22
action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries AWS description logs and returns all the information about a specific network ACL via network ACL ID
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `aws_description` | rename id as networkAclId | search  networkAclId=$networkAclId$ | table id account_id vpc_id network_acl_entries{}.*

[ESCU - AWS Network Interface details via resourceId - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - AWS Network Interface details via resourceId - Response Task
description = This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface.
action.escu.creation_date = 2018-05-07
action.escu.modification_date = 2018-05-07
action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `aws_config` resourceId=$resourceId$ | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId  configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp

[ESCU - AWS S3 Bucket details via bucketName - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - AWS S3 Bucket details via bucketName - Response Task
description = This search queries AWS configuration logs and returns the information about a specific S3 bucket. The information returned includes the time the S3 bucket was created, the resource ID, the region it belongs to, the value of action performed, AWS account ID, and configuration values of the access-control lists associated with the bucket.
action.escu.creation_date = 2018-06-26
action.escu.modification_date = 2018-06-26
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries AWS configuration logs and returns the information about a specific S3 bucket. The information returned includes the time the S3 bucket was created, the resource ID, the region it belongs to, the value of action performed, AWS account ID, and configuration values of the access-control lists associated with the bucket.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `aws_config` | rename resourceId as bucketName |search bucketName=$bucketName$ | table resourceCreationTime bucketName vendor_region action aws_account_id supplementaryConfiguration.AccessControlList

[ESCU - GCP Kubernetes activity by src ip - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - GCP Kubernetes activity by src ip - Response Task
description = This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address
action.escu.creation_date = 2020-04-13
action.escu.modification_date = 2020-04-13
action.escu.analytic_story = ["Kubernetes Scanning Activity"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `google_gcp_pubsub_message` | rename data.protoPayload.requestMetadata.callerIp as src_ip | search src_ip =$src_ip$ | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name data.resource.type

[ESCU - Get All AWS Activity From City - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get All AWS Activity From City - Response Task
description = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.creation_date = 2018-03-19
action.escu.modification_date = 2018-03-19
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` | iplocation sourceIPAddress | search City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode

[ESCU - Get All AWS Activity From Country - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get All AWS Activity From Country - Response Task
description = This search retrieves all the activity from a specific country and will create a table containing the time, country, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.creation_date = 2018-03-19
action.escu.modification_date = 2018-03-19
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search retrieves all the activity from a specific country and will create a table containing the time, country, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` | iplocation sourceIPAddress | search Country=$Country$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip, awsRegion, eventName, errorCode

[ESCU - Get All AWS Activity From IP Address - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get All AWS Activity From IP Address - Response Task
description = This search retrieves all the activity from a specific IP address and will create a table containing the time, ARN, username, the type of user, the IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.creation_date = 2018-03-19
action.escu.modification_date = 2018-03-19
action.escu.analytic_story = ["AWS Network ACL Activity", "AWS Suspicious Provisioning Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Cloud Instance Activities", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search retrieves all the activity from a specific IP address and will create a table containing the time, ARN, username, the type of user, the IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` | iplocation sourceIPAddress | search src_ip=$src_ip$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, awsRegion, eventName, errorCode

[ESCU - Get All AWS Activity From Region - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get All AWS Activity From Region - Response Task
description = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.creation_date = 2018-03-19
action.escu.modification_date = 2018-03-19
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` | iplocation sourceIPAddress | search Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode

[ESCU - Get Backup Logs For Endpoint - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Backup Logs For Endpoint - Response Task
description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week.
action.escu.creation_date = 2017-09-14
action.escu.modification_date = 2017-09-14
action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `netbackup` COMPUTERNAME=$dest$ | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature

[ESCU - Get Certificate logs for a domain - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Certificate logs for a domain - Response Task
description = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers.
action.escu.creation_date = 2019-04-29
action.escu.modification_date = 2019-04-29
action.escu.analytic_story = ["Common Phishing Frameworks"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*$domain$  by All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash | `drop_dm_object_name(All_Certificates)` | `drop_dm_object_name(SSL)` | rename ssl_subject_common_name as domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`

[ESCU - Get DNS Server History for a host - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get DNS Server History for a host - Response Task
description = While investigating any detections it is important to understand which and how many DNS servers a host has connected to in the past. This search uses data that is tagged as DNS and gives you a count and list of DNS servers that a particular host has connected to the previous 24 hours.
action.escu.creation_date = 2017-11-09
action.escu.modification_date = 2017-11-09
action.escu.analytic_story = ["AWS Network ACL Activity", "DNS Hijacking", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Host Redirection", "Prohibited Traffic Allowed or Protocol Mismatch", "Suspicious AWS Traffic", "Suspicious DNS Traffic", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = While investigating any detections it is important to understand which and how many DNS servers a host has connected to in the past. This search uses data that is tagged as DNS and gives you a count and list of DNS servers that a particular host has connected to the previous 24 hours.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | search tag=dns src_ip=$src_ip$ dest_port=53 | streamstats time_window=1d count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count | sort -count

[ESCU - Get DNS traffic ratio - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get DNS traffic ratio - Response Task
description = This search calculates the ratio of DNS traffic originating and coming from a host to a list of DNS servers over the last 24 hours. A high value of this ratio could be very useful to quickly understand if a src_ip (host) is sending a high volume of data out via port 53, could be an indicator of data exfiltration via DNS.  
action.escu.creation_date = 2017-11-09
action.escu.modification_date = 2017-11-09
action.escu.analytic_story = ["AWS Network ACL Activity", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Suspicious AWS Traffic", "Suspicious DNS Traffic", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search calculates the ratio of DNS traffic originating and coming from a host to a list of DNS servers over the last 24 hours. A high value of this ratio could be very useful to quickly understand if a src_ip (host) is sending a high volume of data out via port 53, could be an indicator of data exfiltration via DNS.  
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest| `drop_dm_object_name(All_Traffic)` | rename src as src_ip | rename dest as dest_ip | search src_ip=$src_ip$ | search dest_ip = $dest_ip | eval ratio = (bytes_out/bytes_in) | table ratio

[ESCU - Get EC2 Instance Details by instanceId - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get EC2 Instance Details by instanceId - Response Task
description = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field
action.escu.creation_date = 2018-02-12
action.escu.modification_date = 2018-02-12
action.escu.analytic_story = ["AWS Cryptomining", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "Unusual AWS EC2 Modifications", "AWS Security Hub Alerts"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `aws_description` | dedup id sortby -_time |rename id as instanceId|  search instanceId=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC

[ESCU - Get EC2 Launch Details - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get EC2 Launch Details - Response Task
description = This search returns some of the launch details for a EC2 instance.
action.escu.creation_date = 2018-03-12
action.escu.modification_date = 2018-03-12
action.escu.analytic_story = ["AWS Cryptomining", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "AWS Security Hub Alerts"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search returns some of the launch details for a EC2 instance.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` dest=$dest$ |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId as dest, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture as architecture, responseElements.instancesSet.items{}.keyName as keyName | table arn, awsRegion, dest, architecture, privateIpAddress, amiID, keyName

[ESCU - Get Email Info - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Email Info - Response Task
description = This search returns all the information Splunk might have collected a specific email message over the last 2 hours.
action.escu.creation_date = 2017-11-09
action.escu.modification_date = 2017-11-09
action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search returns all the information Splunk might have collected a specific email message over the last 2 hours.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | from datamodel Email.All_Email | search message_id=$message_id$

[ESCU - Get Emails From Specific Sender - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Emails From Specific Sender - Response Task
description = This search returns all the emails from a specific sender over the last 24 and next hours.
action.escu.creation_date = 2017-11-09
action.escu.modification_date = 2017-11-09
action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails", "Web Fraud Detection"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search returns all the emails from a specific sender over the last 24 and next hours.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | from datamodel Email.All_Email | search src_user=$src_user$

[ESCU - Get First Occurrence and Last Occurrence of a MAC Address - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get First Occurrence and Last Occurrence of a MAC Address - Response Task
description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network.
action.escu.creation_date = 2017-09-13
action.escu.modification_date = 2017-09-13
action.escu.analytic_story = ["Asset Tracking"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Network_Sessions"]
action.escu.eli5 = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`

[ESCU - Get History Of Email Sources - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get History Of Email Sources - Response Task
description = This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source.
action.escu.creation_date = 2019-02-21
action.escu.modification_date = 2019-02-21
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "SamSam Ransomware"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Email"]
action.escu.eli5 = This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = |tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) as recepient  min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email by All_Email.src |`drop_dm_object_name(All_Email)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search src=$src$

[ESCU - Get Logon Rights Modifications For Endpoint - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Logon Rights Modifications For Endpoint - Response Task
description = This search allows you to retrieve any modifications to logon rights associated with a specific host.
action.escu.creation_date = 2017-09-12
action.escu.modification_date = 2017-09-12
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search allows you to retrieve any modifications to logon rights associated with a specific host.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `wineventlog_security` (signature_id=4718 OR signature_id=4717) dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature

[ESCU - Get Logon Rights Modifications For User - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Logon Rights Modifications For User - Response Task
description = This search allows you to retrieve any modifications to logon rights for a specific user account.
action.escu.creation_date = 2019-02-27
action.escu.modification_date = 2019-02-27
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search allows you to retrieve any modifications to logon rights for a specific user account.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `wineventlog_security` (signature_id=4718 OR signature_id=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature

[ESCU - Get Notable History - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Notable History - Response Task
description = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation.
action.escu.creation_date = 2017-09-20
action.escu.modification_date = 2017-09-20
action.escu.analytic_story = ["AWS Cross Account Activity", "AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "ColdRoot MacOS RAT", "Collection and Staging", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Cloud Authentication Activities", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual AWS EC2 Modifications", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse", "Data Exfiltration", "F5 TMUI RCE CVE-2020-5902", "Detect Zerologon Attack", "GCP Cross Account Activity", "Kubernetes Sensitive Object Access Activity", "Kubernetes Sensitive Role Activity", "Ransomware Cloud", "Ryuk Ransomware", "Suspicious Cloud Provisioning Activities", "Suspicious GCP Storage Activities", "Windows DNS SIGRed CVE-2020-1350", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | search `notable` | search dest=$dest$ | table _time, dest, rule_name, owner, priority, severity, status_description

[ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task
description = This search returns the information of the users that sent emails to the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, and from `redhat@gmail.com`.
action.escu.creation_date = 2018-06-14
action.escu.modification_date = 2018-06-14
action.escu.analytic_story = ["Hidden Cobra Malware"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Email"]
action.escu.eli5 = This search returns the information of the users that sent emails to the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, and from `redhat@gmail.com`.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | from datamodel Email.All_Email | search recipient=misswang8107@gmail.com OR src_user=redhat@gmail.com | stats count earliest(_time) as firstTime, latest(_time) as lastTime values(dest) values(src) by src_user recipient | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`

[ESCU - Get Parent Process Info - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Parent Process Info - Response Task
description = This search queries the Endpoint data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest
action.escu.creation_date = 2019-02-28
action.escu.modification_date = 2019-02-28
action.escu.analytic_story = ["Collection and Staging", "DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Phishing Payloads", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search queries the Endpoint data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")` | search  parent_process_name= $parent_process_name$ |search dest = $dest$ | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`

[ESCU - Get Process File Activity - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Process File Activity - Response Task
description = This search returns the file activity for a specific process on a specific endpoint
action.escu.creation_date = 2019-11-06
action.escu.modification_date = 2019-11-06
action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Zoom Child Processes"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search returns the file activity for a specific process on a specific endpoint
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` values(Filesystem.file_name) as file_name values(Filesystem.dest) as dest, values(Filesystem.process_name) as process_name from datamodel=Endpoint.Filesystem by Filesystem.dest Filesystem.process_name Filesystem.file_path, Filesystem.action, _time | `drop_dm_object_name(Filesystem)`  | search dest=$dest$  | search process_name=$process_name$ | table _time, process_name, dest, action, file_name, file_path

[ESCU - Get Process Info - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Process Info - Response Task
description = This search queries the Endpoint data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address.
action.escu.creation_date = 2019-04-01
action.escu.modification_date = 2019-04-01
action.escu.analytic_story = ["AWS Network ACL Activity", "Collection and Staging", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search queries the Endpoint data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")` | search  process_name= $process_name$ | search dest = $dest$ | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`

[ESCU - Get Process Information For Port Activity - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Process Information For Port Activity - Response Task
description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host.
action.escu.creation_date = 2019-04-01
action.escu.modification_date = 2019-04-01
action.escu.analytic_story = ["AWS Network ACL Activity", "DHS Report TA18-074A", "Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious AWS Traffic", "Use of Cleartext Protocols", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search dest=$dest$ | join dest type=inner [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports by Ports.process_id Ports.src Ports.dest_port | `drop_dm_object_name(Ports)` | search dest_port=$dest_port$ | rename src as dest]

[ESCU - Get Process Responsible For The DNS Traffic - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Process Responsible For The DNS Traffic - Response Task
description = While investigating, an analyst will want to know what process and parent_process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of `dest` in the search to get specific details on the process responsible for creating the DNS traffic.
action.escu.creation_date = 2019-04-01
action.escu.modification_date = 2019-04-01
action.escu.analytic_story = ["AWS Network ACL Activity", "Brand Monitoring", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Suspicious AWS Traffic", "Suspicious DNS Traffic", "Command And Control"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = While investigating, an analyst will want to know what process and parent_process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of `dest` in the search to get specific details on the process responsible for creating the DNS traffic.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.parent_process Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search dest = $dest$ | join dest type=inner [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=53 by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest]

[ESCU - Get Sysmon WMI Activity for Host - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Sysmon WMI Activity for Host - Response Task
description = This search queries Sysmon WMI events for the host of interest.
action.escu.creation_date = 2018-10-23
action.escu.modification_date = 2018-10-23
action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search queries Sysmon WMI events for the host of interest.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `sysmon` EventCode>18 EventCode<22 | rename host as dest | search dest=$dest$| table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter

[ESCU - Get Web Session Information via session id - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Get Web Session Information via session id - Response Task
description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code.
action.escu.creation_date = 2018-10-08
action.escu.modification_date = 2018-10-08
action.escu.analytic_story = ["Web Fraud Detection"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `stream_http` session_id  = $session_id$ | stats values(url) values(http_user_agent) by src_ip status

[ESCU - Investigate AWS activities via region name - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate AWS activities via region name - Response Task
description = This search lists all the user activities logged by CloudTrail for a specific region in question and will create a table of the values of parameters requested, the type of the event and the response from the AWS API by each user
action.escu.creation_date = 2018-02-09
action.escu.modification_date = 2018-02-09
action.escu.analytic_story = ["AWS Cryptomining", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search lists all the user activities logged by CloudTrail for a specific region in question and will create a table of the values of parameters requested, the type of the event and the response from the AWS API by each user
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` vendor_region=$vendor_region$| rename requestParameters.instancesSet.items{}.instanceId as instanceId | stats values(eventName) by user instanceId vendor_region

[ESCU - Investigate AWS User Activities by user field - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate AWS User Activities by user field - Response Task
description = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information.
action.escu.creation_date = 2018-03-12
action.escu.modification_date = 2018-03-12
action.escu.analytic_story = ["AWS User Monitoring", "Suspicious Cloud Authentication Activities"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `cloudtrail` user=$user$ | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType 

[ESCU - Investigate Failed Logins for Multiple Destinations - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Failed Logins for Multiple Destinations - Response Task
description = This search returns failed logins to multiple destinations by user.
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
action.escu.analytic_story = ["Credential Dumping"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search returns failed logins to multiple destinations by user.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app  from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search user=$user$

[ESCU - Investigate Network Traffic From src ip - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Network Traffic From src ip - Response Task
description = This search allows you to find all the network traffic from a specific IP address.
action.escu.creation_date = 2018-06-15
action.escu.modification_date = 2018-06-15
action.escu.analytic_story = ["ColdRoot MacOS RAT", "Splunk Enterprise Vulnerability CVE-2018-11409"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search allows you to find all the network traffic from a specific IP address.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | from datamodel Network_Traffic.All_Traffic | search src_ip=$src_ip$

[ESCU - Investigate Okta Activity by app - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Okta Activity by app - Response Task
description = This search returns all okta events associated with a specific app
action.escu.creation_date = 2020-04-02
action.escu.modification_date = 2020-04-02
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search returns all okta events associated with a specific app
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `okta` app=$app$ | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason

[ESCU - Investigate Okta Activity by IP Address - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Okta Activity by IP Address - Response Task
description = This search returns all okta events from a specific IP address.
action.escu.creation_date = 2020-04-02
action.escu.modification_date = 2020-04-02
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search returns all okta events from a specific IP address.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `okta` src_ip={src_ip} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason

[ESCU - Investigate Pass the Hash Attempts - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Pass the Hash Attempts - Response Task
description = This search hunts for dumped NTLM hashes used for pass the hash.
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
action.escu.analytic_story = ["Credential Dumping"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search hunts for dumped NTLM hashes used for pass the hash.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate | stats count earliest(_time) as first_login latest(_time) as last_login by src_user dest | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | search dest=$dest$

[ESCU - Investigate Pass the Ticket Attempts - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Pass the Ticket Attempts - Response Task
description = This search hunts for dumped kerberos ticket from LSASS memory.
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
action.escu.analytic_story = ["Credential Dumping"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search hunts for dumped kerberos ticket from LSASS memory.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?<new_user>[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest| search dest=$dest$ | where sum_count/max_count!=2 | rename new_user AS user 

[ESCU - Investigate Previous Unseen User - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Previous Unseen User - Response Task
description = This search returns previous unseen user, which didn't log in for 30 days.
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
action.escu.analytic_story = ["Credential Dumping"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search returns previous unseen user, which didn't log in for 30 days.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search dest=$dest$

[ESCU - Investigate Successful Remote Desktop Authentications - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Successful Remote Desktop Authentications - Response Task
description = This search returns the source, destination, and user for all successful remote-desktop authentications. A successful authentication after a brute-force attack on a destination machine is suspicious behavior. 
action.escu.creation_date = 2018-12-14
action.escu.modification_date = 2018-12-14
action.escu.analytic_story = ["Hidden Cobra Malware", "Active Directory Lateral Movement", "SamSam Ransomware"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Authentication"]
action.escu.eli5 = This search returns the source, destination, and user for all successful remote-desktop authentications. A successful authentication after a brute-force attack on a destination machine is suspicious behavior. 
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")` | search dest=$dest$ | table firstTime lastTime src src_nt_domain dest user app count | sort count

[ESCU - Investigate Suspicious Strings in HTTP Header - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Suspicious Strings in HTTP Header - Response Task
description = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest.
action.escu.creation_date = 2017-10-20
action.escu.modification_date = 2017-10-20
action.escu.analytic_story = ["Apache Struts Vulnerability"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest.
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `stream_http` | search src_ip=$src_ip$ | search dest_ip=$dest_ip$ | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?<suspicious_strings>cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False")  | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url

[ESCU - Investigate User Activities In Okta - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate User Activities In Okta - Response Task
description = This search returns all okta events by a specific user
action.escu.creation_date = 2020-04-02
action.escu.modification_date = 2020-04-02
action.escu.analytic_story = ["Suspicious Okta Activity"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = []
action.escu.eli5 = This search returns all okta events by a specific user
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = `okta` user=$user$ | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason

[ESCU - Investigate Web POSTs From src - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Investigate Web POSTs From src - Response Task
description = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. 
action.escu.creation_date = 2018-12-06
action.escu.modification_date = 2018-12-06
action.escu.analytic_story = ["Apache Struts Vulnerability"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Web"]
action.escu.eli5 = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. 
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search http_method, "POST" | search src=$src$



### END ESCU RESPONSE TASKS ###