splunk-security_content/dist/escu/default/savedsearches.conf

#############
# Automatically generated by generator.py in splunk/security_content
# On Date: 2021-12-15T02:51:17 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############

### ESCU DETECTIONS ###

[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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1560.001", "T1560"]}
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 = 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 7z.exe may be used.
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 = []
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = archive process $process_name$ with suspicious cmdline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1560.001", "T1560"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
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 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.notable.param.rule_title = 7zip CommandLine To SMB Share 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 (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 - 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 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen City
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
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 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen Country
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 1"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
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 1"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen IP Address
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"]
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 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen Region
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["PR.DS", "PR.AC", "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
action.escu.creation_date = 2021-02-22
action.escu.modification_date = 2021-02-22
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 | search 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 userIdentity.principalId 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 search looks for AWS CloudTrail events where a user A who has already permission to create access keys, makes an API call to create access keys 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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["PR.DS", "PR.AC", "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 create access keys, makes an API call to create access keys 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 = 2021-07-19
action.escu.modification_date = 2021-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS CreateAccessKey - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ is attempting to create access keys for $requestParameters.userName$ from this IP $src$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 63}]
action.risk.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 CreateAccessKey - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "cis20": ["CIS 13"], "confidence": 90, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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| search userIdentity.userName!=requestParameters.userName  |  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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["PR.DS", "PR.AC", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Privilege Escalation"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 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 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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"}, {"threat_object_field": "requestedAccountId", "threat_object_type": "other"}]
action.risk.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 16"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"], "observable": [{"name": "requestingAccountId", "role": ["Attacker"], "type": "Other"}, {"name": "requestedAccountId", "role": ["Victim"], "type": "Other"}]}
schedule_window = auto
alert.digest_mode = 1
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 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 = {"mitre_attack": ["T1486"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 50, "mitre_attack": ["T1486"], "observable": [{"name": "userIdentity.principalId", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 = {"mitre_attack": ["T1486"]}
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 = bucket with S3 encryption
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 with KMS keys performing encryption S3 - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}, {"threat_object_field": "dest_file", "threat_object_type": "file"}]
action.risk.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"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 30, "mitre_attack": ["T1486"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "dest_file", "role": ["Target"], "type": "File"}]}
schedule_window = auto
alert.digest_mode = 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 bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime 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 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "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 = 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 High - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = [{"threat_object_field": "image", "threat_object_type": "system"}]
action.risk.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "image", "role": ["Victim"], "type": "System"}]}
schedule_window = auto
action.notable = 1
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, user, 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "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 = 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 Low Informational Unknown - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Vulnerabilities with severity high found in repository $repositoryName$
action.risk.param._risk = [{"threat_object_field": "repositoryName", "threat_object_type": "system"}]
action.risk.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 Low Informational Unknown - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 10, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "repositoryName", "role": ["Victim"], "type": "System"}]}
schedule_window = auto
action.notable = 1
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 Low Informational Unknown
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 IN (LOW, INFORMATIONAL, UNKNWON) | 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, user, 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "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 = 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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = [{"threat_object_field": "image", "threat_object_type": "system"}]
action.risk.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "image", "role": ["Victim"], "type": "System"}]}
schedule_window = auto
action.notable = 1
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 Medium
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=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, user, 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}, {"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = AWS ECR Container Upload Outside Business Hours
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=PutImage date_hour>=20 OR date_hour<8 NOT (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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}, {"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = AWS ECR Container Upload Unknown User
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=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 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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = AWS EKS Kubernetes cluster sensitive object access
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 = `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 - 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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, "context": ["Source:Cloud Data", "Scope:Inbound", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 eventName 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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580"]}
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", "Splunk Security Analytics for AWS"]
action.escu.providing_technologies = []
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.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"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Blocked", "Stage:Discovery"], "impact": 20, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}, {"name": "userIdentity.arn", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580", "T1110"]}
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", "Splunk Security Analytics for AWS"]
action.escu.providing_technologies = []
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.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"], "confidence": 70, "context": ["Source:Cloud Data", "Scope:Inbound", "Stage:Credential Access", "Other:Policy Violation"], "impact": 40, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580", "T1110"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
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", "Splunk Security Analytics for AWS"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has deleted AWS Policies from IP address $src$ by executing the following command $eventName$
action.risk.param._risk = [{"risk_object_field": "src", "risk_object_type": "system", "risk_score": 10}, {"risk_object_field": "user_arn", "risk_object_type": "user", "risk_score": 10}]
action.risk.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 Delete Policy - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Stage:Execution", "Other:Policy Violation"], "impact": 20, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId userIdentity.arn | `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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
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", "Splunk Security Analytics for AWS"]
action.escu.providing_technologies = []
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.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"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Stage:Execution"], "impact": 10, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}, {"name": "group_name", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1069.003", "T1098", "T1069"]}
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", "Splunk Security Analytics for AWS"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = User $user_arn$ has sucessfully deleted mulitple groups $group_deleted$ 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_deleted", "risk_object_type": "user", "risk_score": 5}]
action.risk.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 Successful Group Deletion - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Stage:Execution"], "impact": 10, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1069.003", "T1098", "T1069"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}, {"name": "group_deleted", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 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 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.DP", "DE.AE"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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": "userName", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "requestParameters.cidrBlock", "risk_object_type": "system", "risk_score": 48}]
action.risk.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 11"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Stage:Execution", "Stage:Defense Evasion"], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "userName", "role": ["Victim"], "type": "User"}, {"name": "requestParameters.cidrBlock", "role": ["Victim"], "type": "IP Address"}]}
schedule_window = auto
alert.digest_mode = 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 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 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.DP", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 11"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:External", "Stage:Execution"], "impact": 10, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007", "T1562"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 userName userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_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 = {"mitre_attack": ["T1078"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}, {"threat_object_field": "recipientAccountId", "threat_object_type": "other"}]
action.risk.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"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Stage:Credential Access", "Stage:Privilege Escalation"], "impact": 80, "mitre_attack": ["T1078"], "observable": [{"name": "sourceIPAddress", "role": ["Attacker"], "type": "IP Address"}, {"name": "recipientAccountId", "role": ["Victim", "Target"], "type": "Other"}]}
schedule_window = auto
alert.digest_mode = 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 = {"mitre_attack": ["T1078"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Stage:Credential Access"], "impact": 80, "mitre_attack": ["T1078"], "observable": [{"name": "sourceIPAddress", "role": ["Attacker"], "type": "IP Address"}, {"name": "userIdentity.principalId", "role": ["Victim", "Target"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["PR.DS", "PR.AC", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = From IP address $sourceIPAddress$, user agent $userAgent$ 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.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 13"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Stage:Credential Access", "Stage:Privilege Escalation"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["PR.DS", "PR.AC", "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 = 2021-07-19
action.escu.modification_date = 2021-07-19
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS UpdateLoginProfile - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = From IP address $sourceIPAddress$, 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.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 13"], "confidence": 60, "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.003", "T1136"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_arn", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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| search userIdentity.userName!=requestParameters.userName  |  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 - 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Abnormally High AWS Instances Launched by User
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=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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Abnormally High AWS Instances Launched by User - MLTK
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=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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Suspicious AWS EC2 Activities"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Abnormally High AWS Instances Terminated by User
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=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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Suspicious AWS EC2 Activities"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Abnormally High AWS Instances Terminated by User - MLTK
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=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 - 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Cloud User Activities"]
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.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"], "cis20": ["CIS 16"], "confidence": 50, "context": ["Source:Cloud Data", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "DE.CM", "PR.AC"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Cloud Instance Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious Cloud Instance Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 16"], "confidence": 50, "context": ["Source:Cloud Data", "Scope:Inbound", "Outcome:Allowed", "Stage:Execution", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["DE.DP", "DE.CM", "PR.AC"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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 - Access LSASS Memory for Dump Creation - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect memory dumping of the LSASS process.
action.escu.mappings = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect memory dumping of the LSASS process.
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 = []
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}, {"threat_object_field": "TargetImage", "threat_object_type": "process"}]
action.risk.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 6", "CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "TargetImage", "role": ["Target"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = Detect memory dumping of the LSASS process.
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 Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = admin or power user may used this series of command.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 10, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_name", "role": ["Process"], "type": "Process Name"}]}
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 >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.014", "T1547"]}
action.escu.data_models = []
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 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 = Active setup installer may add or modify this registry.
action.escu.creation_date = 2021-09-28
action.escu.modification_date = 2021-09-28
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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": 64}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}]
action.risk.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 80, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.014", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_value_name = "StubPath" Registry.registry_key_name = "*\\SOFTWARE\\Microsoft\\Active Setup\\Installed Components*" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1552.002", "T1552"]}
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 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 = 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 - 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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1552.002", "T1552"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 = Add DefaultUser And Password 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_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name= DefaultPassword OR Registry.registry_value_name= DefaultUserName by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = ["Endpoint"]
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 = 2021-11-25
action.escu.modification_date = 2021-11-25
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 = []
action.escu.analytic_story = ["Remcos", "Windows Defense Evasion Tactics"]
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.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": ["Remcos", "Windows Defense Evasion Tactics"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 2021-08-24
action.escu.modification_date = 2021-08-24
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ for user enumeration
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 25}]
action.risk.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": ["Active Directory Discovery"], "confidence": 50, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*[adsisearcher]*" Message = "*objectcategory=user*" Message = "*.findAll()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.007", "T1562"]}
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 = 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 = 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 = []
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 - Allow File And Printing Sharing In Firewall - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.007", "T1562"]}
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 = This 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021.001", "T1021"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This 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 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 = network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered.
action.escu.creation_date = 2021-05-26
action.escu.modification_date = 2021-05-26
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 = []
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"]
action.risk = 1
action.risk.param._risk_message = Suspicious firewall 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": 3}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 3}]
action.risk.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 10, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021.001", "T1021"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This 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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_name = "*|Action=Allow|*" Registry.registry_value_name = "*|Dir=In|*" Registry.registry_value_name = "*|Profile=Public|*" Registry.registry_value_name = "*|LPort=*" by Registry.registry_path Registry.registry_key_name Registry.user Registry.registry_value_name Registry.dest | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021.001", "T1021"]}
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 = []
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 10, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021.001", "T1021"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.007", "T1562"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.007", "T1562"]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548"]}
action.escu.data_models = []
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 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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Ransomware"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" Registry.registry_value_name = ConsentPromptBehaviorAdmin Registry.registry_value_data = "0x00000000" 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)` | `allow_operation_with_consent_admin_filter`

[ESCU - Amazon EKS Kubernetes Pod scan detection - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API
action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
action.escu.data_models = []
action.escu.eli5 = This search provides detection information on unauthenticated requests against Kubernetes' Pods API
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 = []
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"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API
action.notable.param.rule_title = Amazon EKS Kubernetes Pod 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 = `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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
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 = []
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"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS
action.notable.param.rule_title = Amazon EKS 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 = `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 - 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 = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1560.001", "T1560"]}
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 = 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 = 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 = 2021-04-22
action.escu.modification_date = 2021-04-22
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 = []
action.escu.analytic_story = ["Cobalt Strike", "NOBELIUM Group"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Cobalt Strike", "NOBELIUM Group"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 80, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1560.001", "T1560"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Anomalous usage of 7zip
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 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 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 = 2021-03-01
action.escu.modification_date = 2021-03-01
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 = []
action.escu.analytic_story = ["Malicious PowerShell", "Ingress Tool Transfer", "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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Malicious PowerShell", "Ingress Tool Transfer", "Log4Shell CVE-2021-44228"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Exploitation"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 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 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 = 2021-03-01
action.escu.modification_date = 2021-03-01
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 = []
action.escu.analytic_story = ["Malicious PowerShell", "HAFNIUM Group", "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 $user$. 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Malicious PowerShell", "HAFNIUM Group", "Ingress Tool Transfer"], "confidence": 70, "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 = This search looks for execution of commonly used attacker tools on an endpoint.
action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1036.005", "T1036", "T1003", "T1595"], "nist": ["ID.AM", "PR.DS"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for execution of commonly used attacker tools on an endpoint.
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. 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 usually generated via logs that report process tracking in your Windows audit settings.
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 = []
action.escu.analytic_story = ["Monitor for Unauthorized Software", "XMRig", "SamSam Ransomware", "Unusual Processes"]
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}, {"threat_object_field": "parent_process", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 2"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Exploitation", "Stage:Recon", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1036.005", "T1036", "T1003", "T1595"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process", "Attacker"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for execution of commonly used attacker tools on an endpoint.
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 = Attempt To Add Certificate To Untrusted Store
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004", "T1553"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Attempt To Add Certificate To Untrusted Store
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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 3", "CIS 5", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004", "T1553"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = Attempt To Add Certificate To Untrusted Store
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.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 = This search looks for attempts to stop security-related services on the endpoint.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for attempts to stop security-related services on the endpoint.
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 = None identified. Attempts to disable security-related services should be identified and understood.
action.escu.creation_date = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
action.escu.analytic_story = ["Disabling Security Tools", "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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Disabling Security Tools", "Trickbot"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for attempts to stop security-related services on the endpoint.
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.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 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "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 = 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 = None identified.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
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 = []
action.escu.analytic_story = ["Credential Dumping", "DarkSide 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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Credential Dumping", "DarkSide Ransomware"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.002", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 `process_reg` OR `process_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.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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1552.002", "T1552"]}
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 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 = 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 - 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 = []
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": 63}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1552.002", "T1552"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Registry.registry_key_name=AutoAdminLogon AND Registry.registry_value_name=1 by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `auto_admin_logon_registry_entry_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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
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 = 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. Tune based on parent process names.
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Impact"], "impact": 100, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["BITS Jobs"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197", "T1105"]}
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 = 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 = Limited false positives, however it may be required to filter based on parent process name or network connection.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
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 = []
action.escu.analytic_story = ["Ingress Tool Transfer", "BITS Jobs", "DarkSide 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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197", "T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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=*transfer* 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)` | `bitsadmin_download_file_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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = 2021-09-16
action.escu.modification_date = 2021-09-16
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 = []
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}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.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 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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_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 ("*\\system32\\*", "*\\syswow64\\*") Filesystem.file_name="*.bat" 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  | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Impact"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1059"]}
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 = 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 chcp.com may be used.
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 = []
action.escu.analytic_story = ["IcedID"]
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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* 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003", "T1059"]}
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 = 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 may be high based on legitimate scripted code in any environment. 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 - 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 = []
action.escu.analytic_story = ["IcedID", "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 $user$ attempting spawn a new process.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}]
action.risk.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 Carry Out String Command Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Log4Shell CVE-2021-44228"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution"], "cve": ["CVE-2021-44228"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003", "T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = CMD Carry Out String Command 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` 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 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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059", "T1059.003", "T1543.003", "T1543"]}
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 = 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. It is possible filtering may be required to ensure fidelity.
action.escu.creation_date = 2021-05-20
action.escu.modification_date = 2021-05-20
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Cobalt Strike"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059", "T1059.003", "T1543.003", "T1543"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 - CMLUA Or CMSTPLUA UAC Bypass - Rule]
action.escu = 0
action.escu.enabled = 1
description = This 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.003"]}
action.escu.data_models = []
action.escu.eli5 = This 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 = []
action.escu.analytic_story = ["DarkSide Ransomware", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $Computer$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "ImageLoaded", "threat_object_type": "other"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "ImageLoaded", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This 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 Image ImageLoaded process_name Computer EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027.004", "T1027"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = csc.exe with commandline $process$ to compile .net code on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027.004", "T1027"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = CSC Net On The Fly Compilation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
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 = 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 = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
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 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 = []
action.escu.analytic_story = ["Ingress Tool Transfer", "DarkSide 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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Command and Control"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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* 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
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 = 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 = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
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 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 = []
action.escu.analytic_story = ["Ingress Tool Transfer", "DarkSide 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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Command and Control"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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* by Processes.dest Processes.user Processes.original_file_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)` | `certutil_download_with_verifyctl_and_split_arguments_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1140"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Deobfuscate-Decode Files or Information"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1140"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 - 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 = {"kill_chain_phases": ["Installation"]}
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 = 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 = 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 = 2021-01-26
action.escu.modification_date = 2021-01-26
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Cloud Federated Credential Abuse"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 90, "kill_chain_phases": ["Installation"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `certutil_exe_certificate_extraction_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 techninique 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 command to the compromised host.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.001", "T1546"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is developed to detect suspicious registry modification to change the default file association of windows to malicious payload. This techninique 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 command 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 = 2021-09-27
action.escu.modification_date = 2021-09-27
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 100, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.001", "T1546"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 techninique 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 command 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
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 = 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 = 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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Impact"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1033"]}
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 = 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 = 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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
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 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe.
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 = 2020-03-16
action.escu.modification_date = 2020-03-16
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 = []
action.escu.analytic_story = ["Windows Privilege Escalation"]
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": ["Windows Privilege Escalation"], "cis20": ["CIS 5", "CIS 8"], "cve": ["CVE-2018-8440"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "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 - Circle CI Disable Security Job - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for disable security job in CircleCI pipeline.
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1554"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for disable security job in CircleCI pipeline.
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1554"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search looks for disable security job in CircleCI pipeline.
action.notable.param.rule_title = Circle CI Disable Security Job
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 = `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 = This search looks for disable security step in CircleCI pipeline.
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1554"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for disable security step in CircleCI pipeline.
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1554"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search looks for disable security step in CircleCI pipeline.
action.notable.param.rule_title = Circle CI Disable Security Step
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 = `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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.004", "T1070"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Impact"], "impact": 100, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.004", "T1070"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - 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 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "PR.DS"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"]
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", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"], "cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1204"]}
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 = 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 = Operators can execute third party tools using these parameters.
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 Common Exec Parameter - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 100, "kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1204"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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` 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 != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" 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)` | `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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1543"]}
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 = []
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 $dest$ executing known Clop Ransomware service names.
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 100, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1543"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
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 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 - Cloud API Calls From Previously Unseen User Roles - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for new commands from each user role.
action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for new commands from each user role.
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Recon", "Stage:Execution"], "impact": 60, "mitre_attack": ["T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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 1"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["ID.AM"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Recon", "Stage:Execution"], "impact": 30, "mitre_attack": ["T1078.004", "T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 12"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "user"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 = This search looks for cloud compute instances being created with previously unseen image IDs.
action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search looks for cloud compute instances being created with previously unseen image IDs.
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 60, "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 = Find EC2 instances being created with previously unseen instance types.
action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = Find EC2 instances being created with previously unseen instance types.
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 50, "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 1"], "mitre_attack": ["T1078.004", "T1078"], "nist": ["ID.AM"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Cloud Instance Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is modifying an instance $dest$ 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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "mitre_attack": ["T1078.004", "T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Cloud Network ACL Activity"]
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 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src']
action.notable.param.rule_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.notable.param.rule_title = Cloud Network Access Control List Deleted
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=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 - 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 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Cloud Provisioning Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is starting or creating an instance $dest$ 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": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 30, "mitre_attack": ["T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "mitre_attack": ["T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "object", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "mitre_attack": ["T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "object_id", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 1"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "mitre_attack": ["T1078"], "nist": ["ID.AM"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "src", "role": ["Attacker"], "type": "IP Address"}, {"name": "object", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 1
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"]}
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 = 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 = 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 = 2021-09-14
action.escu.modification_date = 2021-09-14
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 = []
action.escu.analytic_story = ["FIN7"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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") 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 - 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1055"], "nist": ["PR.PT", "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 = 2021-02-22
action.escu.modification_date = 2021-02-22
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 = []
action.escu.analytic_story = ["Cobalt Strike", "Trickbot", "DarkSide Ransomware"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $Computer$ by user $user$ accessing known suspicious named pipes related to Cobalt Strike.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Cobalt Strike", "Trickbot", "DarkSide Ransomware"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1055"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Parent Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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_*, \\wkssvc*, \\DserNamePipe*, \\srvsvc_*, \\mojo.*, \\postex_*, \\status_*, \\MSSE-*, \\spoolss_*, \\win_svc*, \\ntsvcs*, \\winsock*, \\UIA_PIPE*) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, process_name, process_id process_path, PipeName | rename Computer as dest | `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 search looks for file modifications with extensions commonly used by Ransomware
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for file modifications with extensions commonly used by Ransomware
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.\
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:** Name, **Field:** Name\
1. \
1. **Label:** File Extension, **Field:** file_extension\
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 = 2020-11-09
action.escu.modification_date = 2020-11-09
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 = []
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware", "Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = A file - $file_name$ was written to disk on endpoint $dest$ by user $user$, this is indicative of a known ransomware file extension and should be reviewed immediately.
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.verbose = 0
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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The search looks for file modifications with extensions commonly used by Ransomware
action.notable.param.rule_title = Common Ransomware 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 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>\.[^\.]+)$" | `ransomware_extensions` | `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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
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 = []
action.escu.analytic_story = ["SamSam Ransomware", "Ransomware", "Ryuk Ransomware", "Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = A file - $file_name$ was written to disk on endpoint $dest$ by user $user$, this is indicative of a known ransomware note file and should be reviewed immediately.
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.verbose = 0
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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Common Ransomware Notes
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204"]}
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 = 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. The following Splunk SOAR playbook can be used to respond to this detection: Ransomware Investigate and Contain
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.002"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["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 $dest$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-40444"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - Correlation by Repository and Risk - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search correlations detections by repository and risk_score
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search correlations detections by repository and risk_score
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Correlation triggered for user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}]
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Correlation by Repository and Risk - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 100, "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search correlations detections by repository and risk_score
action.notable.param.rule_title = 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 = `signals` | 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 = This search correlations detections by user and risk_score
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search correlations detections by user and risk_score
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Dev Sec Ops"]
action.risk = 1
action.risk.param._risk_message = Correlation triggered for user $user$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 70}]
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Correlation by User and Risk - Rule
action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 100, "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1204"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search correlations detections by user and risk_score
action.notable.param.rule_title = 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 = `signals` | 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ create a remote thread to shell app process $TargetImage$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 70}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
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 Computer | `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 = Detect remote thread creation into LSASS consistent with credential dumping.
action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect remote thread creation into LSASS consistent with credential dumping.
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 = []
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 = [{"threat_object_field": "TargetImage", "threat_object_type": "other"}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}]
action.risk.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 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "TargetImage", "role": ["Other"], "type": "Other"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = Detect remote thread creation into LSASS consistent with credential dumping.
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 Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter`

[ESCU - Create local admin accounts using net exe - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the creation of local administrator accounts using net.exe .
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for the creation of local administrator accounts using net.exe .
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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for the creation of local administrator accounts using net.exe .
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 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 = This search looks for the creation or deletion of hidden shares using net.exe.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070", "T1070.005"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for the creation or deletion of hidden shares using net.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. 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 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 = []
action.escu.analytic_story = ["Hidden Cobra Malware"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070", "T1070.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for the creation or deletion of hidden shares using net.exe.
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.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 - 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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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 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}, {"threat_object_field": "parent_process", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 = This search detects the use of wmic and Powershell to create a shadow copy.
action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search detects the use of wmic and Powershell to create a shadow copy.
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 = 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 = []
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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search detects the use of wmic and Powershell to create a shadow copy.
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.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 - 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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "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 = []
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = $process_name$ was identified on endpoint $Computer$ writing $TargetFilename$ to disk. This behavior is related to dumping credentials via Task Manager.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "TargetFilename", "threat_object_type": "file name"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 6", "CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "TargetFilename", "role": ["Victim"], "type": "File Name"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 Computer, object_category, process_name, TargetFilename | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter`

[ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects credential dumping using copy command from a shadow copy.
action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search detects credential dumping using copy command from a shadow copy.
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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search detects credential dumping using copy command from a shadow copy.
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 | `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 = This search detects the creation of a symlink to a shadow copy.
action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search detects the creation of a symlink to a shadow copy.
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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search detects the creation of a symlink to a shadow copy.
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.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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
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 = 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 for Linux, you will need to ensure mapping is occurring correctly. If the EDR is not parsing the pipe bash in the command-line, modifying the analytic will be required. Add parent process name (Processes.parent_process_name) as needed to filter. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
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 = 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 `port` node.
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 = 2021-10-13
action.escu.modification_date = 2021-10-13
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
action.risk = 1
action.risk.param._risk_message = The process $process_name$ was spawned by $parent_image$ 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}, {"threat_object_field": "parent_image", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_image", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h  Processes.process_guid 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="(dllhost\.exe.{0,4}$)" | join  process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_guid Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename  dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_guid connection_to_CNC dest_port | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious DNS Traffic", "Dynamic DNS", "Command and Control", "Data Exfiltration"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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", "Command and Control", "Data Exfiltration"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_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 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004", "T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
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 = []
action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"]
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 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004", "T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_description = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.
action.notable.param.rule_title = DNS Query Length Outliers - MLTK
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 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 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
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 = []
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}, {"threat_object_field": "query", "threat_object_type": "dnsquery"}]
action.risk.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 8", "CIS 12"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 70, "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["PR.PT", "DE.AE", "DE.CM"], "observable": [{"name": "host", "role": ["Victim"], "type": "Hostname"}, {"name": "query", "role": ["Attacker"], "type": "dnsquery"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = DNS Query Length With High Standard Deviation
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 NOT DNS.message_type IN("Pointer","PTR") by DNS.query | `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 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 - 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 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"]
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", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.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 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["DNS Hijacking"]
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 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src']
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
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 = 2021-05-12
action.escu.modification_date = 2021-05-12
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 = []
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 $ComputerName$ by $User$.
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 81}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "User", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 Message= "*ShadowCopy*" (Message = "*Delete*" OR Message = "*Remove*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"]}
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 = 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 = System administrators or scripts may delete user accounts via this technique. 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 - Deleting Of Net Users - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["XMRig"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware", "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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8", "CIS 10"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 16"], "nist": ["DE.DP", "PR.AC"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 API activity from users without MFA - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect API activity from users without MFA
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` 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 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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
cron_schedule = 59 * * * *
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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src_interface', 'firstTime', 'lastTime', 'count']
action.notable.param.rule_description = ARP Poisoning has been detected on interface $src_interface$ on host $orig_host$. This may be an indication of a MITM attack.
action.notable.param.rule_title = ARP Poisoning Detected on $orig_host$
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 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 AWS API Activities From Unapproved Accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect AWS API Activities From Unapproved 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 = `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 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 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "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.
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 = 2020-05-28
action.escu.modification_date = 2020-05-28
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Cloud Authentication Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is logging into the AWS console for the first time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 30}]
action.risk.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 AWS Console Login by New User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Authentication Activities"], "cis20": ["CIS 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "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 = 2020-10-07
action.escu.modification_date = 2020-10-07
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is logging into the AWS console from City $City$ for the first time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 18}]
action.risk.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 AWS Console Login by User from New City - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"], "cis20": ["CIS 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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)` | table firstTime lastTime user City | join user  type=outer [| inputlookup previously_seen_users_console_logins | stats min(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City  userStatus 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "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 = 2020-10-07
action.escu.modification_date = 2020-10-07
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is logging into the AWS console from Country $Country$ for the first time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 42}]
action.risk.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 AWS Console Login by User from New Country - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"], "cis20": ["CIS 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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)` | table firstTime lastTime user Country | join user  type=outer [| inputlookup previously_seen_users_console_logins | stats min(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country  userStatus 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "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 = 2020-10-07
action.escu.modification_date = 2020-10-07
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"]
action.risk = 1
action.risk.param._risk_message = User $user$ is logging into the AWS console from Region $Region$ for the first time
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}]
action.risk.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 AWS Console Login by User from New Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities", "Suspicious Cloud Authentication Activities"], "cis20": ["CIS 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 1
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)` | table firstTime lastTime user Region | join user  type=outer [| inputlookup previously_seen_users_console_logins | stats min(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region  userStatus userRegion  | `detect_aws_console_login_by_user_from_new_region_filter`

[ESCU - Detect Activity Related to Pass the Hash Attacks - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1550", "T1550.002"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
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.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = The following $EventCode$ occurred on $dest$ by $user$ with Logon Type 3, which may be indicative of the pass the hash technique.
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}, {"threat_object_field": "EventCode", "threat_object_type": "other"}]
action.risk.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 Activity Related to Pass the Hash Attacks - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1550", "T1550.002"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "EventCode", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique.
action.notable.param.rule_title = Detect Activity Related to Pass the Hash Attacks
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=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION 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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 80, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"]}
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 = []
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}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 70, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 | `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 = This search detects the heap-based buffer overflow of sudoedit
action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects 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 = []
action.escu.analytic_story = ["Baron Samedit CVE-2021-3156"]
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 8", "CIS 12", "CIS 16"], "cve": ["CVE-2021-3156"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects 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` | search "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 = This search detects the heap-based buffer overflow of sudoedit
action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects the heap-based buffer overflow of sudoedit
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 = []
action.escu.analytic_story = ["Baron Samedit CVE-2021-3156"]
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 8", "CIS 12", "CIS 16"], "cve": ["CVE-2021-3156"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects the heap-based buffer overflow of sudoedit
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` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search 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 = This search detects the heap-based buffer overflow of sudoedit
action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects the heap-based buffer overflow of sudoedit
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 = []
action.escu.analytic_story = ["Baron Samedit CVE-2021-3156"]
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 8", "CIS 12", "CIS 16"], "cve": ["CVE-2021-3156"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects the heap-based buffer overflow of sudoedit
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 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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1210"], "nist": ["DE.AE", "DE.CM"]}
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 = []
action.escu.analytic_story = ["Detect Zerologon Attack"]
action.risk = 1
action.risk.param._risk_message = The following $EventCode$ occurred on $dest$ by $user$ with Logon Type 3, which may be indicative of the an account or group being changed by an anonymous account.
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}, {"threat_object_field": "EventCode", "threat_object_type": "other"}]
action.risk.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 Computer Changed with Anonymous Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Detect Zerologon Attack"], "cis20": ["CIS 6", "CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "cve": ["CVE-2020-1472"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1210"], "nist": ["DE.AE", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "EventCode", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.
action.notable.param.rule_title = Detect Computer Changed with Anonymous 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=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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"]}
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 = []
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-36934"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 = This search looks for reading lsass memory consistent with credential dumping.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for reading lsass memory consistent with credential dumping.
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 = []
action.escu.analytic_story = ["Credential Dumping", "Detect Zerologon Attack"]
action.risk = 1
action.risk.param._risk_message = The $source_image$ has attempted access to read $TargetImage$ was identified on endpoint $Computer$, this is indicative of credential dumping and should be investigated.
action.risk.param._risk = [{"threat_object_field": "source_image", "threat_object_type": "other"}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "TargetImage", "threat_object_type": "other"}]
action.risk.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 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"], "observable": [{"name": "source_image", "role": ["Victim"], "type": "Other"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "TargetImage", "role": ["Victim"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search looks for reading lsass memory consistent with credential dumping.
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 Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_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 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1566.003"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Common Phishing Frameworks"]
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 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1566.003"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 2021-06-09
action.escu.modification_date = 2021-06-09
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = The following behavior was identified and typically related to PowerShell-Empire on $ComputerName$ by $User$.
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 81}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 81}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "User", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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  (Message=*system.net.webclient* AND Message=*frombase64string*) | 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_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 16"], "mitre_attack": ["T1078", "T1078.002"], "nist": ["PR.IP"]}
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 = 2020-11-09
action.escu.modification_date = 2020-11-09
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 = []
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.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 16"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 60, "mitre_attack": ["T1078", "T1078.002"], "nist": ["PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.
action.notable.param.rule_title = Detect Excessive Account Lockouts From Endpoint
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 values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" 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 16"], "mitre_attack": ["T1078", "T1078.003"], "nist": ["PR.IP"]}
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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
action.escu.analytic_story = ["Account Monitoring and Controls"]
action.risk = 1
action.risk.param._risk_message = Multiple accounts have been locked out. Review $nodename$ and $result$ related to $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 36}, {"threat_object_field": "result", "threat_object_type": "other"}]
action.risk.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 16"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 60, "mitre_attack": ["T1078", "T1078.003"], "nist": ["PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "result", "role": ["Victim"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search detects user accounts that have been locked out a relatively high number of times in a short period.
action.notable.param.rule_title = Detect Excessive User Account Lockouts
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=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" 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. 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505", "T1505.003"]}
action.escu.data_models = []
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\`. 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 = 2021-10-05
action.escu.modification_date = 2021-10-05
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 = []
action.escu.analytic_story = ["HAFNIUM Group", "ProxyShell"]
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}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.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": ["HAFNIUM Group", "ProxyShell"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Exploitation"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505", "T1505.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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\`. 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="*.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 | `detect_exchange_web_shell_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 8", "CIS 11"], "kill_chain_phases": ["Exploitation"], "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 = []
action.escu.analytic_story = ["F5 TMUI RCE CVE-2020-5902"]
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 8", "CIS 11"], "cve": ["CVE-2020-5902"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
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 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", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
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 = []
action.escu.analytic_story = ["Suspicious GCP Storage Activities"]
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", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Detect GCP Storage access from a new IP
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` | 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.csv | 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.csv | 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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "DE.CM"]}
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 = 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 = Although unlikely a renamed instance of hh.exe will be used legitimately, 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 Renamed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Compiled HTML Activity"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Renamed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Detect HTML Help Renamed
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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` 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Compiled HTML 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 $user$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Compiled HTML Activity"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_proces_name$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "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 = 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 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 = []
action.escu.analytic_story = ["Suspicious Compiled HTML Activity"]
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
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 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]}
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 = []
action.escu.analytic_story = ["Command and Control"]
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 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
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 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 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"]
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 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.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 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "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 = 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 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 = []
action.escu.analytic_story = ["Suspicious MSHTA Activity"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 Mimikatz Using Loaded Images - Rule]
action.escu = 0
action.escu.enabled = 1
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.escu.mappings = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.AE", "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 = []
action.escu.analytic_story = ["Credential Dumping", "Detect Zerologon Attack", "Cloud Federated Credential Abuse", "DarkSide Ransomware"]
action.risk = 1
action.risk.param._risk_message = A process, $Image$, has loaded $ImageLoaded$ that are typically related to credential dumping on $Computer$. Review for further details.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 64}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "ImageLoaded", "threat_object_type": "parent process"}, {"threat_object_field": "Image", "threat_object_type": "process"}]
action.risk.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 Using Loaded Images - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Detect Zerologon Attack", "Cloud Federated Credential Abuse", "DarkSide Ransomware"], "cis20": ["CIS 6", "CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.AE", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "ImageLoaded", "role": ["Other"], "type": "Parent Process"}, {"name": "Image", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename Computer as dest | `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 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Cloud Federated Credential Abuse"]
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 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003"]}
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 = 2021-06-09
action.escu.modification_date = 2021-06-09
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
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 $ComputerName$ by $User$.
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 90}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 90}]
action.risk.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": ["Malicious PowerShell"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003"], "observable": [{"name": "User", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 Message IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*, *kerberos::ptt*, *kerberos::golden*) | 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_mimikatz_with_powershell_script_block_logging_filter`

[ESCU - Detect New Local Admin account - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for newly created accounts that have been elevated to local administrators.
action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for newly created accounts that have been elevated to local administrators.
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 = []
action.escu.analytic_story = ["DHS Report TA18-074A", "HAFNIUM Group"]
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.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"], "cis20": ["CIS 16"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 60, "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for newly created accounts that have been elevated to local administrators.
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 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 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
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 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
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 = []
action.escu.analytic_story = ["Suspicious GCP Storage Activities"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'src']
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 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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}, {"threat_object_field": "bucketName", "threat_object_type": "other"}]
action.risk.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 13"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "userIdentity.userName", "role": ["Attacker"], "type": "User"}, {"name": "bucketName", "role": ["Victim"], "type": "Other"}]}
schedule_window = auto
alert.digest_mode = 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 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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}, {"threat_object_field": "bucketName", "threat_object_type": "other"}]
action.risk.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 13"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user_arn", "role": ["Attacker"], "type": "User"}, {"name": "bucketName", "role": ["Victim"], "type": "Other"}]}
schedule_window = auto
alert.digest_mode = 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 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 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1190", "T1059"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]}
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. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = []
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = An outbound LDAP connection from $src_ip$ in your infrastructure connecting to dest ip $dest_ip$
action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "dest_ip", "risk_object_type": "system", "risk_score": 56}]
action.risk.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 LDAP Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Log4Shell CVE-2021-44228"], "cis20": ["CIS 12", "CIS 13"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access"], "cve": ["CVE-2021-44228"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1190", "T1059"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Victim"], "type": "IP Address"}, {"name": "dest_ip", "role": ["Attacker"], "type": "IP Address"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Detect Outbound LDAP 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 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 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "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 = []
action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A", "NOBELIUM Group"]
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 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1071.002", "T1071"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
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 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 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["ID.AM", "PR.DS"]}
action.escu.data_models = []
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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
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 - Detect Outlook exe writing a zip file - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566", "T1566.001"], "nist": ["ID.AM", "PR.DS"]}
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 OR Processes.process_name=explorer.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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009", "T1574"], "nist": ["PR.PT", "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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009", "T1574"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Exploitation", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Exploitation", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
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 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["PR.PT", "DE.CM"]}
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 = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. 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 = 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 = []
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Zoom Child Processes", "NOBELIUM Group"]
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 prohibited applications.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 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 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = Detect Prohibited Applications Spawning cmd 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) 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021", "T1021.002"], "nist": ["PR.PT", "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 = 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 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 = []
action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "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 $user$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021", "T1021.002"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1020"]}
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 = 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 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 50, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1020"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 Rare Executables - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.
action.escu.mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.
action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes.  These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.
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 = 2020-03-16
action.escu.modification_date = 2020-03-16
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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Unusual Processes", "Cloud Federated Credential Abuse"]
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": ["Emotet Malware  DHS Report TA18-201A ", "Unusual Processes", "Cloud Federated Credential Abuse"], "cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.
action.notable.param.rule_title = Detect Rare Executables
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
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 | rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Regsvcs Regasm 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 $user$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "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 = 2021-02-16
action.escu.modification_date = 2021-02-16
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 = []
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ contacting a remote destination was identified on endpoint $Computer$ 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": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = 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 = 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 = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Regsvcs Regasm 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 $user$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "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 = 2021-02-16
action.escu.modification_date = 2021-02-16
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 = []
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ contacting a remote destination was identified on endpoint $Computer$ 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": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = 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 = 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 = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Suspicious Regsvcs Regasm Activity"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "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 = 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 = Limited false positives related to third party software registering .DLL's.
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 Regsvr32 Application Control Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Regsvr32 Activity", "Cobalt Strike"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Suspicious Regsvr32 Activity", "Cobalt Strike"], "cis20": ["CIS 8", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1560.001", "T1560"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Collection and Staging"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 27}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 27}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Renamed 7-Zip - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1560.001", "T1560"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Detect Renamed 7-Zip
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Exploitation", "Lateral Movement", "Execution"], "mitre_attack": ["T1569", "T1569.002"]}
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 = 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 = 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 = 2021-09-16
action.escu.modification_date = 2021-09-16
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 = []
action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 27}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 27}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Renamed PSExec - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Active Directory Lateral Movement"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exploitation", "Lateral Movement", "Execution"], "mitre_attack": ["T1569", "T1569.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Detect Renamed PSExec
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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_psexec` 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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1020"]}
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 = 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 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 = []
action.escu.analytic_story = ["DarkSide Ransomware", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 27}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 27}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Renamed RClone - Rule
action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1020"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 `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.notable.param.rule_title = Detect Renamed RClone
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Exploitation", "Exfiltration"], "mitre_attack": ["T1560.001", "T1560"]}
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 = 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. 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 = []
action.escu.analytic_story = ["Collection and Staging"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 27}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 27}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Renamed WinRAR - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exploitation", "Exfiltration"], "mitre_attack": ["T1560.001", "T1560"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Detect Renamed WinRAR
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
cron_schedule = 59 * * * *
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 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src_mac', 'firstTime', 'lastTime', 'count', 'message_type']
action.notable.param.rule_description = DHCP Snooping has detected a Rogue DHCP Server on $orig_host$ from $src_mac$. This may be an indication of a MITM attack.
action.notable.param.rule_title = Rogue DHCP Server Detected on $orig_host$
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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity"]
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": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity"]
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": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity"]
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": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious MSHTA Activity", "NOBELIUM Group"]
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.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"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
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 = []
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Detect S3 access from a new IP
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 = `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.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | 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 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1041"], "nist": ["PR.DS", "DE.CM", "DE.AE"]}
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 = []
action.escu.analytic_story = ["Data Exfiltration"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1041"], "nist": ["PR.DS", "DE.CM", "DE.AE"]}
schedule_window = auto
action.notable = 1
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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"]}
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 = 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 = 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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"]}
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 = 2021-05-27
action.escu.modification_date = 2021-05-27
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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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") 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)` | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"]}
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 = 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 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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1069.001", "T1482", "T1087.001", "T1087", "T1069.002", "T1069"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005", "T1542"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
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 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005", "T1542"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
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 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect Spike in AWS API 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 = `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 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 13"], "nist": ["DE.DP"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 13"], "confidence": 50, "context": ["Source:Cloud Data", "Stage:Execution"], "impact": 30, "nist": ["DE.DP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 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.DP", "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 = []
action.escu.analytic_story = ["AWS Security Hub Alerts"]
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"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect Spike in AWS Security Hub Alerts for User
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 = `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 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 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect Spike in Network ACL 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 = `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 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
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 = []
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect Spike in S3 Bucket deletion
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=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 - 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect Spike in Security Group 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 = `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 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 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]}
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 = []
action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Detect Spike in blocked Outbound Traffic from your AWS
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 = `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 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 1", "CIS 11"], "kill_chain_phases": ["Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1020", "T1498", "T1020.001"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Router and Infrastructure Security"]
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 1", "CIS 11"], "kill_chain_phases": ["Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1020", "T1498", "T1020.001"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
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 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 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]}
action.escu.data_models = ["Change_Analysis"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Data Protection"]
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 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.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 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 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Asset Tracking"]
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 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
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.signature=DHCPREQUEST by All_Sessions.src_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 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["PR.PT", "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 = 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 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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Command-Line Executions"]
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.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"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.003", "T1546"]}
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 = []
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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.003", "T1546"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_wmi_event_subscription_persistence_filter`

[ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects SIGRed via Splunk Stream.
action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects SIGRed via Splunk Stream.
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 = []
action.escu.analytic_story = ["Windows DNS SIGRed CVE-2020-1350"]
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 8", "CIS 12"], "cve": ["CVE-2020-1350"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects SIGRed via Splunk Stream.
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 = This search detects SIGRed via Zeek DNS and Zeek Conn data.
action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = This search detects SIGRed via Zeek DNS and Zeek Conn data.
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 = []
action.escu.analytic_story = ["Windows DNS SIGRed CVE-2020-1350"]
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 8", "CIS 16"], "cve": ["CVE-2020-1350"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects SIGRed via Zeek DNS and Zeek Conn data.
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 = This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC
action.escu.mappings = {"cis20": ["CIS 8", "CIS 11"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC
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 = []
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 Zerologon via Zeek - Rule
action.correlationsearch.annotations = {"analytic_story": ["Detect Zerologon Attack"], "cis20": ["CIS 8", "CIS 11"], "cve": ["CVE-2020-1472"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC
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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]}
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 = []
action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"]
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"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 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 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1189"], "nist": ["PR.DS", "PR.PT", "DE.AE", "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 = []
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}, {"threat_object_field": "query", "threat_object_type": "dnsquery"}]
action.risk.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 8", "CIS 12", "CIS 13"], "confidence": 80, "context": ["source:endpoint", {"stage": "Initial Access"}], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1189"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"], "observable": [{"name": "host", "role": ["Victim"], "type": "Hostname"}, {"name": "query", "role": ["Attacker"], "type": "dnsquery"}]}
schedule_window = auto
action.notable = 1
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 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 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "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 = []
action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"]
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 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 - 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious MSHTA 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 $user$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"]}
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 = 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 = Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, 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 renamed - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious MSHTA Activity"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 80}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 renamed - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Detect mshta renamed
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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_mshta` 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 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 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS User Monitoring"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect new API calls from user roles
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Detect new user AWS Console Login
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=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 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 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1016"], "nist": ["ID.AM", "PR.DS"]}
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 = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes 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 usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings.
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 2"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 40, "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1016"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter`

[ESCU - Detect shared ec2 snapshot - 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 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1537"], "nist": ["PR.DS", "PR.AC", "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 = 2021-07-20
action.escu.modification_date = 2021-07-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect shared ec2 snapshot - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}]
action.risk.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 shared ec2 snapshot - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities", "Data Exfiltration"], "cis20": ["CIS 13"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution", "Stage:Exfiltration"], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1537"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "user_arn", "role": ["Attacker"], "type": "User"}, {"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}]}
schedule_window = auto
alert.digest_mode = 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 | where match = "No Match" | `detect_shared_ec2_snapshot_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 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1071.001"], "nist": ["PR.IP", "DE.DP"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Dynamic DNS"]
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 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1071.001"], "nist": ["PR.IP", "DE.DP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.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. Deprecated because existing detection is doing the same.
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "PR.DS"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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. Deprecated because existing detection is doing the same.
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 = 2017-09-18
action.escu.modification_date = 2017-09-18
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 = []
action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Command and Control"]
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"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src']
action.notable.param.rule_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. Deprecated because existing detection is doing the same.
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 - 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 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072"], "nist": ["PR.IP"]}
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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A "]
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 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 = network operator may disable this feature of windows but not so common.
action.escu.creation_date = 2021-06-22
action.escu.modification_date = 2021-06-22
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 = []
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 - Disable AMSI Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable" Registry.registry_value_data = "0x00000000" 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)` | `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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = admin or user may choose to disable windows defender product
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 Defender AntiVirus Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["IceID"]
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.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = 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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender*" Registry.registry_value_name = DisableAntiVirus Registry.registry_value_data = 0x00000001 by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `disable_defender_antivirus_registry_filter`

[ESCU - Disable Defender BlockAtFirstSeen Feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the BlockAtFirstSeen feature where it block suspicious file first seen in the host.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the BlockAtFirstSeen feature where it block suspicious file first seen in 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 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = admin or user may choose to disable windows defender product
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 Defender BlockAtFirstSeen Feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["IceID"]
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.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the BlockAtFirstSeen feature where it block suspicious file first seen in 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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = DisableBlockAtFirstSeen Registry.registry_value_data = 0x00000001 by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `disable_defender_blockatfirstseen_feature_filter`

[ESCU - Disable Defender Enhanced Notification - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the Enhanced Notification feature wher user or admin set to show or display alerts.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the Enhanced Notification feature wher user or admin set to show or display alerts.
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 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 = user may choose to disable windows defender AV
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 Defender Enhanced Notification - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["IceID"]
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.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the Enhanced Notification feature wher user or admin set to show or display 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.Registry where Registry.registry_path = "*Microsoft\\Windows Defender\\Reporting*" Registry.registry_value_name = DisableEnhancedNotifications Registry.registry_value_data = 0x00000001 by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `disable_defender_enhanced_notification_filter`

[ESCU - Disable Defender MpEngine Registry - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = admin or user may choose to disable windows defender product
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 Defender MpEngine Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["IceID"]
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.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = 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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender\\MpEngine*" Registry.registry_value_name = MpEnablePus Registry.registry_value_data = 0x00000000 by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `disable_defender_mpengine_registry_filter`

[ESCU - Disable Defender Spynet Reporting - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the spynet reporting for its telemetry.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the spynet reporting for its 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 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = admin or user may choose to disable windows defender product
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 Defender Spynet Reporting - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["IceID"]
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.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the spynet reporting for its 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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = SpynetReporting Registry.registry_value_data = 0x00000000 by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `disable_defender_spynet_reporting_filter`

[ESCU - Disable Defender Submit Samples Consent Feature - Rule]
action.escu = 0
action.escu.enabled = 1
description = his analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the submit samples feature for further analysis..
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = his analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the submit samples feature 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 6.0.4 of the Sysmon TA.
action.escu.known_false_positives = admin or user may choose to disable windows defender product
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 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 = []
action.escu.analytic_story = ["IceID"]
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": 49}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}]
action.risk.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = his analytic is to detect a suspicious modification of registry to disable windows defender feature. This technique is to bypassed or evade detection from Windows Defender AV product specially the submit samples feature 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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = SubmitSamplesConsent Registry.registry_value_data = 0x00000000 by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 = network operator may disable this feature of windows but not so common.
action.escu.creation_date = 2021-06-22
action.escu.modification_date = 2021-06-22
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 = []
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 - Disable ETW Through Registry - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" Registry.registry_value_data = "0x00000000" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070", "T1070.001"]}
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 = 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 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 = []
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.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"], "confidence": 80, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070", "T1070.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 may disable this application for non technical user.
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_data = "0x00000001" 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)` | `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 IceID 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 IceID 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 = 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 = 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 = []
action.escu.analytic_story = ["IcedID"]
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 IceID 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"]}
action.escu.data_models = []
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 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 = 2021-10-05
action.escu.modification_date = 2021-10-05
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Control\\MiniNt\\*" by Registry.dest Registry.user Registry.registry_value_name Registry.registry_key_name Registry.registry_path Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1564.001", "T1562.001", "T1564", "T1562"]}
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 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 = unknown
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1564.001", "T1562.001", "T1564", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = Disable Show Hidden 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 = | 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\\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 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
action.escu.data_models = []
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 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 = admin may set this policy for non-critical machine.
action.escu.creation_date = 2021-09-29
action.escu.modification_date = 2021-09-29
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"]
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.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"], "confidence": 100, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\CurrentVersion\\Policies\\System*" Registry.registry_value_name="LocalAccountTokenFilterPolicy" Registry.registry_value_data="0x00000001" by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `disable_uac_remote_restriction_filter`

[ESCU - Disable Windows App Hotkeys - Rule]
action.escu = 0
action.escu.enabled = 1
description = This 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = []
action.escu.eli5 = This 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 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 CarbonBlack 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 = 2021-05-05
action.escu.modification_date = 2021-05-05
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 = []
action.escu.analytic_story = ["XMRig"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This 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 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="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_name = "HotKey Disabled" AND Registry.registry_key_name = "Debugger" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 disable this windows features.
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "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": 40}]
action.risk.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": ["Windows Defense Evasion Tactics", "Ransomware", "Revil Ransomware"], "confidence": 100, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime 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" Registry.registry_value_data = "0x00000001" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 disable this windows features.
action.escu.creation_date = 2021-03-31
action.escu.modification_date = 2021-03-31
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled"  Registry.registry_value_name = "Off" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 may disable this application for non technical user.
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 CMD Application - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_data = "0x00000001" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 may disable this application for non technical user.
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 ControlPanel - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_data = "0x00000001" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 = admin or user may choose to disable windows defender product
action.escu.creation_date = 2021-10-20
action.escu.modification_date = 2021-10-20
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 = []
action.escu.analytic_story = ["IceID"]
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.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": ["IceID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 min(_time) as firstTime max(_time) as lastTime 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 Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Disabling Firewall with 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 may disable this application for non technical user.
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 FolderOptions Windows Feature - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_data = "0x00000001" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 may disable this application for non technical user.
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 NoRun Windows App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_data = "0x00000001" 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)` | `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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Remcos"]
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.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"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1548.002", "T1548"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 = in some cases admin can disable systemrestore on a machine.
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 SystemRestore In Registry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime 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" Registry.registry_value_data = "0x00000001" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 may disable this application for non technical user.
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 Task Manager - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_data = "0x00000001" 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)` | `disabling_task_manager_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = 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 Net App - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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 Dsquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 `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.notable.param.rule_title = Domain Account Discovery with Dsquery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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 Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = Domain Controller 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="" 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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.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 Dsquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = Domain Group Discovery With Dsquery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 Net - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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` 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.notable.param.rule_title = Domain 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*) 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.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 Wmic - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = Domain 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* 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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 | `security_content_ctime(firstTime)` | `domain_group_discovery_with_adsisearcher_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
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 = []
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = Suspicious files were downloaded with the Telegram application on $dest$ by $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 49}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 49}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 Computer EventCode Image 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204", "T1204.002"]}
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ create a file $TargetImage$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.param.verbose = 0
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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204", "T1204.002"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Drop IcedID License dat
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter`

[ESCU - Dump LSASS via comsvcs DLL - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect the usage of comsvcs.dll for dumping the lsass process.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Detect the usage of comsvcs.dll for dumping the lsass 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 = None identified.
action.escu.creation_date = 2020-02-21
action.escu.modification_date = 2020-02-21
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 = []
action.escu.analytic_story = ["Credential Dumping", "Suspicious Rundll32 Activity", "HAFNIUM Group"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Credential Dumping", "Suspicious Rundll32 Activity", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = Detect the usage of comsvcs.dll for dumping the lsass process.
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.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 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "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 = 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 = None identified.
action.escu.creation_date = 2021-09-16
action.escu.modification_date = 2021-09-16
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 = []
action.escu.analytic_story = ["Credential Dumping", "HAFNIUM Group"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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.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 - 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 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Credential Dumping", "HAFNIUM Group"]
action.risk = 1
action.risk.param._risk_message = The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$, attempting to dump lsass.exe.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 - Dump LSASS via procdump Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = Dump LSASS via procdump Rename
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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* | rename Computer as dest |  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 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Unusual AWS EC2 Modifications"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = EC2 Instance Modified With Previously Unseen User
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
action.risk = 1
action.risk.param._risk_message = 
action.risk.param._risk = []
action.risk.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 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = EC2 Instance Started In Previously Unseen Region
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` 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 1"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Cryptomining"]
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 1"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = EC2 Instance Started With Previously Unseen AMI
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 1"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Cryptomining"]
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 1"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = EC2 Instance Started With Previously Unseen Instance Type
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"]
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 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = EC2 Instance Started With Previously Unseen User
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.006", "T1127", "T1562"]}
action.escu.data_models = []
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 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 = 2021-10-07
action.escu.modification_date = 2021-10-07
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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": 90}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 90}]
action.risk.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 100, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.006", "T1127", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\SOFTWARE\\Microsoft\\.NETFramework*") Registry.registry_value_name = ETWEnabled Registry.registry_value_data=0x00000000 by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `etw_registry_disabled_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Elevated group discovery using PowerView on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 21}]
action.risk.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 PowerView - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Elevated Group 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 (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 | `security_content_ctime(firstTime)` | `elevated_group_discovery_with_powerview_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 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
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 = 2017-09-19
action.escu.modification_date = 2017-09-19
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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Emails"]
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": ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Emails"], "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Email Attachments With Lots Of Spaces
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_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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1114.001"]}
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 = []
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 - Email files written outside of the Outlook directory - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1114.001"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1114.002"], "nist": ["PR.PT", "DE.CM", "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 = []
action.escu.analytic_story = ["Collection and Staging", "HAFNIUM Group"]
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 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Email servers sending high volume traffic to hosts
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` 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021"]}
action.escu.data_models = []
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 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 = 2021-05-19
action.escu.modification_date = 2021-05-19
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 = []
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 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="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112", "T1003"]}
action.escu.data_models = []
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 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 = 2021-10-05
action.escu.modification_date = 2021-10-05
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 = []
action.escu.analytic_story = ["Credential Dumping"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SecurityProviders\\WDigest\\*" Registry.registry_value_name = "UseLogonCredential" Registry.registry_value_data = 0x00000001 by Registry.dest Registry.user Registry.registry_value_name Registry.registry_key_name Registry.registry_path Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087"]}
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 = []
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1003.002", "T1003"]}
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 = 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 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 = []
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 capture credentials for offline cracking or observability.
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": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.verbose = 0
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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1003.002", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Esentutl SAM 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_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 - 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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548.002", "T1548"]}
action.escu.data_models = []
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 = 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 = Some false positives may be present and will need to be filtered.
action.escu.creation_date = 2021-03-01
action.escu.modification_date = 2021-03-01
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "IcedID"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 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="*mscfile\\shell\\open\\command\\*"  by Registry.user, Registry.dest , Registry.registry_value_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"]}
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 = 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 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1489"]}
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 = 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 sc.exe may be used.
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 = []
action.escu.analytic_story = ["XMRig"]
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1489"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive Attempt To 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` 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 >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_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 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004", "T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"]
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 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004", "T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Excessive DNS Failures
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.query") as queries 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"| `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)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1489"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["XMRig", "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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1489"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive Service Stop 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` 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["XMRig"]
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive Usage Of Cacls 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.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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"]}
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 = 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. Filter as needed. Modify the time span as needed.
action.escu.creation_date = 2021-05-06
action.escu.modification_date = 2021-05-06
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 = []
action.escu.analytic_story = ["XMRig", "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 name"}]
action.risk.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": ["XMRig", "Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Scope:Local", "Stage:Execution"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_name", "role": ["Process", "Attacker"], "type": "Process Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive Usage Of 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.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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1569", "T1569.002"]}
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 = []
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 - Excessive Usage Of SC Service Utility - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1569", "T1569.002"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Excessive Usage Of SC Service Utility
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 Computer, _time |  eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by Computer |  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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["XMRig"]
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive Usage Of Taskkill
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048"]}
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 = 2021-04-21
action.escu.modification_date = 2021-04-21
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 = []
action.escu.analytic_story = ["Suspicious DNS Traffic", "Dynamic DNS", "Command and Control", "Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = Excessive usage of nslookup.exe has been detected on $Computer$. This detection is triggered as as it violates the dynamic threshold
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 28}]
action.risk.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", "Command and Control", "Data Exfiltration"], "confidence": 70, "context": ["Source:Endpoint", "Scope:Local", "Stage:Exfiltration"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 = Excessive Usage of 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 = `sysmon` EventCode = 1 process_name = "nslookup.exe" |  bucket _time span=15m | stats count as numNsLookup by Computer, _time |  eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by Computer |  eval upperThreshold=(avgNsLookup + stdNsLookup *3) |  eval isOutlier=if(avgNsLookup > 20 and avgNsLookup >= upperThreshold, 1, 0) |  search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_nslookup_app_filter`

[ESCU - Excessive number of distinct processes created in Windows Temp folder - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"]}
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 = To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. 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 sc.exe may be used.
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 = 2021-06-03
action.escu.modification_date = 2021-06-03
action.escu.confidence = high
action.escu.full_search_name = ESCU - Excessive number of distinct processes created in Windows Temp folder - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 distinct processes created in Windows Temp folder - Rule
action.correlationsearch.annotations = {"analytic_story": ["Meterpreter"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive number of distinct processes created in Windows 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 = | 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 = "*\\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_number_of_distinct_processes_created_in_windows_temp_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = []
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 = 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.
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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive number of service control start as 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` 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1033"]}
action.escu.data_models = []
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 = To successfully implement this search you need to be ingesting events related to processes on the endpoints that include the name of the process and process id into the `Endpoint` datamodel in the `Processes` node.
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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = Excessive number of taskhost 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` 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 - 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. \
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies suspicious behavior related to ProxyShell against on-premise Microsoft Exchange servers. \
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 = 2021-08-27
action.escu.modification_date = 2021-08-27
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 = []
action.escu.analytic_story = ["ProxyShell"]
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This analytic identifies suspicious behavior related to ProxyShell against on-premise Microsoft Exchange servers. \
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.json*" 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. Currently, there is no active data shared or data we could re-produce relate to this part of the ProxyShell chain of exploits.  \
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).
action.escu.mappings = {"kill_chain_phases": ["Reconnaissance", "Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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. Currently, there is no active data shared or data we could re-produce relate to this part of the ProxyShell chain of exploits.  \
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).
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 = 2021-08-27
action.escu.modification_date = 2021-08-27
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 = []
action.escu.analytic_story = ["ProxyShell"]
action.risk = 1
action.risk.param._risk_message = Local user 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.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": ["ProxyShell"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance", "Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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. Currently, there is no active data shared or data we could re-produce relate to this part of the ProxyShell chain of exploits.  \
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).
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 Message IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*") | stats count min(_time) as firstTime max(_time) as lastTime by Path Message OpCode ComputerName User EventCode| `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$). This represents suspicious behavior as its commonly used by tools like like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The Trickbot malware family also implements this behavior to try to infect other machines in the infected network.
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.002"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic identifies executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like 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. The following Splunk SOAR playbook can be used to respond to this detection: Delete Detected Files
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 = 2021-11-18
action.escu.modification_date = 2021-11-18
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement", "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.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", "Trickbot"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = The following analytic identifies executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). This represents suspicious behavior as its commonly used by tools like like PsExec/PaExec and others to stage service binaries before creating and starting a Windows service on remote endpoints. Red Teams and adversaries alike may abuse administrative shares for lateral movement and remote code execution. The Trickbot malware family also implements this behavior to try to infect other machines in the infected network.
action.notable.param.rule_title = Executable File Written in Administrative SMB Share
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.exe","*.dll") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\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 will identify suspicious executable or scripts (known file extensions) in list of suspicious file path in Windows. This technique is used by adversaries to evade detection. The suspicious file path are known paths used in the wild and are not common to have executable or scripts.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will identify suspicious executable or scripts (known file extensions) in list of suspicious file path in Windows. This technique is used by adversaries to evade detection. The suspicious file path are known paths used in the wild and are not common to have executable or scripts.
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 = 2021-05-06
action.escu.modification_date = 2021-05-06
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 = []
action.escu.analytic_story = ["XMRig", "Remcos"]
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": 56}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_name", "threat_object_type": "file name"}]
action.risk.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": ["XMRig", "Remcos"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_id", "role": ["Attacker"], "type": "Process"}, {"name": "file_name", "role": ["Other", "Attacker"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This analytic will identify suspicious executable or scripts (known file extensions) in list of suspicious file path in Windows. This technique is used by adversaries to evade detection. The suspicious file path are known paths used in the wild and are not common to have executable or scripts.
action.notable.param.rule_title = Executables Or Script Creation In 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` 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*) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user | `drop_dm_object_name(Processes)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.005"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.005"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_id", "role": ["Attacker"], "type": "Process"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}]}
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 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 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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 = 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 = []
action.escu.analytic_story = ["Windows File Extension and Association Abuse", "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 - 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 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.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 - 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 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036", "T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
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 = 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.
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 = []
action.escu.analytic_story = ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities"]
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.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"], "cis20": ["CIS 3", "CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036", "T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process", "role": ["Parent Process", "Attacker"], "type": "Process"}]}
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 = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.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 - 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": ["PR.IP"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = Extended Period Without Successful Netbackup 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 = `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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["DarkSide Ransomware", "Credential Dumping"]
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Credential Access", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_id", "role": ["Parent Process", "Attacker"], "type": "Process"}]}
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 search looks for file writes with extensions consistent with a SamSam ransomware attack.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for file writes with extensions consistent with a SamSam ransomware attack.
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 = []
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.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 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 100, "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "file_name", "role": ["Other", "Attacker"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The search looks for file writes with extensions consistent with a SamSam ransomware attack.
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 = This 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1562.004", "T1562"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This 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 = 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 = 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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1562.004", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This 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.notable.param.rule_title = Firewall Allowed Program 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.Processes where Processes.process = "*firewall*" Processes.process = "*allowedprogram*" 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 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - 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 Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window.
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 = []
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.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 3", "CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_name", "role": ["Attacker", "Child Process"], "type": "Process Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen.
action.notable.param.rule_title = First Time Seen Child Process of Zoom
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 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 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["ID.AM", "PR.DS", "PR.AC", "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 = []
action.escu.analytic_story = ["Windows Service Abuse", "Orangeworm Attack Group", "NOBELIUM Group"]
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 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1569", "T1569.002"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = First Time Seen Running Windows 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=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 - 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 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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. Please make sure you run the support search "Previously seen command line arguments,"&#151;which creates a lookup file called `previously_seen_cmd_line_arguments.csv`&#151;a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function.
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 = []
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 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = First time seen command line 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` 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 - 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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1112", "T1548.002", "T1548"]}
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 = 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 = 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 = []
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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 90, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1112", "T1548.002", "T1548"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070"]}
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 = 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 = 2021-08-11
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 = []
action.escu.analytic_story = ["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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GCP Detect accounts with high risk roles by project
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.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 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
action.escu.data_models = []
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 = []
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 - GCP Detect gcploit framework - Rule
action.correlationsearch.annotations = {"analytic_story": ["GCP Cross Account Activity"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'src']
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 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GCP Detect high risk permissions by resource and account
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.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 GCR container uploaded - 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 show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path.
action.escu.mappings = {"mitre_attack": ["T1525"]}
action.escu.data_models = []
action.escu.eli5 = 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 show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path.
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 subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives.
action.escu.known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path.
action.escu.creation_date = 2020-02-20
action.escu.modification_date = 2020-02-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - GCP GCR container uploaded - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - GCP GCR container uploaded - Rule
action.correlationsearch.annotations = {"analytic_story": ["Container Implantation Monitoring and Investigation"], "mitre_attack": ["T1525"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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 show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path.
action.notable.param.rule_title = GCP GCR container uploaded
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 min(_time) as firstTime max(_time) as lastTime  FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")`  | `gcp_gcr_container_uploaded_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
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 = []
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"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods
action.notable.param.rule_title = GCP Kubernetes cluster pod 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` 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 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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 - GCP Kubernetes cluster scan detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
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 = 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 = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2021-04-19
action.escu.modification_date = 2021-04-19
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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 $connection_to_CNC$ 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"}]
action.risk.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": ["Cobalt Strike"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Command And Control"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}, {"name": "connection_to_CNC", "role": ["Other"], "type": "IP Address"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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_guid 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="(gpupdate\.exe.{0,4}$)" | join  process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_guid Ports.dest Ports.dest_port| `drop_dm_object_name(Ports)` | rename  dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_guid connection_to_CNC dest_port | `gpupdate_with_no_command_line_arguments_with_network_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"], "observable": [{"name": "source.address", "role": ["attacker"], "type": "User"}, {"name": "destination{}.address", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GSuite Email Suspicious Attachment
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566"]}
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 = []
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Gdrive suspicious file sharing
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 = `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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 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 = 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 = []
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": 9}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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 ADDefaultDomainPasswordPolicy with Powershell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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-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.notable.param.rule_title = Get ADDefaultDomainPasswordPolicy 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 2021-08-26
action.escu.modification_date = 2021-08-26
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ to query domain password policy
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 9}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 9}]
action.risk.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 ADDefaultDomainPasswordPolicy with Powershell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 30, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Get ADDefaultDomainPasswordPolicy 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 Message ="*Get-ADDefaultDomainPasswordPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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 ADUser with PowerShell - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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. 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.notable.param.rule_title = Get ADUser 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 2021-08-24
action.escu.modification_date = 2021-08-24
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ for user enumeration
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 25}]
action.risk.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 ADUser with PowerShell Script Block - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Get ADUser 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 Message = "*get-aduser*" Message = "*-filter*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 2021-08-26
action.escu.modification_date = 2021-08-26
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ to query domain user password policy.
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 9}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 9}]
action.risk.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"], "confidence": 30, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message ="*Get-ADUserResultantPasswordPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 2021-08-26
action.escu.modification_date = 2021-08-26
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ to query domain policy.
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 30}]
action.risk.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"], "confidence": 60, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message ="*Get-DomainPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainpolicy_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 2021-08-24
action.escu.modification_date = 2021-08-24
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ for user enumeration
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 25}]
action.risk.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"], "confidence": 50, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*Get-DomainUser*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainuser_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"]}
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 = 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 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System group discovery 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Get WMIObject Group 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=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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"]}
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 = 2021-09-14
action.escu.modification_date = 2021-09-14
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System group discovery enumeration 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 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.notable.param.rule_title = Get WMIObject Group Discovery 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 = "*Get-WMIObject*" AND Message = "*Win32_Group*" | stats count min(_time) as firstTime max(_time) as lastTime by  Message OpCode ComputerName User EventCode| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_wmiobject_group_discovery_with_script_block_logging_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"]}
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 = 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 = 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 = []
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.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"], "confidence": 40, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"]}
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 = 2021-08-24
action.escu.modification_date = 2021-08-24
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-DomainTrust was identified on endpoint $ComputerName$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 12}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 40, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
schedule_window = auto
action.notable = 1
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 Message = "*get-foresttrust*" | stats count min(_time) as firstTime max(_time) as lastTime by Message ComputerName User EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"]}
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 = 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 = 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 = []
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.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"], "confidence": 40, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"]}
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 = UPDATE_KNOWN_FALSE_POSITIVES
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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Suspicious PowerShell Get-ForestTrust was identified on endpoint $ComputerName$ by user $User$.
action.risk.param._risk = [{"risk_object_field": "User", "risk_object_type": "user", "risk_score": 12}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 12}]
action.risk.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"], "confidence": 40, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1482"], "observable": [{"name": "User", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 Message = "*get-foresttrust*" | stats count min(_time) as firstTime max(_time) as lastTime by  Message OpCode ComputerName User EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_script_block_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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-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.notable.param.rule_title = GetAdComputer 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 2021-09-01
action.escu.modification_date = 2021-09-01
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 = []
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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GetAdComputer 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 (Message = "*Get-AdComputer*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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-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.notable.param.rule_title = GetAdGroup 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 2021-08-25
action.escu.modification_date = 2021-08-25
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 = []
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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GetAdGroup 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 (Message = "*Get-ADGroup*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System user discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = GetCurrent 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="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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"]}
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 = 2021-09-13
action.escu.modification_date = 2021-09-13
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System user discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GetCurrent 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 (Message = "*[System.Security.Principal.WindowsIdentity]*" AND Message = "*GetCurrent()*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 2021-09-02
action.escu.modification_date = 2021-09-02
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery with PowerView on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message = "*Get-DomainComputer*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery using PowerView on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.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 - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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-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.notable.param.rule_title = GetDomainController 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 2021-09-02
action.escu.modification_date = 2021-09-02
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Remote system discovery with PowerView on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 24}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message = "*Get-DomainController*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 2021-08-26
action.escu.modification_date = 2021-08-26
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Domain group discovery enumeration using PowerView on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message = "*Get-DomainGroup*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local user 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = GetLocalUser 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"]}
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 = 2021-08-23
action.escu.modification_date = 2021-08-23
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local user 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GetLocalUser 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 (Message = "*Get-LocalUser*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Network Connection discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = GetNetTcpconnection 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-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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"]}
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 = 2021-09-10
action.escu.modification_date = 2021-09-10
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Network Connection discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = GetNetTcpconnection 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 (Message = "*Get-NetTcpconnection*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `getnettcpconnection_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 = 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"]}
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 = 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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = powershell process having commandline $Message$ for user enumeration
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 25}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 25}]
action.risk.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"], "confidence": 50, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*get-wmiobject*" Message = "*ds_user*" Message = "*-namespace*" Message = "*root\\directory\\ldap*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 2021-09-01
action.escu.modification_date = 2021-09-01
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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message=*Get-WmiObject* AND Message=*"namespace root\\directory\\ldap"* AND Message=*"class ds_computer"*) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 = 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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"]}
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 = 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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message=*Get-WmiObject* AND Message=*"namespace root\\directory\\ldap"* AND Message=*"class ds_group"*) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `getwmiobject_ds_group_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"]}
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 = 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 = 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 - 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local user 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.verbose = 0
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": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = GetWmiObject User Account 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=*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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"]}
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 = 2021-08-23
action.escu.modification_date = 2021-08-23
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local user 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.verbose = 0
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": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 `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.notable.param.rule_title = GetWmiObject User Account 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 (Message="*Get-WmiObject*" AND Message="*Win32_UserAccount*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `getwmiobject_user_account_with_powershell_script_block_filter`

[ESCU - GitHub Dependabot Alert - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for Dependabot Alerts in Github logs.
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1195.001", "T1195"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for Dependabot Alerts in Github logs.
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = [{"threat_object_field": "repository", "threat_object_type": "system"}]
action.risk.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1195.001", "T1195"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "repository", "role": ["Victim"], "type": "System"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search looks for Dependabot Alerts in Github logs.
action.notable.param.rule_title = GitHub Dependabot Alert
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 = `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 = This search looks for Pull Request from unknown user.
action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1195.001", "T1195"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for Pull Request from unknown user.
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = [{"threat_object_field": "repository", "threat_object_type": "system"}]
action.risk.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1195.001", "T1195"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "repository", "role": ["Victim"], "type": "System"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search looks for Pull Request from unknown user.
action.notable.param.rule_title = GitHub Pull Request from Unknown User
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 = `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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"], "observable": [{"name": "commit.commit.author.email", "role": ["attacker"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Github Commit Changes In Master
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 | eval severity="low" | eval phase="code" |  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, phase, severity | eval phase="code" | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"], "observable": [{"name": "commit.commit.author.email", "role": ["attacker"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Github Commit In Develop
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 - 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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1567.002", "T1567"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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": 72}, {"risk_object_field": "email", "risk_object_type": "user", "risk_score": 72}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 80, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1567.002", "T1567"], "observable": [{"name": "parameters.owner", "role": ["Attacker"], "type": "User"}, {"name": "email", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Gsuite Drive Share In External Email
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = []
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Gsuite Email Suspicious Subject With Attachment
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = []
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Gsuite Email With Known Abuse Web Service Link
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048.003", "T1048"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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": 9}, {"risk_object_field": "destination{}.address", "risk_object_type": "user", "risk_score": 9}]
action.risk.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048.003", "T1048"], "observable": [{"name": "source.address", "role": ["attacker"], "type": "User"}, {"name": "destination{}.address", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Gsuite Outbound Email With Attachment To External Domain
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"]}
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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001", "T1566"], "observable": [{"name": "parameters.owner", "role": ["attacker"], "type": "User"}, {"name": "email", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Gsuite Suspicious Shared 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 = `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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566"]}
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 = []
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Gsuite suspicious calendar invite
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 = `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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = []
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 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 CarbonBlack 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. Filter as needed.
action.escu.creation_date = 2021-05-05
action.escu.modification_date = 2021-05-05
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 = []
action.escu.analytic_story = ["XMRig"]
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "registry_value_name", "role": ["Attacker"], "type": "Other"}]}
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 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="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_data = "0x00000000" by Registry.dest Registry.user Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `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 8"], "kill_chain_phases": ["Actions on Objectives"], "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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"]
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.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"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Persistence"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1222", "T1222.001"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process", "role": ["Attacker", "Parent Process"], "type": "Other"}]}
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1485"]}
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 = 2021-03-16
action.escu.modification_date = 2021-03-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - High File Deletion Frequency - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = High frequency file deletion activity detected on host $Computer$
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 72}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "deleted_files", "threat_object_type": "file name"}]
action.risk.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 File Deletion Frequency - Rule
action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1485"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "deleted_files", "role": ["Target"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = High File Deletion Frequency
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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") | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID |where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `high_file_deletion_frequency_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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1537"]}
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 = []
action.escu.analytic_story = ["Information Sabotage"]
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 30, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1537"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = High Frequency Copy Of Files In Network 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 ("*.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 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1110.001", "T1110"], "nist": ["DE.DP", "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 = []
action.escu.analytic_story = ["Office 365 Detections"]
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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1110.001", "T1110"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = High Number of Login Failures from a single source
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=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 - High Process Termination Frequency - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytics are designed to indentify a high frequency of process termination on a machine 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1486"]}
action.escu.data_models = []
action.escu.eli5 = This analytics are designed to indentify a high frequency of process termination on a machine 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 = 2021-03-16
action.escu.modification_date = 2021-03-16
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 = []
action.escu.analytic_story = ["Clop Ransomware"]
action.risk = 1
action.risk.param._risk_message = High frequency process termination (more than 15 processes within 3s) detected on host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "proc_terminated", "threat_object_type": "process"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1486"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "proc_terminated", "role": ["Target"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This analytics are designed to indentify a high frequency of process termination on a machine 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.notable.param.rule_title = High Process Termination Frequency
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 Computer EventCode ProcessID | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_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 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002", "T1114"], "nist": ["PR.PT", "DE.CM", "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 = []
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 - Hosts receiving high volume of network traffic from email server - Rule
action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002", "T1114"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Hosts receiving high volume of network traffic from email 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 = | 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 - 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 `$ctx:loginId`, that may be found in the header used by the adversary. \
lookup matching is meant to catch some basic obfuscation that has been identified using upper, lower and date. \
Scoring will then occur based on any findings. The base score is meant to be 2 , created by jndi_fastmatch. Everything else is meant to increase that score. \
Finally, a simple table is created to show the scoring and the _raw field. Sort based on score or columns of interest.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting query assists with quickly assessing CVE-2021-44228, or Log4Shell, activity mapped to the Web Datamodel. This is a combination query attempting to identify, score and dashboard. Because the Log4Shell vulnerability requires the string to be in the logs, this will work to identify the activity anywhere in the HTTP headers using _raw. Modify the first line to use the same pattern matching against other log sources. Scoring is based on a simple rubric of 0-5. 5 being the best match, and less than 5 meant to identify additional patterns that will equate to a higher total score. \
The first jndi match identifies the standard pattern of `{jndi:` \
jndi_fastmatch is meant to identify any jndi in the logs. The score is set low and is meant to be the "base" score used later. \
jndi_proto is a protocol match that identifies `jndi` and one of `ldap, ldaps, rmi, dns, nis, iiop, corba, nds, http, https.` \
all_match is a very well written regex by https://gist.github.com/Schvenn that identifies nearly all patterns of this attack behavior. \
env works to identify environment variables in the header, meant to capture `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `env`. \
uri_detect is string match looking for the common uri paths currently being scanned/abused in the wild. \
keywords matches on enumerated values that, like `$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 = []
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = Hunting for Log4Shell exploitation has occurred.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"threat_object_field": "http_method", "threat_object_type": "other"}, {"threat_object_field": "src", "threat_object_type": "other"}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:network"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "http_method", "role": ["other"], "type": "Other"}, {"name": "src", "role": ["other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Hunting for Log4Shell
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 = | 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, "date:") OR match(_raw, "upper:") OR match(_raw, "lower:"),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, _raw | `hunting_for_log4shell_filter`

[ESCU - ICACLS Grant Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft and coinminer scripts to evade detections and restrict access to their component files.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft and coinminer scripts to evade detections and restrict access to their component files.
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 icacls.exe may be used.
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 - ICACLS Grant Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft and coinminer scripts to evade detections and restrict access to their component files.
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` 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 = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/grant*" 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)` | `icacls_grant_command_filter`

[ESCU - Icacls Deny Command - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files.
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 icacls.exe may be used.
action.escu.known_false_positives = Unknown. It is possible some administrative scripts use ICacls. Filter as needed.
action.escu.creation_date = 2021-04-29
action.escu.modification_date = 2021-04-29
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 = []
action.escu.analytic_story = ["XMRig"]
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files.
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` 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 = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/deny*" 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)` | `icacls_deny_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1560.001", "T1560"]}
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ create a file $TargetImage$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.param.verbose = 0
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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1560.001", "T1560"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = IcedID Exfiltrated Archived 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 = `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 Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_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 16"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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 16"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Identify New User 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 = | 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 - 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = 2021-11-19
action.escu.modification_date = 2021-11-19
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.002", "T1021.003", "T1047", "T1543.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = "*/c* \\\\127.0.0.1\\*" OR Processes.process= "*/c* 2>&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 - 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"]}
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 = 2021-11-18
action.escu.modification_date = 2021-11-18
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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message="*Enter-PSSession*" AND Message="*-ComputerName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
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. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = []
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}, {"threat_object_field": "http_user_agent", "threat_object_type": "other"}, {"threat_object_field": "http_method", "threat_object_type": "other"}]
action.risk.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"], "confidence": 50, "context": ["Source:network"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "http_user_agent", "role": ["other"], "type": "Other"}, {"name": "http_method", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"]}
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 = 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 = 2021-09-13
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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
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 = This search detects a potential kerberoasting attack via service principal name requests
action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1558.003", "T1558"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects a potential kerberoasting attack via service principal name requests
action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos
action.escu.known_false_positives = Older systems that support kerberos RC4 by default NetApp may generate false positives
action.escu.creation_date = 2020-10-16
action.escu.modification_date = 2020-10-16
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 = []
action.escu.analytic_story = ["Windows Privilege Escalation"]
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.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": ["Windows Privilege Escalation"], "cis20": ["CIS 8", "CIS 16"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1558.003", "T1558"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects a potential kerberoasting attack via service principal name requests
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 Ticket_Options=0x40810000 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
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 = []
action.escu.analytic_story = ["Ransomware", "BlackMatter 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}, {"threat_object_field": "Message", "threat_object_type": "other"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Message", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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$*") 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 - Kubernetes AWS 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 top to see both extremes of RBAC by accounts occurrences
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
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 RBAC authorization by account - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 RBAC authorization by account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes AWS detect RBAC authorization by account
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 = `aws_cloudwatchlogs_eks` 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_aws_detect_rbac_authorization_by_account_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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes AWS detect most active service accounts by pod
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 = `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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes AWS detect sensitive role access
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 = `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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes AWS detect service accounts forbidden failure access
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 = `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 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 = {"kill_chain_phases": ["Lateral Movement"]}
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 = 2020-06-23
action.escu.modification_date = 2020-06-23
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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
action.notable.param.rule_title = Kubernetes AWS detect suspicious kubectl calls
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 = `aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous  | table  src_ip src_user verb userAgent requestURI  | stats  count by src_ip src_user verb userAgent requestURI |`kubernetes_aws_detect_suspicious_kubectl_calls_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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure detect RBAC authorization by account
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 = `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 most 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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 detect most 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 = []
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 most active service accounts by pod namespace - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure detect most active service accounts by pod namespace
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 = `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_detect_most_active_service_accounts_by_pod_namespace_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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure detect sensitive object access
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 = `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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure detect sensitive role access
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 = `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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure detect service accounts forbidden failure access
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 = `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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure detect suspicious kubectl calls
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 = `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 = {"kill_chain_phases": ["Reconnaissance"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Reconnaissance"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure pod scan fingerprint
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 = `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Kubernetes Azure scan fingerprint
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 = `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 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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Kubernetes GCP detect RBAC authorizations by account
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.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 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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Kubernetes GCP detect most active service accounts by pod
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.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 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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Kubernetes GCP detect sensitive object access
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.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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Kubernetes GCP detect sensitive role access
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.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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Kubernetes GCP detect service accounts forbidden failure access
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` 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 = {"kill_chain_phases": ["Lateral Movement"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Kubernetes GCP detect suspicious kubectl calls
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.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 - 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}]}
schedule_window = auto
action.notable = 1
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}]}
schedule_window = auto
action.notable = 1
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "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", "Dev Sec Ops Analytics"]
action.escu.providing_technologies = []
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 = [{"threat_object_field": "host", "threat_object_type": "entity"}]
action.risk.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, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "host", "type": "Entity"}]}
schedule_window = auto
action.notable = 1
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 - 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 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1498", "T1498.002"], "nist": ["PR.PT", "DE.AE", "PR.IP"]}
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 = []
action.escu.analytic_story = ["DNS Amplification Attacks"]
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 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1498", "T1498.002"], "nist": ["PR.PT", "DE.AE", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Large Volume of DNS ANY Queries
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 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
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 = 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 for Linux, you will need to ensure mapping is occurring correctly. Ensure EDR product is mapping OS Linux to the datamodel properly. Add any additional java process names for your environment to the analytic as needed. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = 2021-12-13
action.escu.modification_date = 2021-12-13
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 = []
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 Linux shell, potentially indicative of exploitation.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 40}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Log4Shell CVE-2021-44228"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - 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 highly suspect. Why is it needed? In most malicious instances, 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055", "T1055.001"]}
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 highly suspect. Why is it needed? In most malicious instances, 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 = 2021-11-18
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 = []
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = dynwrapx.dll loaded by process $process_name$ on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055", "T1055.001"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 highly suspect. Why is it needed? In most malicious instances, 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.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 Computer EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `loading_of_dynwrapx_module_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local user discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = Local 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 `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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local user discovery enumeration on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087", "T1087.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = Local Account 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 `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 - 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 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = []
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. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = []
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
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.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"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 30, "context": ["Source:Application Log", "Stage:Execution"], "cve": ["CVE-2021-44228"], "impact": 50, "kill_chain_phases": ["Reconnaissance", "Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Log4Shell JNDI Payload Injection Attempt
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 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 `${jndi:ldap://PAYLOAD_INJECTED}`, when executed against vulnerable web applications the invocation can be seen in various part of web logs. Specifically it has been successfully exploited via headers like X-Forwarded-For, User-Agent, Referer, and X-Api-Version. In this detection we match the invocation function with a network connection to a malicious ip address.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = CVE-2021-44228 Log4Shell payloads can be injected via various methods, but on of the most common vectors injection is via Web calls. Many of the vulnerable java web applications that are using log4j have a web component to them are specially targets of this injection, specifically projects like Apache Struts, Flink, Druid, and Solr. The exploit is triggered by a LDAP lookup function in the log4j package, its invocation is similar to `${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. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = []
action.escu.analytic_story = ["Log4Shell CVE-2021-44228"]
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.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"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 30, "context": ["Source:Application Log", "Stage:Execution"], "cve": ["CVE-2021-44228"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Log4Shell JNDI Payload Injection with Outbound Connection
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 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1037", "T1037.001"]}
action.escu.data_models = []
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 = 2021-09-27
action.escu.modification_date = 2021-09-27
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 100, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1037", "T1037.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"]}
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 = []
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = MS Scripting Process Loading Ldap 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 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 | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"]}
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 = []
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.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.007"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = MS Scripting Process Loading WMI 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 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 | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1127"]}
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 = 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 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 = []
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.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"], "confidence": 70, "context": ["Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1127"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 2021-09-09
action.escu.modification_date = 2021-09-09
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 = []
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 endpoint $dest$ loading mshtml.dll.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-40444"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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  process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.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 Computer, process_name, ImageLoaded, OriginalFileName, process_id | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mshtml_module_load_in_office_product_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 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "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 = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model.
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 = []
action.escu.analytic_story = []
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 = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1114", "T1114.001"]}
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 = 2021-05-19
action.escu.modification_date = 2021-05-19
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 = []
action.escu.analytic_story = ["Data Exfiltration"]
action.risk = 1
action.risk.param._risk_message = mailsniper.ps1 functions $Message$ executed on a $ComputerName$ 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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1114", "T1114.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message 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 EventCode Message ComputerName 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.010", "T1112"]}
action.escu.data_models = []
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 = 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, 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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.010", "T1112"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 Process - 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 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common.
action.escu.creation_date = 2021-10-05
action.escu.modification_date = 2021-10-05
action.escu.confidence = high
action.escu.full_search_name = ESCU - Malicious PowerShell Process - 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 = []
action.escu.analytic_story = ["Malicious PowerShell", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "HAFNIUM Group", "Log4Shell CVE-2021-44228"]
action.risk = 1
action.risk.param._risk_message = PowerShell processes $process$ started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet on host $dest$ executed by user $user$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}, {"threat_object_field": "process", "threat_object_type": "process"}]
action.risk.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 - Connect To Internet With Hidden Window - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "HAFNIUM Group", "Log4Shell CVE-2021-44228"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Command And Control"], "cve": ["CVE-2021-44228"], "impact": 90, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process", "role": ["Attacker"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Malicious PowerShell Process - Connect To Internet With Hidden Window
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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+[^-]") | `malicious_powershell_process___connect_to_internet_with_hidden_window_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 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
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 = 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 = System administrators may use this option, but it's not common.
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 PowerShell Process - Encoded Command - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Malicious PowerShell", "NOBELIUM Group"]
action.risk = 1
action.risk.param._risk_message = Powershell.exe running potentially malicious encodede commands on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}]
action.risk.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 - Encoded Command - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "NOBELIUM Group"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = Malicious PowerShell Process - Encoded 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 `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 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["DHS Report TA18-074A", "HAFNIUM Group"]
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.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"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 - Multiple Suspicious 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 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Malicious PowerShell"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Deprecated - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Malicious PowerShell Process - Multiple Suspicious 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* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_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 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
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 = 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 = These characters might be legitimately on the command-line, but it is 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 - 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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
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.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"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - 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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569", "T1569.002"]}
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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569", "T1569.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
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 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1021", "T1021.003"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1021", "T1021.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1491"]}
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 = []
action.escu.analytic_story = ["Ransomware", "Revil Ransomware", "BlackMatter 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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1491"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"]}
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 = 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 cacls.exe may be used.
action.escu.known_false_positives = administrators may use this command. 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 - 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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Modify ACL permission To Files Or 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(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 everyone:*"  OR Processes.process = "*/G SYSTEM:*") 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 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 = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]}
action.escu.data_models = ["Network_Resolution"]
action.escu.eli5 = 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.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.
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 = []
action.escu.analytic_story = ["Brand Monitoring"]
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"], "kill_chain_phases": ["Delivery", "Actions on Objectives"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.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 - 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 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
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 = []
action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"]
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 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
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 - 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 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.010", "T1547"], "nist": ["PR.PT", "DE.CM", "PR.AC"]}
action.escu.data_models = []
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 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 = You will encounter noise from legitimate print-monitor registry entries.
action.escu.creation_date = 2020-11-23
action.escu.modification_date = 2020-11-23
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 = []
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
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.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"], "cis20": ["CIS 8", "CIS 5"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Persistence", "Stage:Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.010", "T1547"], "nist": ["PR.PT", "DE.CM", "PR.AC"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
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 min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_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 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
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 = []
action.escu.analytic_story = ["Brand Monitoring"]
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 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src']
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Trickbot", "IcedID"]
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.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "executions"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1574.002", "T1574"]}
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 = 2021-07-05
action.escu.modification_date = 2021-07-05
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 = []
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1574.002", "T1574"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
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 Filesystem.file_path != "*\\Program Files\\windows defender\\*" by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1048.003", "T1048"]}
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 = []
action.escu.analytic_story = ["Command and Control", "Data Exfiltration"]
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": "uripath"}, {"threat_object_field": "form_data", "threat_object_type": "formdata"}]
action.risk.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": ["Command and Control", "Data Exfiltration"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 50, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1048.003", "T1048"], "observable": [{"name": "uri_path", "role": ["Attacker"], "type": "UriPath"}, {"name": "form_data", "role": ["Attacker"], "type": "formdata"}]}
schedule_window = auto
action.notable = 1
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 - Multiple Disabled Users Failing To Authenticate From Host 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-14
action.escu.modification_date = 2021-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Disabled Users Failing To Authenticate From Host Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $Client_Address$
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Disabled Users Failing To Authenticate From Host Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "Client_Address", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Disabled Users Failing To Authenticate From Host 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 Account_Name!="*$" Result_Code=0x12 | 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 | `multiple_disabled_users_failing_to_authenticate_from_host_using_kerberos_filter` 

[ESCU - Multiple Invalid Users Failing To Authenticate From Host 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-14
action.escu.modification_date = 2021-04-14
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Invalid Users Failing To Authenticate From Host Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $Client_Address$
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Invalid Users Failing To Authenticate From Host Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "Client_Address", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Invalid Users Failing To Authenticate From Host 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 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 | `multiple_invalid_users_failing_to_authenticate_from_host_using_kerberos_filter` 

[ESCU - Multiple Invalid Users Failing 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-15
action.escu.modification_date = 2021-04-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Invalid Users Failing 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 = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential NTLM based password spraying attack from $Source_Workstation$
action.risk.param._risk = [{"risk_object_field": "Source_Workstation", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Invalid Users Failing To Authenticate From Host Using NTLM - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "Source_Workstation", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Invalid Users Failing 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 Logon_Account!="*$" 0xC0000064 action=failure | bucket span=2m _time | stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_invalid_users_failing_to_authenticate_from_host_using_ntlm_filter`

[ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.
action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.
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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
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 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.
action.notable.param.rule_title = Multiple Okta Users With Invalid Credentials From The Same IP
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=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(user) as distinct_users values(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 - Multiple Users Attempting To Authenticate Using Explicit Credentials - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Users Attempting To Authenticate Using Explicit Credentials - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack from $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Users Attempting To Authenticate Using Explicit Credentials - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Users Attempting To Authenticate Using Explicit Credentials
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 | bucket span=2m _time | eval Source_Account = mvindex(Account_Name, 0) | eval Destination_Account = mvindex(Account_Name, 1) | search Source_Account != "*$" Source_Account !="-" Destination_Account !="*$" | stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_account by _time, ComputerName, Source_Account | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_attempting_to_authenticate_using_explicit_credentials_filter` 

[ESCU - Multiple Users Failing To Authenticate From Host 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-08
action.escu.modification_date = 2021-04-08
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Users Failing To Authenticate From Host Using Kerberos - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential Kerberos based password spraying attack from $Client_Address$
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Users Failing To Authenticate From Host Using Kerberos - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "Client_Address", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Users Failing To Authenticate From Host 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 Failure_Code=0x18 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 | `multiple_users_failing_to_authenticate_from_host_using_kerberos_filter`

[ESCU - Multiple Users Failing 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Users Failing 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 = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential NTLM based password spraying attack from $Source_Workstation$
action.risk.param._risk = [{"risk_object_field": "Source_Workstation", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Users Failing To Authenticate From Host Using NTLM - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "Source_Workstation", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Users Failing 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 Logon_Account!="*$" 0xC000006A action=failure | bucket span=2m _time | stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_host_using_ntlm_filter`

[ESCU - Multiple Users Failing 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Users Failing To Authenticate From Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack from $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Users Failing To Authenticate From Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Users Failing 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 Caller_Process_Name!="-" | bucket span=2m _time | eval Source_Account = mvindex(Account_Name, 0) | eval Destination_Account = mvindex(Account_Name, 1) | stats dc(Destination_Account) AS unique_accounts values(Account_Name) as tried_accounts by _time, Caller_Process_Name, Source_Account, ComputerName | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Caller_Process_Name, Source_Account, ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_process_filter` 

[ESCU - Multiple Users Remotely Failing 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"]}
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 = 2021-04-13
action.escu.modification_date = 2021-04-13
action.escu.confidence = high
action.escu.full_search_name = ESCU - Multiple Users Remotely Failing To Authenticate From Host - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Password Spraying"]
action.risk = 1
action.risk.param._risk_message = Potential password spraying attack on $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 49}]
action.risk.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 Users Remotely Failing To Authenticate From Host - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Password Spraying"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003", "T1110"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Multiple Users Remotely Failing 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 Source_Network_Address!="-" | bucket span=2m _time | eval Destination_Account = mvindex(Account_Name, 1) | stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_accounts by _time, Source_Network_Address, ComputerName | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Network_Address, ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_remotely_failing_to_authenticate_from_host_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = 2021-07-12
action.escu.modification_date = 2021-07-12
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 = []
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 $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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_name = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "nist": ["PR.PT", "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 = 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 = Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.
action.escu.creation_date = 2021-01-25
action.escu.modification_date = 2021-01-25
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 = []
action.escu.analytic_story = ["Ryuk Ransomware", "Domain Trust Discovery", "IcedID", "Active Directory Discovery"]
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.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"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"]}
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 = 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 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 - Net Localgroup Discovery - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Discovery", "Windows Discovery Techniques"]
action.risk = 1
action.risk.param._risk_message = Local group discovery 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.verbose = 0
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": ["Active Directory Discovery", "Windows Discovery Techniques"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Net Localgroup 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=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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Network Connection discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = Network Connection Discovery With Arp
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Network Connection discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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` 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.notable.param.rule_title = Network Connection 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=*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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Network Connection discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1049"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = Network Connection Discovery With Netstat
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1016", "T1016.001"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Network Connection discovery on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}]
action.risk.param.verbose = 0
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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1016", "T1016.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Network Discovery Using Route 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.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 - New container uploaded to AWS ECR - Rule]
action.escu = 0
action.escu.enabled = 1
description = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model.
action.escu.mappings = {"mitre_attack": ["T1525"]}
action.escu.data_models = []
action.escu.eli5 = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model.
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 install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives.
action.escu.known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry.
action.escu.creation_date = 2020-02-20
action.escu.modification_date = 2020-02-20
action.escu.confidence = high
action.escu.full_search_name = ESCU - New container uploaded to AWS ECR - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - New container uploaded to AWS ECR - Rule
action.correlationsearch.annotations = {"analytic_story": ["Container Implantation Monitoring and Investigation"], "mitre_attack": ["T1525"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'src']
action.notable.param.rule_description = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model.
action.notable.param.rule_title = New container uploaded to AWS ECR
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 min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 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 = 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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Command and Control"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - 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 18"], "nist": ["PR.PT", "PR.MA"]}
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 = []
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 18"], "nist": ["PR.PT", "PR.MA"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = No Windows Updates in a time frame
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 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 - 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 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 a noise coming from firefox need to be filter and also sqlite db browser and explorer .exe to make this detection more stable.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1555", "T1555.003"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect an anomaly event of 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 a noise coming from firefox need to be filter and also 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 = 2021-09-15
action.escu.modification_date = 2021-09-15
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 = []
action.escu.analytic_story = ["FIN7", "Remcos"]
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.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": ["FIN7", "Remcos"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1555", "T1555.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search is to detect an anomaly event of 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 a noise coming from firefox need to be filter and also sqlite db browser and explorer .exe to make this detection more stable.
action.notable.param.rule_title = Non Chrome Process Accessing Chrome Default Dir
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 non-firefox process accessing the files in  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) need to be enabled to tthe firefox profile directory to be eable to use this. Since you monitoring this access to the folder a noise coming from firefox need to be filter and also sqlite db browser and explorer .exe to make this detection more stable.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1555", "T1555.003"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect an anomaly event of non-firefox process accessing the files in  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) need to be enabled to tthe firefox profile directory to be eable to use this. Since you monitoring this access to the folder a noise coming from firefox need to be filter and also 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 = 2021-09-15
action.escu.modification_date = 2021-09-15
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 = []
action.escu.analytic_story = ["FIN7", "Remcos"]
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.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": ["FIN7", "Remcos"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1555", "T1555.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search is to detect an anomaly event of non-firefox process accessing the files in  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) need to be enabled to tthe firefox profile directory to be eable to use this. Since you monitoring this access to the folder a noise coming from firefox need to be filter and also sqlite db browser and explorer .exe to make this detection more stable.
action.notable.param.rule_title = Non Firefox Process Access Firefox Profile Dir
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 - 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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Credential Dumping", "HAFNIUM Group"]
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.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"], "cis20": ["CIS 8", "CIS 16"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 100, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003", "T1003"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - O365 Add App Role Assignment Grant User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.
action.escu.mappings = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003", "T1136"]}
action.escu.data_models = []
action.escu.eli5 = This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.
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 = 2021-01-26
action.escu.modification_date = 2021-01-26
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $Actor.ID$ has created a new federation setting on $dest$ from IP Address $ActorIpAddress$
action.risk.param._risk = [{"risk_object_field": "ActorIpAddress", "risk_object_type": "system", "risk_score": 18}, {"risk_object_field": "Actor.ID", "risk_object_type": "user", "risk_score": 18}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 18}]
action.risk.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"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 30, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003", "T1136"], "observable": [{"name": "ActorIpAddress", "role": ["Attacker"], "type": "IP Address"}, {"name": "Actor.ID", "role": ["Attacker"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 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 by ActorIpAddress dest ResultStatus | `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 = This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.
action.escu.mappings = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003", "T1136"]}
action.escu.data_models = []
action.escu.eli5 = This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.
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 = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Added Service Principal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $Actor.ID$ created a new federation setting on $Target.ID$ and added service principal credentials from IP Address $ActorIpAddress$
action.risk.param._risk = [{"risk_object_field": "ActorIpAddress", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "Target.ID", "risk_object_type": "system", "risk_score": 42}]
action.risk.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"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003", "T1136"], "observable": [{"name": "ActorIpAddress", "role": ["Attacker"], "type": "IP Address"}, {"name": "Target.ID", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
alert.digest_mode = 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 signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `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 = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1562.007", "T1562"]}
action.escu.data_models = []
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 = 2021-07-19
action.escu.modification_date = 2021-07-19
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1562.007", "T1562"], "observable": [{"name": "ip_addresses_new_added", "role": ["Attacker"], "type": "IP Address"}, {"name": "user_id", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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` signature="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 signature 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 = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"]}
action.escu.data_models = []
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 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 = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Disable MFA - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections"]
action.risk = 1
action.risk.param._risk_message = User $user$ has executed an operation $Operation$ for this destination $dest$
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.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"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 user status signature dest ResultStatus |`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 = {"kill_chain_phases": ["Not Applicable"], "mitre_attack": ["T1110"]}
action.escu.data_models = []
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 = 2020-12-16
action.escu.modification_date = 2020-12-16
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution", "Stage:Initial Access"], "impact": 80, "kill_chain_phases": ["Not Applicable"], "mitre_attack": ["T1110"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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=Failed | stats count earliest(_time) as firstTime latest(_time) 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 = This search 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 = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"]}
action.escu.data_models = []
action.escu.eli5 = This search 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 = 2021-01-26
action.escu.modification_date = 2021-01-26
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $UserId$ has caused excessive number of SSO logon errors from $ActorIpAddress$ using UserAgent $UserAgent$.
action.risk.param._risk = [{"risk_object_field": "ActorIpAddress", "risk_object_type": "system", "risk_score": 64}, {"risk_object_field": "UserId", "risk_object_type": "user", "risk_score": 64}]
action.risk.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"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution", "Stage:Initial Access"], "impact": 80, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"], "observable": [{"name": "ActorIpAddress", "role": ["Attacker"], "type": "IP Address"}, {"name": "UserId", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | 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 = This search detects the addition of a new Federated domain.
action.escu.mappings = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003", "T1136"]}
action.escu.data_models = []
action.escu.eli5 = This search detects the addition of a new Federated domain.
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 = 2021-01-26
action.escu.modification_date = 2021-01-26
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "Cloud Federated Credential Abuse"]
action.risk = 1
action.risk.param._risk_message = User $UserId$ has added a new federated domaain $Parameters.Value$ for $OrganizationName$
action.risk.param._risk = [{"threat_object_field": "OrganizationName", "threat_object_type": "other"}, {"risk_object_field": "UserId", "risk_object_type": "user", "risk_score": 64}]
action.risk.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"], "confidence": 80, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Execution", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003", "T1136"], "observable": [{"name": "OrganizationName", "role": ["Victim"], "type": "Other"}, {"name": "UserId", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `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 = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1114"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Exfiltration"], "impact": 80, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1114"], "observable": [{"name": "Source", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.DP", "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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Outcome:Allowed", "Stage:Exfiltration"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002", "T1114"], "nist": ["DE.DP", "DE.AE"]}
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 Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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.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 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Stage:Exfiltration", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002", "T1114"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}]}
schedule_window = auto
alert.digest_mode = 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 = This search detects when multiple user configured a forwarding rule to the same destination.
action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search detects when multiple user configured a forwarding rule 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 User Email Forwarding - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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}]
action.risk.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 16"], "confidence": 60, "context": ["Source:Cloud Data", "Scope:External", "Stage:Exfiltration", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003", "T1114"], "nist": ["DE.DP", "DE.AE"], "observable": [{"name": "user", "role": ["Attacker"], "type": "User"}, {"name": "ForwardingSmtpAddress", "role": ["Other"], "type": "Email Address"}]}
schedule_window = auto
alert.digest_mode = 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 - Office Application Drop Executable - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a suspicious MS office application that drop or create executables or script in the host. 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a suspicious MS office application that drop or create executables or script in the host. 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 = 2021-09-13
action.escu.modification_date = 2021-09-13
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 = []
action.escu.analytic_story = ["FIN7"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ drops a file $TargetFilename$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:recon"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search is to detect a suspicious MS office application that drop or create executables or script in the host. 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") 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 ("*.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)` | 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_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = 2021-07-30
action.escu.modification_date = 2021-07-30
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 = []
action.escu.analytic_story = ["IcedID"]
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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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") `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 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 trickbot that used MS office as its weapon or attack vector to initially infect the machines.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 trickbot that used MS office as its weapon or attack vector to initially infect the machines.
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 = 2021-04-13
action.escu.modification_date = 2021-04-13
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 = []
action.escu.analytic_story = ["Spearphishing Attachments", "Trickbot", "IcedID"]
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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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") 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 2021-04-14
action.escu.modification_date = 2021-04-14
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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded = "*\\taskschd.dll" | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 2021-04-14
action.escu.modification_date = 2021-04-14
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 = []
action.escu.analytic_story = ["Spearphishing Attachments", "Trickbot", "IcedID"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.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 | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 office application and browser may be used.
action.escu.known_false_positives = Default browser not in the filter list.
action.escu.creation_date = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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") 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"]}
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 = 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 or network admin may create an document automation that will run shell script.
action.escu.creation_date = 2021-07-19
action.escu.modification_date = 2021-07-19
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 = []
action.escu.analytic_story = ["Trickbot"]
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.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "executions"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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") `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = No false positives known. Filter as needed.
action.escu.creation_date = 2021-04-26
action.escu.modification_date = 2021-04-26
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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]}
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") `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = No false positives known. Filter as needed.
action.escu.creation_date = 2021-04-26
action.escu.modification_date = 2021-04-26
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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]}
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") `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = No false positives known. Filter as needed.
action.escu.creation_date = 2021-04-26
action.escu.modification_date = 2021-04-26
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 = []
action.escu.analytic_story = ["Spearphishing Attachments", "IcedID"]
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]}
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") `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 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 - 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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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 name"}]
action.risk.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
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") `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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = No false positives known. 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 - Office Product Spawning Wmic - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Spearphishing Attachments", "FIN7"]
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]}
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") `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
action.escu.data_models = []
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 = 2021-09-10
action.escu.modification_date = 2021-09-10
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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-40444"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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") by _time span=1h Processes.process_id Processes.process_name Processes.process 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_name IN ("*.inf","*.cab") 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, process_name, process, file_create_time, file_name, file_path | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = Limited false positives should be present.
action.escu.creation_date = 2021-09-08
action.escu.modification_date = 2021-09-08
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-40444"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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") 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 - Okta Account Lockout Events - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect Okta user lockout events
action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect Okta user lockout events
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 = 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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
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 Account Lockout Events - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = Detect Okta user lockout events
action.notable.param.rule_title = Okta Account Lockout Events
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` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` 

[ESCU - Okta Failed SSO Attempts - Rule]
action.escu = 0
action.escu.enabled = 1
description = Detect failed Okta SSO events
action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect failed Okta SSO events
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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
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 Failed SSO Attempts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = Detect failed Okta SSO events
action.notable.param.rule_title = Okta Failed SSO Attempts
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` displayMessage="User attempted unauthorized access to app" | stats  min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_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 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
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 = []
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 User Logins From Multiple Cities - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_description = This search detects logins from the same user from different cities in a 24 hour period.
action.notable.param.rule_title = Okta User Logins From Multiple Cities
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` 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 - 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 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Splunk Enterprise Vulnerability"]
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 Enterprise Vulnerability"], "cis20": ["CIS 3", "CIS 4", "CIS 18"], "cve": ["CVE-2016-4859"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.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 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["ColdRoot MacOS RAT"]
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 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
action.escu.data_models = []
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 = 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. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
action.escu.known_false_positives = Legitimate Java applications may use perform outbound connections to these ports. 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 - 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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution"], "cve": ["CVE-2021-44228"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 =  `sysmon` EventCode=3  (process_name=java OR process_name=java.exe) (DestinationPort=389 OR DestinationPort=1389 OR DestinationPort = 1099 ) | rename Computer as dest |  stats count min(_time) as firstTime max(_time) as lastTime by dest, process_name, DestinationPort | `security_content_ctime(firstTime)` | `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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546", "T1546.008"], "nist": ["PR.PT", "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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
action.escu.analytic_story = ["Windows Privilege Escalation"]
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_path"}]
action.risk.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": ["Windows Privilege Escalation"], "cis20": ["CIS 8"], "confidence": 90, "context": ["source:endpoint", {"stage": "privilege escalation"}], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546", "T1546.008"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_path", "role": ["Attacker"], "type": "file_path"}]}
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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"]}
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 = 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 = 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 = []
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": 9}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 9}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}]
action.risk.param.verbose = 0
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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1201"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}]}
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 used to obtain the domain password policy. Red Teams and adversaries may leverage `net.exe` for situational awareness and Active Directory Discovery.
action.notable.param.rule_title = Password Policy 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") 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Ransomware"]
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 name"}]
action.risk.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
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 = {"kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1187"]}
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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Credential Access"], "cve": ["CVE-2021-36942"], "impact": 80, "kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1187"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1003"]}
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 = []
action.escu.analytic_story = ["PetitPotam NTLM Relay on Active Directory Certificate Services"]
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Credential Access"], "cve": ["CVE-2021-36942"], "impact": 80, "kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 - 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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1048.003", "T1048"]}
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 = []
action.escu.analytic_story = ["Command and Control", "Data Exfiltration"]
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": "uripath"}, {"threat_object_field": "form_data", "threat_object_type": "formdata"}]
action.risk.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": ["Command and Control", "Data Exfiltration"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 70, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1048.003", "T1048"], "observable": [{"name": "uri_path", "role": ["Attacker"], "type": "UriPath"}, {"name": "form_data", "role": ["Attacker"], "type": "formdata"}]}
schedule_window = auto
action.notable = 1
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 - Possible Browser Pass View Parameter - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic will detect a suspicious process contains a commandline parameter related to web browser credential dumper. This technique was used by Remcos RAT malware where it use the techique of Nirsoft webbrowserpassview.exe application to dump web browser credentials. Remcos use the "/stext" commandline to dump the credential in text format. This Hunting query is good indicator to look further for possible remcos infection within the network or possible compromised host. Since the detections is only base on the parameter command and the possible path where it will drop the text credential information, It may catch normal tools that having same command and behavior.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1555.003", "T1555"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic will detect a suspicious process contains a commandline parameter related to web browser credential dumper. This technique was used by Remcos RAT malware where it use the techique of Nirsoft webbrowserpassview.exe application to dump web browser credentials. Remcos use the "/stext" commandline to dump the credential in text format. This Hunting query is good indicator to look further for possible remcos infection within the network or possible compromised host. Since the detections is only base on the parameter command and the possible path where it will drop the text credential information, It may catch normal tools that having same command and behavior.
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 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 = []
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = suspicious process $process_name$ contains commandline $process$ on $dest$
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}]
action.risk.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 Browser Pass View Parameter - Rule
action.correlationsearch.annotations = {"analytic_story": ["Remcos"], "confidence": 40, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1555.003", "T1555"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 contains a commandline parameter related to web browser credential dumper. This technique was used by Remcos RAT malware where it use the techique of Nirsoft webbrowserpassview.exe application to dump web browser credentials. Remcos use the "/stext" commandline to dump the credential in text format. This Hunting query is good indicator to look further for possible remcos infection within the network or possible compromised host. Since the detections is only base on the parameter command and the possible path where it will drop the text credential information, It may catch normal tools that having same command and behavior.
action.notable.param.rule_title = Possible Browser Pass View 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  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 assists with identifying a PowerShell process spawned as a child or grand child process of commonly abused processes during lateral movement techniques including `services.exe`, `wmiprsve.exe`, `svchost.exe`, `wsmprovhost.exe` and `mmc.exe`. Legitimate Windows features such as the Service Control Manager, Windows Management Instrumentation, Task Scheduler, Windows Remote Management and the DCOM protocol can be abused to start a process on a remote endpoint. Looking for PowerShell spawned out of this processes may reveal a lateral movement attack. Red Teams and adversaries alike may abuse these services during a breach for lateral movement and remote code execution.
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement", "Malicious PowerShell"], "mitre_attack": ["T1021", "T1021.003", "T1021.006", "T1047", "T1053.005", "T1543.003", "T1059.001"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic assists with identifying a PowerShell process spawned as a child or grand child process of commonly abused processes during lateral movement techniques including `services.exe`, `wmiprsve.exe`, `svchost.exe`, `wsmprovhost.exe` and `mmc.exe`. Legitimate Windows features such as the Service Control Manager, Windows Management Instrumentation, Task Scheduler, Windows Remote Management and the DCOM protocol can be abused to start a process on a remote endpoint. Looking for PowerShell spawned out of this processes may reveal a lateral movement attack. Red Teams and adversaries alike may abuse these services during a breach for lateral movement and remote code execution.
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.
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 = 2021-11-29
action.escu.modification_date = 2021-11-29
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement", "Malicious PowerShell"]
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement", "Malicious PowerShell"], "mitre_attack": ["T1021", "T1021.003", "T1021.006", "T1047", "T1053.005", "T1543.003", "T1059.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The following analytic assists with identifying a PowerShell process spawned as a child or grand child process of commonly abused processes during lateral movement techniques including `services.exe`, `wmiprsve.exe`, `svchost.exe`, `wsmprovhost.exe` and `mmc.exe`. Legitimate Windows features such as the Service Control Manager, Windows Management Instrumentation, Task Scheduler, Windows Remote Management and the DCOM protocol can be abused to start a process on a remote endpoint. Looking for PowerShell spawned out of this processes may reveal a lateral movement attack. Red Teams and adversaries alike may abuse these services during a breach 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 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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 2021-08-18
action.escu.modification_date = 2021-08-18
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
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 suspicious commands.
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": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 4104 Hunting - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = PowerShell 4104 Hunting
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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(Message,"(?i)(\$doit)"), "4", 0) | eval enccom=if(match(Message,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4}|[A-Za-z0-9+\/]{3}=|[A-Za-z0-9+\/]{2}==)") OR match(Message, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) | eval suspcmdlet=if(match(Message, "(?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(Message),"frombase64"), "4", 0) | eval empire=if(match(lower(Message),"system.net.webclient") AND match(lower(Message), "frombase64string") ,5,0) | eval mimikatz=if(match(lower(Message),"mimikatz") OR match(lower(Message), "-dumpcr") OR match(lower(Message), "SEKURLSA::Pth") OR match(lower(Message), "kerberos::ptt") OR match(lower(Message), "kerberos::golden") ,5,0) | eval iex = if(match(lower(Message),"iex"), "2", 0) | eval webclient=if(match(lower(Message),"http") OR match(lower(Message),"web(client|request)") OR match(lower(Message),"socket") OR match(lower(Message),"download(file|string)") OR match(lower(Message),"bitstransfer") OR match(lower(Message),"internetexplorer.application") OR match(lower(Message),"xmlhttp"),5,0) | eval get = if(match(lower(Message),"get-"), "1", 0) | eval rundll32 = if(match(lower(Message),"rundll32"), "4", 0) | eval suspkeywrd=if(match(Message, "(?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(Message),"syswow64"), "3", 0) | eval httplocal = if(match(lower(Message),"http://127.0.0.1"), "4", 0) | eval reflection = if(match(lower(Message),"reflection"), "1", 0) | eval invokewmi=if(match(lower(Message), "(?i)(wmiobject|WMIMethod|RemoteWMI|PowerShellWmi|wmicommand)"),5,0) | eval downgrade=if(match(Message, "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(Message),"powershell -version"),3,0) | eval compressed=if(match(Message, "(?i)GZipStream|::Decompress|IO.Compression|write-zip|(expand|compress)-Archive"),5,0) | eval invokecmd = if(match(lower(Message),"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 DoIt, enccom, compressed, downgrade, iex, mimikatz, rundll32, empire, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get, suspcmdlet, suspkeywrd | `powershell_4104_hunting_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains domain enumeration command in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 42}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 42}]
action.risk.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"], "confidence": 70, "context": ["source:endpoint", {"stage": "recon"}], "impact": 60, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message IN (*get-netdomaintrust*, *get-netforesttrust*, *get-addomain*, *get-adgroupmember*, *get-domainuser*) | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_domain_enumeration_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"]}
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 = 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 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local group discovery 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = PowerShell Get LocalGroup 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=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 Loading DotNET into Memory via System Reflection Assembly - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - PowerShell Loading DotNET into Memory via System Reflection Assembly - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains reflective class assembly  command in $Message$ to load .net code in memory with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 56}]
action.risk.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 System Reflection Assembly - Rule
action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 System Reflection Assembly
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*") | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_loading_dotnet_into_memory_via_system_reflection_assembly_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"]}
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 = 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 = 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 = []
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.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027", "T1027.005"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains Thread Mutex in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 40}]
action.risk.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027", "T1027.005"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*Threading.Mutex*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 = Limited false positives. However, tune based on scripts that may perform this action.
action.escu.creation_date = 2021-07-05
action.escu.modification_date = 2021-07-05
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 = []
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 = 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*") 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027", "T1027.005"]}
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 = 2021-06-22
action.escu.modification_date = 2021-06-22
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 = []
action.escu.analytic_story = ["Malicious PowerShell", "Ransomware"]
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": ["Malicious PowerShell", "Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027", "T1027.005"]}
schedule_window = auto
action.notable = 1
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 Message = "*Enable-WindowsOptionalFeature*" Message = "*SMB1Protocol*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.015", "T1546"]}
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 = 2021-08-10
action.escu.modification_date = 2021-08-10
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 = []
action.escu.analytic_story = ["Malicious PowerShell", "Ransomware"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains COM CLSID command in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 5}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 5}]
action.risk.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": ["Malicious PowerShell", "Ransomware"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 10, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.015", "T1546"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*CreateInstance([type]::GetTypeFromCLSID*" OR Message = "*CreateInstance([Type]::GetTypeFromProgID*"| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = $var_unsafe_native_methods.GetMethod(GetProcAddress` and later referenced/executed elsewhere. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1055", "T1059.001"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `GetProcAddress` in the script block. This is not normal to be used by most PowerShell scripts and is typically unsafe/malicious. Many attack toolkits use GetProcAddress to obtain code execution. \
In use, `$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 = 2021-06-08
action.escu.modification_date = 2021-06-08
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains GetProcAddress API in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 48}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 48}]
action.risk.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "recon"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1055", "T1059.001"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message=*getprocaddress* | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `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[]]$var_code = [System.Convert]::FromBase64String(38uqIyMjQ6rG....` \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.
action.escu.mappings = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059", "T1027", "T1059.001"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies `FromBase64String` within the script block. A typical malicious instance will include additional code. \
Command example - `[Byte[]]$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 = 2021-06-08
action.escu.modification_date = 2021-06-08
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains base64 command in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 56}]
action.risk.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": ["Malicious PowerShell"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059", "T1027", "T1059.001"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message=*frombase64string* | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_fileless_script_contains_base64_encoded_content_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"]}
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 = 2021-09-14
action.escu.modification_date = 2021-09-14
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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local group discovery 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 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.notable.param.rule_title = Powershell Get LocalGroup Discovery 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 = "*get-localgroup*" | stats count min(_time) as firstTime max(_time) as lastTime by Message OpCode ComputerName User EventCode| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_with_script_block_logging_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains stream command in $Message$ commonly for processing compressed or to decompressed binary file with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 40}]
action.risk.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1059.001"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*IO.Compression.*" OR Message = "*IO.StreamReader*" OR Message = "*]::Decompress*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
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 = 2021-04-19
action.escu.modification_date = 2021-04-19
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 = []
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 $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Defense Evasion"}, "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
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 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 Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_thread_to_known_windows_process_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1140"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains memorystream command in $Message$ as new object backstore with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 40}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 40}]
action.risk.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1140"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 Message = "*New-Object IO.MemoryStream*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_using_memory_as_backing_store_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 = 2021-11-25
action.escu.modification_date = 2021-11-25
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 = []
action.escu.analytic_story = ["Remcos", "Windows Defense Evasion Tactics"]
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.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": ["Remcos", "Windows Defense Evasion Tactics"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "User", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
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 = 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 bcdedit.exe may be used.
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 = []
action.escu.analytic_story = ["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.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
action.escu.data_models = []
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 = 2021-09-28
action.escu.modification_date = 2021-09-28
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 100, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
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 = []
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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "cve": ["CVE-2021-34527", "CVE-2021-1675"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
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 = []
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.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "cve": ["CVE-2021-34527", "CVE-2021-1675"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566", "T1566.002"], "nist": ["ID.AM", "PR.DS"]}
action.escu.data_models = []
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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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.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"], "cis20": ["CIS 7", "CIS 8"], "confidence": 90, "context": ["source:endpoint", {"stage": "Initial Access"}], "impact": 70, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566", "T1566.002"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070"]}
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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 - 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 = []
action.escu.analytic_story = ["Clop Ransomware", "Remcos"]
action.risk = 1
action.risk.param._risk_message = A process $Image$ tries to delete its process path in commandline $cmdline$ as part of defense evasion 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.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", "Remcos"], "confidence": 100, "context": ["source:endpoint", {"stage": "Credential Access"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
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 cmdline = "*/c 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 Computer user ParentImage ParentCommandLine Image cmdline 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 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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.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 3", "CIS 5"], "confidence": 70, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 = 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 = 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 = []
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.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"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1559.001"]}
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, 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 = []
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = An instance of $process_name$ was identified on endpoint $dest$ downloading the DynamicWrapperX dll.
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.verbose = 0
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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059", "T1559.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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, 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.notable.param.rule_title = Process Writing DynamicWrapperX
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]}
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 = []
action.escu.analytic_story = ["ColdRoot MacOS RAT"]
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 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]}
schedule_window = auto
action.notable = 1
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 - 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes 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 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 = []
action.escu.analytic_story = ["Netsh Abuse"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.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 - 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004", "T1562"], "nist": ["PR.PT", "DE.CM"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Netsh Abuse", "Disabling Security Tools", "DHS Report TA18-074A"]
action.risk = 1
action.risk.param._risk_message = A process $process_name$ that tries to execute netsh commandline $process$ 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.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"], "cis20": ["CIS 8"], "confidence": 70, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004", "T1562"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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. 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.notable.param.rule_title = Processes launching 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 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 - 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 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "PR.AC"]}
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 = []
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"]
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 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "PR.AC"]}
schedule_window = auto
action.notable = 1
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 - 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 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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. 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 usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search <code>Add Prohibited Processes to Enterprise Security</code>.
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 = []
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 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Prohibited Software 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_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` | `prohibited_software_on_endpoint_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 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE", "PR.AC"]}
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 = []
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"]
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 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003", "T1048"], "nist": ["DE.AE", "PR.AC"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Protocol or Port Mismatch
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=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 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]}
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 = []
action.escu.analytic_story = ["Use of Cleartext Protocols"]
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 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest', 'src']
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 - 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 = {"kill_chain_phases": ["Privilege Escalation", "Lateral Movement", "Persistence"], "mitre_attack": ["T1053", "T1053.005"]}
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A windows scheduled task with a suspicious task name was created on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 45}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Persistence", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Privilege Escalation", "Lateral Movement", "Persistence"], "mitre_attack": ["T1053", "T1053.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Randomly Generated Scheduled Task 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_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 = {"kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"]}
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A Windows Service with a suspicious service name was installed on $ComputerName$
action.risk.param._risk = [{"threat_object_field": "Service_File_Name", "threat_object_type": "other"}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 45}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"], "observable": [{"name": "Service_File_Name", "role": ["Other"], "type": "Other"}, {"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Randomly Generated Windows 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 | 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 = {"kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1486"]}
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 = []
action.escu.analytic_story = ["Clop Ransomware", "DarkSide Ransomware", "BlackMatter Ransomware"]
action.risk = 1
action.risk.param._risk_message = A high frequency file creation of $file_name$ in different file path in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 81}, {"risk_object_field": "user", "risk_object_type": "user", "risk_score": 81}]
action.risk.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 90, "kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1486"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Ransomware Notes bulk 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=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 | 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Ransomware", "Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains AV recon command in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 56}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 56}]
action.risk.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": ["Ransomware", "Malicious PowerShell"], "confidence": 80, "context": ["source:endpoint", {"stage": "Reconnaissance"}], "impact": 70, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
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 (Message = "*SELECT*" OR Message = "*WMIC*") AND (Message = "*AntiVirusProduct*" OR Message = "*AntiSpywareProduct*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"]}
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = A suspicious powershell script contains host recon command in $Message$ with EventCode $EventCode$ in host $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 60}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 60}]
action.risk.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": ["Malicious PowerShell"], "confidence": 80, "context": ["source:endpoint", {"stage": "Reconnaissance"}], "impact": 75, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Recon Using WMI Class
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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= "*SELECT*" OR Message= "*Get-WmiObject*") AND (Message= "*Win32_Bios*" OR Message= "*Win32_OperatingSystem*" OR Message= "*Win32_Processor*" OR Message= "*Win32_ComputerSystem*" OR Message= "*Win32_ComputerSystemProduct*" OR Message= "*Win32_ShadowCopy*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.004", "T1070"]}
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 = 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 = network operator may use this batch command to delete recursively a directory or files within directory
action.escu.creation_date = 2021-06-22
action.escu.modification_date = 2021-06-22
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 = []
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 - Recursive Delete of Directory In Batch CMD - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.004", "T1070"]}
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 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1574.011", "T1574"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
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 = 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 = []
action.escu.analytic_story = ["Windows Service Abuse", "Windows Persistence Techniques"]
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.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"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 60, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation", "Defense Evasion"], "impact": 75, "kill_chain_phases": ["Installation"], "mitre_attack": ["T1574.011", "T1574"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 - 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.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 - Registry Keys Used For Persistence - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for modifications to registry keys that can be used to launch an application or service at system startup.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.001", "T1547"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The search looks for modifications to registry keys that can be used to launch an application or service at system startup.
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 = 2021-09-07
action.escu.modification_date = 2021-09-07
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 = []
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"]
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.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"], "cis20": ["CIS 8"], "confidence": 95, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.001", "T1547"], "nist": ["PR.PT", "DE.CM", "DE.AE"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 can be used to launch an application or service at system startup.
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 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\\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="*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 | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012", "T1546"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 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 upon system startup and will use these registry keys to accomplish that task.
action.escu.creation_date = 2020-11-27
action.escu.modification_date = 2020-11-27
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 = []
action.escu.analytic_story = ["Windows Privilege Escalation", "Suspicious Windows Registry Activities", "Cloud Federated Credential Abuse"]
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.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": ["Windows Privilege Escalation", "Suspicious Windows Registry Activities", "Cloud Federated Credential Abuse"], "cis20": ["CIS 8"], "confidence": 95, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012", "T1546"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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  min(_time) as firstTime max(_time) as lastTime 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 Registry.dest  Registry.user Registry.registry_path Registry.registry_value_name | `security_content_ctime(lastTime)`  | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other 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 leverage shim databases for compatibility purposes for legacy applications
action.escu.creation_date = 2020-11-26
action.escu.modification_date = 2020-11-26
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 = []
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
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.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"], "cis20": ["CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_for_creating_shim_databases_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.010"]}
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 = 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 = Other third part application may used this parameter but not so common in base windows environment.
action.escu.creation_date = 2021-10-04
action.escu.modification_date = 2021-10-04
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 = []
action.escu.analytic_story = ["Suspicious Regsvr32 Activity", "Remcos"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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": ["Suspicious Regsvr32 Activity", "Remcos"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.010"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Regsvr32 Silent and Install Param Dll 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` 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.010"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["IcedID", "Suspicious Regsvr32 Activity", "Remcos"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.010"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Regsvr32 with Known Silent Switch 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_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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1113"]}
action.escu.data_models = []
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 = []
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.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 100, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1113"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remcos_rat_file_creation_in_remcos_folder_filter`

[ESCU - Remcos client registry install entry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects registry key license at host where Remcos RAT agent is installed.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"]}
action.escu.data_models = []
action.escu.eli5 = This search detects registry key license at host where Remcos RAT agent is installed.
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 = unknown
action.escu.creation_date = 2021-09-24
action.escu.modification_date = 2021-09-24
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 = []
action.escu.analytic_story = ["Remcos"]
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.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"], "confidence": 100, "context": ["Source:Endpoint"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search detects registry key license at host where Remcos RAT agent is installed.
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 values(Registry.registry_path) as  registry_path FROM datamodel=Endpoint.Registry where (Registry.registry_key_name=*\\Software\\Remcos*) by Registry.dest  Registry.user Registry.registry_key_name Registry.process_id| `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`|`remcos_client_registry_install_entry_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 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
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 = []
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware"]
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 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
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 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
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 = []
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "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 Network Traffic - Rule
action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Active Directory Lateral Movement"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.notable.param.rule_title = Remote Desktop 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 where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source 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 - 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 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
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 = 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. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system 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 = 2020-07-21
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 = []
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 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001", "T1021"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Remote Desktop Process Running On 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=*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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.003"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.003"]}
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 = 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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 $ComputerName by abusing WMI using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.003"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message="*Document.Application.ShellExecute*" OR Message="*Document.ActiveView.ExecuteShellCommand*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `remote_process_instantiation_via_dcom_and_powershell_script_block_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 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
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 = 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 = 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 = []
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.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 3", "CIS 5"], "confidence": 70, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1047"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1047"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1047"]}
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 = 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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 $ComputerName by abusing WMI using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1047"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message="*Invoke-WmiMethod*" AND Message="*-CN*" AND Message="*-Class Win32_Process*" AND Message="*-Name create*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `remote_process_instantiation_via_wmi_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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"]}
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 = 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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 $ComputerName by abusing WinRM using PowerShell.exe
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 45}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message="*Invoke-Command*" AND Message="*-ComputerName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 2021-09-01
action.escu.modification_date = 2021-09-01
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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 (Message = "*[adsisearcher]*" AND Message = "*objectclass=computer*" AND Message = "*findAll()*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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 Dsquery - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = Remote System Discovery with Dsquery
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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 Net - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = Remote System 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="*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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"]}
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 = 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 = 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 = []
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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1018"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
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 = 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. Deprecated because duplicate of Remote Process Instantiation via WMI.
action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. Filter as needed.
action.escu.creation_date = 2018-12-03
action.escu.modification_date = 2018-12-03
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 = []
action.escu.analytic_story = ["Suspicious WMI Use"]
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.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": ["Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "confidence": 60, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
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 = 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 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 = []
action.escu.analytic_story = ["Clop 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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204"]}
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 = 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 = 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 = []
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"]}
action.escu.data_models = []
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 = 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 = unknown
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 Registry Entry - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
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.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"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 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="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") AND (Registry.registry_value_name = "\.*" OR Registry.registry_value_name = "Binary Data") by Registry.registry_value_name Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `revil_registry_entry_filter`

[ESCU - RunDLL Loading DLL By Ordinal - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for executing scripts with rundll32. 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.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for executing scripts with rundll32. 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.
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 = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process
action.escu.creation_date = 2020-11-30
action.escu.modification_date = 2020-11-30
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 = []
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = A rundll32 process $process_name$ with ordinal parameter like this process commandline $process$ 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.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"], "cis20": ["CIS 8"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for executing scripts with rundll32. 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.
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.process_name Processes.parent_process_name Processes.original_file_name Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1134", "T1134.001"]}
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 = 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 = 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 = 2021-11-12
action.escu.modification_date = 2021-11-12
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 = []
action.escu.analytic_story = ["Windows Privilege Escalation"]
action.risk = 1
action.risk.param._risk_message = elevated process using runas on $dest$ by $user$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 25}]
action.risk.param.verbose = 0
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": ["Windows Privilege Escalation"], "confidence": 50, "context": ["Source:Endpoint", "stage:Privilege Escalation"], "impact": 50, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1134", "T1134.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Runas Execution in CommandLine
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
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 = 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 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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "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 $dest$ 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": 15}, {"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Hunt - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Microsoft MSHTML Remote Code Execution CVE-2021-40444"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-40444"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Rundll32 Control RunDLL Hunt
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 OR Processes.original_file_name=RUNDLL32.EXE) 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
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 = 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 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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "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 $dest$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-40444"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 (Processes.process_name=rundll32.exe OR Processes.original_file_name=RUNDLL32.EXE) 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 process to cmd.exe process. This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information the the compromised host. browser process.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
action.escu.eli5 = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to cmd.exe process. This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information the the compromised host. 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 = 2021-07-29
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = rundl32 process $SourceImage$ create a remote thread to process $TargetImage$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to cmd.exe process. This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information the the compromised host. browser process.
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 Computer | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = rundl32 process $SourceImage$ create a remote thread to browser process $TargetImage$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 70}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
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 Computer | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
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 = 2021-07-26
action.escu.modification_date = 2021-07-26
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = rundll32 process $process_name$ having a dns query to $QueryName$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 56}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
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 by Image QueryName QueryStatus ProcessId direction Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_dnsquery_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
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 = []
action.escu.analytic_story = ["IcedID"]
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": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Unusual Processes"]
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.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"], "confidence": 100, "context": ["Stage:Execution", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
action.escu.data_models = []
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 = 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 `port` node. 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 = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
action.escu.creation_date = 2021-10-13
action.escu.modification_date = 2021-10-13
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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Cobalt Strike", "PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = A rundll32 process $process_name$ with no commandline argument like this process commandline $process$ 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": "processname"}]
action.risk.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", "PrintNightmare CVE-2021-34527"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "cve": ["CVE-2021-34527"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "processname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 FROM datamodel=Endpoint.Processes where `process_rundll32` by _time span=1h  Processes.process_guid 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)` | regex process="(rundll32\.exe.{0,4}$)" | join  process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_guid Ports.dest Ports.dest_port| `drop_dm_object_name(Ports)` | rename  dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_guid connection_to_CNC dest_port | `rundll32_with_no_command_line_arguments_with_network_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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = []
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.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 8"], "confidence": 100, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 70, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1059", "T1059.003"]}
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 = 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 = 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 = []
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1059", "T1059.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"]}
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 = 2021-07-23
action.escu.modification_date = 2021-07-23
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 = []
action.escu.analytic_story = ["Credential Dumping"]
action.risk = 1
action.risk.param._risk_message = The following process $process_name$ accessed the object $Object_Name$ attempting to gain access to credentials on $dest$ 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}, {"threat_object_field": "process_name", "threat_object_type": "process"}, {"threat_object_field": "Object_Name", "threat_object_type": "file"}]
action.risk.param.verbose = 0
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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "cve": ["CVE-2021-36934"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002", "T1003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}, {"name": "Object_Name", "role": ["Other"], "type": "File"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = SAM Database File Access 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 = `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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = 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 = 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 = []
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Privilege Escalation"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "Hostname"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = 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 = 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 = []
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.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"], "confidence": 90, "context": ["source:endpoint", {"stage": "Privilege Escalation"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 - SMB Traffic Spike - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.CM"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for spikes in the number of Server Message Block (SMB) traffic connections.
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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['src']
action.notable.param.rule_description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections.
action.notable.param.rule_title = SMB Traffic Spike
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_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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.CM"]}
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_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 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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002", "T1021"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_description = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.
action.notable.param.rule_title = SMB Traffic Spike - MLTK
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.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 - SQL Injection with Long URLs - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for long URLs that have several SQL commands visible within them.
action.escu.mappings = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1190"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This search looks for long URLs that have several SQL commands visible within them.
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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = []
action.escu.analytic_story = ["SQL Injection"]
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 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1190"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_description = This search looks for long URLs that have several SQL commands visible within them.
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 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 - 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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "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 = []
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.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 8"], "confidence": 20, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 60, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1543.003", "T1543"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Windows Persistence Techniques", "Disabling Security Tools", "NOBELIUM Group"]
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.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"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Installation"], "mitre_attack": ["T1543.003", "T1543"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087.002", "T1087"]}
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 = []
action.escu.analytic_story = ["blackMatter ransomware"]
action.risk = 1
action.risk.param._risk_message = process $Image$ create a file $TargetFilename$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 25}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087.002", "T1087"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = SchCache Change By App Connect And Create ADSI 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=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 Computer | `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 query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. 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 is 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]}
action.escu.data_models = []
action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. 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 is 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 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 HTTP Command Arguments - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Windows Persistence Techniques"]
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}, {"threat_object_field": "Arguments", "threat_object_type": "arguments"}]
action.risk.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": ["Windows Persistence Techniques"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Arguments", "role": ["Attacker"], "type": "Arguments"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. 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 is 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 = 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 query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. 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 is 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]}
action.escu.data_models = []
action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. 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 is 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 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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Trickbot", "IcedID"]
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}, {"threat_object_field": "Arguments", "threat_object_type": "arguments"}]
action.risk.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"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Arguments", "role": ["Attacker"], "type": "Arguments"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. 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 is 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 = 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 = This analytic looks for the execution of `at.exe` with command-line arguments utilized to create a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution. The `at.exe` binary internally leverages the AT protocol which was deprecated starting with Windows 8 and Windows Server 2012 but may still work on previous versions of Windows. Furthermore, attackers may enable this protocol on demand by changing a sytem registry key.
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053", "T1053.002"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `at.exe` with command-line arguments utilized to create a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution. The `at.exe` binary internally leverages the AT protocol which was deprecated starting with Windows 8 and Windows Server 2012 but may still work on previous versions of Windows. Furthermore, attackers may enable this protocol on demand by changing a sytem registry key.
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.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053", "T1053.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `at.exe` with command-line arguments utilized to create a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution. The `at.exe` binary internally leverages the AT protocol which was deprecated starting with Windows 8 and Windows Server 2012 but may still work on previous versions of Windows. Furthermore, attackers may enable this protocol on demand by changing a sytem registry key.
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 search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.
action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.
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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
action.escu.known_false_positives = Tasks should not be manually created via CLI, this is rarely done by admins as well
action.escu.creation_date = 2020-12-17
action.escu.modification_date = 2020-12-17
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 = []
action.escu.analytic_story = ["DHS Report TA18-074A", "NOBELIUM Group"]
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.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": ["DHS Report TA18-074A", "NOBELIUM Group"], "cis20": ["CIS 3"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.
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 = This analytic looks for the execution of `schtasks.exe` with command-line arguments utilized to start a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053", "T1053.005"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `schtasks.exe` with command-line arguments utilized to start a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
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.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053", "T1053.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `schtasks.exe` with command-line arguments utilized to start a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
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 - 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 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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 - Deprecated - Scheduled tasks used in BadRabbit ransomware - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.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 - Schtasks Run Task On Demand - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic identifies an on demand run of a Windows Schedule Task through shell or command-line. This technique has been used by adversaries that force to run their created Schedule Task as their persistence mechanism or for lateral movement as part of their malicious attack to the compromised machine.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic identifies an on demand run of a Windows Schedule Task through shell or command-line. This technique has been used by adversaries that force to run their created Schedule Task as their persistence mechanism or for lateral movement as part of their malicious attack to the compromised machine.
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 schtasks.exe may be used.
action.escu.known_false_positives = Administrators may use to debug Schedule Task entries. 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 - 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 = []
action.escu.analytic_story = ["XMRig"]
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.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": ["XMRig"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This analytic identifies an on demand run of a Windows Schedule Task through shell or command-line. This technique has been used by adversaries that force to run their created Schedule Task as their persistence mechanism or for lateral movement as part of their malicious attack to the compromised machine.
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 = This analytic looks for the execution of `schtasks.exe` with command-line arguments utilized to create a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic looks for the execution of `schtasks.exe` with command-line arguments utilized to create a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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. It is important to validate and investigate as appropriate.
action.escu.creation_date = 2021-11-11
action.escu.modification_date = 2021-11-11
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement", "NOBELIUM Group"]
action.risk = 1
action.risk.param._risk_message = A schedule task process $process_name$ with remote job commandline $process$ in host $dest$
action.risk.param._risk = [{"risk_object_field": "Processes.dest", "risk_object_type": "system", "risk_score": 63}, {"risk_object_field": "Processes.user", "risk_object_type": "user", "risk_score": 63}]
action.risk.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"], "cis20": ["CIS 3"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["PR.IP"], "observable": [{"name": "Processes.dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Processes.user", "role": ["Victim"], "type": "user"}]}
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 `schtasks.exe` with command-line arguments utilized to create a Scheduled Task on a remote endpoint. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
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 = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled.
action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled.
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 create jobs on systems forcing reboots to perform updates, maintenance, etc.
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware"]
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.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"], "cis20": ["CIS 3"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005", "T1053"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 a forced reboot of system is scheduled.
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546", "T1546.002"]}
action.escu.data_models = []
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 = 2021-09-27
action.escu.modification_date = 2021-09-27
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 90, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546", "T1546.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 = This search looks for scripts launched via WMI.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for scripts launched via WMI.
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. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Suspicious WMI Use"]
action.risk = 1
action.risk.param._risk_message = A wmic.exe process $process_name$ taht 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.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 3", "CIS 5"], "confidence": 60, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search looks for scripts launched via WMI.
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = 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 = Limited to no false positives are expected.
action.escu.creation_date = 2021-07-01
action.escu.modification_date = 2021-07-01
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 = []
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 $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.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 Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1485", "T1070.004", "T1070"]}
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 = 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 = 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 = []
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1485", "T1070.004", "T1070"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
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 = 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 `ports` node.
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 = 2021-10-13
action.escu.modification_date = 2021-10-13
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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": "processname"}]
action.risk.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": ["Cobalt Strike"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}, "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "processname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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_guid 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="(searchprotocolhost\.exe.{0,4}$)" | join  process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_guid Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename  dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_guid connection_to_CNC dest_port | `searchprotocolhost_with_no_command_line_with_network_filter`

[ESCU - SecretDumps Offline NTDS Dumping Tool - Rule]
action.escu = 0
action.escu.enabled = 1
description = This 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.003", "T1003"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This 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 = 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 = 2021-05-26
action.escu.modification_date = 2021-05-26
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 = []
action.escu.analytic_story = ["Credential Dumping"]
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.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"], "confidence": 100, "context": ["source:endpoint", {"stage": "Credential Access"}], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.003", "T1003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This 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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1558.003"]}
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 = 2021-10-14
action.escu.modification_date = 2021-10-14
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 = []
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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1558.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
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 Message="*KerberosRequestorSecurityToken*" | stats count min(_time) as firstTime max(_time) as lastTime by Message OpCode ComputerName User EventCode | `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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1558.003"]}
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 = 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 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 = []
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 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1558.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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$\400619a.exe`. The process lineage is completed with `400619a.exe` spawning rundll32.exe, which is the default `spawnto_` value for Cobalt Strike. The `spawnto_` value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The `spawnto_` process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications.
action.escu.mappings = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies the use of `svc-exe` with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using `svc-exe`, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within `C:\Windows\400619a.exe`. Following, the binary will be added to the registry under key `HKLM\System\CurrentControlSet\Services\400619a\` with multiple keys and values added to look like a legitimate service. Upon loading, `services.exe` will spawn the randomly named binary from `\\127.0.0.1\ADMIN$\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 = 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 = False positives should be limited as `services.exe` should never spawn a process from `ADMIN$`. Filter as needed.
action.escu.creation_date = 2021-05-18
action.escu.modification_date = 2021-05-18
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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.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": ["Cobalt Strike"], "confidence": 95, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548"], "observable": [{"name": "Processes.dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Processes.user", "role": ["Victim"], "type": "user"}]}
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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints.
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 = 2020-11-06
action.escu.modification_date = 2020-11-06
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 = []
action.escu.analytic_story = ["Malicious PowerShell", "Credential Dumping", "HAFNIUM Group"]
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}, {"threat_object_field": "registry_path", "threat_object_type": "registrypath"}]
action.risk.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": ["Malicious PowerShell", "Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1059", "T1059.001"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "registry_path", "role": ["Others"], "type": "RegistryPath"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"]}
action.escu.data_models = []
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 = []
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}, {"threat_object_field": "file_path", "threat_object_type": "file path"}]
action.risk.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 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_path", "role": ["Others"], "type": "file path"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 8"], "kill_chain_phases": ["Actions on Objectives"], "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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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.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 8"], "confidence": 90, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011", "T1546"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
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 leverages Windows Security EventCode 4698, `A scheduled task was created` and Windows Security EventCode 4699, `A scheduled task was deleted` to identify scheduled tasks created and deleted in less than 30 seconds. This behavior may represent a lateral movement attack abusing the Task Scheduler to obtain code execution. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053.005"]}
action.escu.data_models = []
action.escu.eli5 = The following analytic leverages Windows Security EventCode 4698, `A scheduled task was created` and Windows Security EventCode 4699, `A scheduled task was deleted` to identify scheduled tasks created and deleted in less than 30 seconds. This behavior may represent a lateral movement attack abusing the Task Scheduler to obtain code execution. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = A windows scheduled task was created and deleted in 30 seconds on $ComputerName$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 81}, {"threat_object_field": "Command", "threat_object_type": "command"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Persistence", "Stage:Privilege Escalation", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Command", "role": ["Target"], "type": "Command"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = The following analytic leverages Windows Security EventCode 4698, `A scheduled task was created` and Windows Security EventCode 4699, `A scheduled task was deleted` to identify scheduled tasks created and deleted in less than 30 seconds. This behavior may represent a lateral movement attack abusing the Task Scheduler to obtain code execution. Red Teams and adversaries alike may abuse the Task Scheduler for lateral movement and remote code execution.
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 | table _time, ComputerName, 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 = This search detects accounts that were created and deleted in a short time period.
action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1136.001", "T1136"], "nist": ["PR.IP"]}
action.escu.data_models = ["Change"]
action.escu.eli5 = This search detects accounts that were created and deleted in a short time period.
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 = []
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.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 16"], "confidence": 90, "context": ["source:endpoint", {"stage": "Persistence"}], "impact": 70, "mitre_attack": ["T1136.001", "T1136"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search detects accounts that were created and deleted in a short time period.
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = 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 = 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 - SilentCleanup UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.Registry where Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_name = "*.exe*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `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 = This search looks for process names that consist only of a single letter.
action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["ID.AM", "PR.DS"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for process names that consist only of a single letter.
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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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.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 2"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204", "T1204.002"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search looks for process names that consist only of a single letter.
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 - 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 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]}
action.escu.data_models = ["Vulnerabilities"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"]
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 4"], "cve": ["CVE-2017-5753"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.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 - 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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]}
action.escu.data_models = []
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 = []
action.escu.analytic_story = ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = The search looks for a sharp increase in the number of files written to a particular host
action.notable.param.rule_title = Spike in File Writes
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Splunk Enterprise Vulnerability CVE-2018-11409"]
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 Enterprise Vulnerability CVE-2018-11409"], "cis20": ["CIS 3", "CIS 4", "CIS 18"], "cve": ["CVE-2018-11409"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $parent_process$ has spawned $process_name$ on endpoint $ComputerName$. 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"}, {"threat_object_field": "process_id", "threat_object_type": "process"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Local"], "cve": ["CVE-2021-34527"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_id", "role": ["Parent Process", "Attacker"], "type": "Process"}, {"name": "process_id", "role": ["Child Process"], "type": "Process"}]}
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
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 = []
action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"]
action.risk = 1
action.risk.param._risk_message = $Image$ with process id $process_id$ has loaded a driver from $ImageLoaded$ on endpoint $Computer$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "process_id", "threat_object_type": "process name"}, {"threat_object_field": "ImageLoaded", "threat_object_type": "file"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Local"], "cve": ["CVE-2021-34527"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_id", "role": ["Parent Process", "Attacker"], "type": "Process Name"}, {"name": "ImageLoaded", "role": ["Other"], "type": "File"}]}
schedule_window = auto
action.notable = 1
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 process_id EventCode | 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"]}
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 = []
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 $Computer$. This behavior is suspicious and related to PrintNightmare.
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 72}, {"threat_object_field": "ProcessID", "threat_object_type": "process"}, {"threat_object_field": "TargetImage", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Local"], "cve": ["CVE-2021-34527"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "ProcessID", "role": ["Parent Process"], "type": "Process"}, {"name": "TargetImage", "role": ["Target"], "type": "Process Name"}]}
schedule_window = auto
action.notable = 1
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 Computer 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
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 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 = []
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}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_path", "threat_object_type": "file"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion"], "cve": ["CVE-2021-34527"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_id", "role": ["Process"], "type": "Process"}, {"name": "file_path", "role": ["Other"], "type": "File"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"]}
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 = []
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}, {"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_path", "threat_object_type": "file"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Local"], "cve": ["CVE-2021-34527"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_id", "role": ["Process"], "type": "Process"}, {"name": "file_path", "role": ["Other"], "type": "File"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1005"]}
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 = []
action.escu.analytic_story = ["IcedID"]
action.risk = 1
action.risk.param._risk_message = process $SourceImage$ create a file $TargetImage$ in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 9}, {"threat_object_field": "SourceImage", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1005"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
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 process_name TargetFilename EventCode ProcessId Image | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sqlite_module_in_temp_folder_filter`

[ESCU - Start Up During Safe Mode Boot - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search is to detect a modification or registry add to the safeboot registry as an autostart mechanism. This technique was seen in some ransomware to automatically execute its code upon a safe mode boot.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.001", "T1547"]}
action.escu.data_models = []
action.escu.eli5 = This search is to detect a modification or registry add to the safeboot registry as an autostart mechanism. This technique was seen in some ransomware to automatically execute its code upon a safe mode boot.
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 = 2021-06-10
action.escu.modification_date = 2021-06-10
action.escu.confidence = high
action.escu.full_search_name = ESCU - Start Up During Safe Mode Boot - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Ransomware"]
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.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Start Up During Safe Mode Boot - Rule
action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.001", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search is to detect a modification or registry add to the safeboot registry as an autostart mechanism. This technique was seen in some ransomware to automatically execute its code upon a safe mode boot.
action.notable.param.rule_title = Start Up During Safe 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.Registry where Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SafeBoot\\Minimal\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `start_up_during_safe_mode_boot_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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "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 = []
action.escu.analytic_story = ["NOBELIUM Group"]
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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 - Supernova Webshell - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search aims to detect the Supernova webshell used in the SUNBURST attack.
action.escu.mappings = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1505.003"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]}
action.escu.data_models = ["Web"]
action.escu.eli5 = This search aims to detect the Supernova webshell used in the SUNBURST 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 = []
action.escu.analytic_story = ["NOBELIUM Group"]
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 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1505.003"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest', 'src']
action.notable.param.rule_description = This search aims to detect the Supernova webshell used in the SUNBURST 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 - 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 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.001"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes.
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 = []
action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"]
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 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.001"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036.003", "T1036"]}
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 = 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 = every user may do this event but very un-ussual.
action.escu.creation_date = 2021-10-05
action.escu.modification_date = 2021-10-05
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 = []
action.escu.analytic_story = ["Unusual Processes"]
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.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"], "confidence": 90, "context": ["Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036.003", "T1036"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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 | `drop_dm_object_name(Processes)` | `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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1105"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Silver Sparrow", "Ingress Tool Transfer"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1105"]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
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 = 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 = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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.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": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543.003", "T1543"]}
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 = []
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = Suspicious driver $ImageLoaded$ on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 63}, {"threat_object_field": "ImageLoaded", "threat_object_type": "file name"}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543.003", "T1543"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "ImageLoaded", "role": ["Attacker"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
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 Computer ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_driver_loaded_path_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 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566"], "nist": ["PR.IP"]}
action.escu.data_models = ["UEBA"]
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Suspicious Emails"]
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 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Suspicious Email - UBA Anomaly
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 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 Email Attachment Extensions - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for emails that have attachments with suspicious file extensions.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE", "PR.IP"]}
action.escu.data_models = ["Email"]
action.escu.eli5 = This search looks for emails that have attachments with suspicious file extensions.
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 = 2020-07-22
action.escu.modification_date = 2020-07-22
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 = []
action.escu.analytic_story = ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Emails"]
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": ["Emotet Malware  DHS Report TA18-201A ", "Suspicious Emails"], "cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566.001", "T1566"], "nist": ["DE.AE", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search looks for emails that have attachments with suspicious file extensions.
action.notable.param.rule_title = Suspicious Email Attachment Extensions
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=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 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 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]}
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 = []
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 $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 9}]
action.risk.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 3", "CIS 5", "CIS 6"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.notable.param.rule_title = Suspicious 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 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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
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 = 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 = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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.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": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
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 = 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 = 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 = []
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": 56}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1113"]}
action.escu.data_models = []
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 = 2021-09-21
action.escu.modification_date = 2021-09-21
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 = []
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": "Computer", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1113"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 | `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 ("*.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 | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | `suspicious_image_creation_in_appdata_folder_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 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "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 = []
action.escu.analytic_story = ["Apache Struts Vulnerability"]
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 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest', 'src']
action.notable.param.rule_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.notable.param.rule_title = Suspicious Java Classes
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_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 - 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["PR.PT", "DE.CM"]}
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 = 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 = Although unlikely, some legitimate applications may use a moved copy of msbuild, 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 Rename - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild", "Cobalt Strike", "Masquerading - Rename System Utilities"]
action.risk = 1
action.risk.param._risk_message = Suspicious renamed msbuild.exe binary ran on $dest$ 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}]
action.risk.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 Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild", "Cobalt Strike", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Suspicious MSBuild Rename
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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_msbuild` 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127", "T1127.001"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild"]
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.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"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127", "T1127.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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 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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001", "T1543"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Silver Sparrow"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001", "T1543"]}
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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001", "T1543"]}
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 = []
action.escu.analytic_story = ["Silver Sparrow"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001", "T1543"]}
schedule_window = auto
action.notable = 1
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 = This analytic is to detect a suspicious process having a dns query on known abuse text paste web services, or VoIP, instant messaging and digital distribution platform to download some files. This technique is abused by adversaries, malware attacker and red team to download a malicious file on the target host. This is a good TTP indicator for possible initial access technique. Noise and false positive can be seen if the following instant messaging is allowed or common application in corporate network.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.005", "T1059"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to detect a suspicious process having a dns query on known abuse text paste web services, or VoIP, instant messaging and digital distribution platform to download some files. This technique is abused by adversaries, malware attacker and red team to download a malicious file on the target host. This is a good TTP indicator for possible initial access technique. Noise and false positive can be seen if the following instant messaging is allowed or common application in corporate network.
action.escu.how_to_implement = his 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 = Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. filter is needed.
action.escu.creation_date = 2021-11-18
action.escu.modification_date = 2021-11-18
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 = []
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = suspicious process $process_name$ has a dns query in $QueryName$ on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 64}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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": ["Remcos"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.005", "T1059"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This analytic is to detect a suspicious process having a dns query on known abuse text paste web services, or VoIP, instant messaging and digital distribution platform to download some files. This technique is abused by adversaries, malware attacker and red team to download a malicious file on the target host. This is a good TTP indicator for possible initial access technique. Noise and false positive can be seen if the following instant messaging is allowed or common application in 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 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_dns_query_known_abuse_web_services_filter`

[ESCU - Suspicious Process File Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following analytic will detect a suspicious process running in a file path where a process is not commonly seen and is most commonly used by malicious softtware. This behavior has been used by adversaries where they drop and run an exe in a path that is accessible without admin privileges.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic will detect a suspicious process running in a file path where a process is not commonly seen and is most commonly used by malicious softtware. This behavior has been used by adversaries where they drop and run an exe in a path that is accessible without admin privileges.
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 = Administrators may allow execution of specific binaries in non-standard paths. Filter as needed.
action.escu.creation_date = 2021-05-05
action.escu.modification_date = 2021-05-05
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 = []
action.escu.analytic_story = ["XMRig", "Remcos"]
action.risk = 1
action.risk.param._risk_message = Suspicioues process $Processes.process_path.file_path$ running from suspicious location
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"threat_object_field": "Processes.process_path.file_path", "threat_object_type": "file name"}]
action.risk.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": ["XMRig", "Remcos"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The following analytic will detect a suspicious process running in a file path where a process is not commonly seen and is most commonly used by malicious softtware. This behavior has been used by adversaries where they drop and run an exe in a path that is accessible without admin privileges.
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.file_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path.file_path = "*\\Windows\\servicing\\*" OR Processes.process_path.file_path = "*\\Users\\Default\\*" OR Processes.process_path.file_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" 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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
action.escu.data_models = []
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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Disabling Security Tools", "DHS Report TA18-074A"]
action.risk = 1
action.risk.param._risk_message = Suspicious $Processes.process_path.file_path$ process running with an uncommon parent process $Processes.parent_process_name$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"threat_object_field": "Processes.process_path.file_path", "threat_object_type": "file name"}]
action.risk.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 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Suspicious Reg exe 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 != 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 malciious 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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "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 malciious 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 = 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. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by  legitimate processes. 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 = Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues.
action.escu.creation_date = 2021-01-28
action.escu.modification_date = 2021-01-28
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 = []
action.escu.analytic_story = ["Suspicious Regsvr32 Activity", "Iceid"]
action.risk = 1
action.risk.param._risk_message = Suspicious $Processes.process_path.file_path$ process potentially loading malicious code
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"threat_object_field": "Processes.process_path.file_path", "threat_object_type": "file name"}]
action.risk.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", "Iceid"], "cis20": ["CIS 8", "CIS 16"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.010"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]}
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 malciious 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=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) 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_regsvr32_register_suspicious_path_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 IceID malware to execute its initial dll stager to download another payload to the compromised machine.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"]}
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 IceID malware to execute its initial dll stager to download another payload to the compromised 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 `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 = 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 = []
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 name"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.011"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
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 IceID 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 Rename - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1036", "T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following 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 = 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 = Although unlikely, some legitimate applications may use a moved copy of rundll32, 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 - Suspicious Rundll32 Rename - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Masquerading - Rename System Utilities"]
action.risk = 1
action.risk.param._risk_message = Suspicious renamed rundll32.exe binary ran on $dest$ 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}]
action.risk.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 Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1036", "T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The following 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.notable.param.rule_title = Suspicious Rundll32 Rename
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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` 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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "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 = 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 = 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 = 2021-02-04
action.escu.modification_date = 2021-02-04
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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Cobalt Strike", "Trickbot"]
action.risk = 1
action.risk.param._risk_message = rundll32.exe running with suspicious 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.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": ["Suspicious Rundll32 Activity", "Cobalt Strike", "Trickbot"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "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 = 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 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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity"]
action.risk = 1
action.risk.param._risk_message = $Processes.process_path.file_path$ process potentially loading malicious code
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 35}, {"threat_object_field": "Processes.process_path.file_path", "threat_object_type": "file name"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]}
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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = 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 = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
action.escu.creation_date = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Suspicious Rundll32 Activity", "Cobalt Strike", "PrintNightmare CVE-2021-34527"]
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.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", "PrintNightmare CVE-2021-34527"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "cve": ["CVE-2021-34527"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218", "T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(rundll32\.exe.{0,4}$)" | `suspicious_rundll32_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 = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1074"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Silver Sparrow"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1074"]}
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 Scheduled Task from Public Directory - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint.
action.escu.mappings = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1053.005", "T1053"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint.
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 = Limited false positives may be present. Filter as needed by parent process or command line argument.
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 = []
action.escu.analytic_story = ["Ransomware", "Ryuk Ransomware", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = Suspicious scheduled task registered 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.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1053.005", "T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint.
action.notable.param.rule_title = Suspicious Scheduled Task from Public 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=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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
action.escu.data_models = []
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 = 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 = Limited false positives may be present in small environments. Tuning may be required based on parent process.
action.escu.creation_date = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Cobalt Strike"]
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.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": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(searchprotocolhost\.exe.{0,4}$)"  | `suspicious_searchprotocolhost_no_command_line_arguments_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1113"]}
action.escu.data_models = []
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 = 2021-09-21
action.escu.modification_date = 2021-09-21
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 = []
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": "Computer", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "process_name", "threat_object_type": "process name"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1113"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 | `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 ("*.wav") Filesystem.file_path = "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields file_name file_path process_name process_path process dest file_create_time _time ] | `suspicious_wav_file_in_appdata_folder_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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036", "T1127", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
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 = 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 = Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.
action.escu.creation_date = 2021-09-20
action.escu.modification_date = 2021-09-20
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 = []
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution", "Cobalt Strike", "Masquerading - Rename System Utilities"]
action.risk = 1
action.risk.param._risk_message = Suspicious renamed microsoft.workflow.compiler.exe binary ran on $dest$ 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}]
action.risk.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 rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution", "Cobalt Strike", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036", "T1127", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Suspicious microsoft workflow compiler rename
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_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.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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution"]
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.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"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["PR.PT", "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 = 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 = 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 = 2021-01-12
action.escu.modification_date = 2021-01-12
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 = []
action.escu.analytic_story = ["Trusted Developer Utilities Proxy Execution MSBuild", "Cobalt Strike", "Masquerading - Rename System Utilities"]
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.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", "Cobalt Strike", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036", "T1127", "T1036.003", "T1127.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
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!=c:\\windows\\microsoft.net\\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 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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "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 = 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, 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 = []
action.escu.analytic_story = ["Suspicious MSHTA Activity"]
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}, {"threat_object_field": "parent_process", "threat_object_type": "process name"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process", "role": ["Parent Process"], "type": "Process Name"}]}
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 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious MSHTA Activity"]
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.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"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001", "T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]}
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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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.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 3", "CIS 5", "CIS 6"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001", "T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 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 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Suspicious writes to System Volume Information
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 - Suspicious writes to windows Recycle Bin - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search detects writes to the recycle bin by a process other than explorer.exe.
action.escu.mappings = {"cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search detects writes to the recycle bin by a process other than explorer.exe.
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 = []
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.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 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "mitre_attack": ["T1036"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_name", "role": ["Attacker"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This search detects writes to the recycle bin by a process other than explorer.exe.
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 identifies `svchost.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Task Scheduler and creating a malicious remote scheduled task, the executed command is spawned as a child process of `svchost.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 svchost.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053", "T1053.005"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The following analytic identifies `svchost.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Task Scheduler and creating a malicious remote scheduled task, the executed command is spawned as a child process of `svchost.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 svchost.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
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.
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1053", "T1053.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = The following analytic identifies `svchost.exe` spawning a LOLBAS execution process. When adversaries execute code on remote endpoints abusing the Task Scheduler and creating a malicious remote scheduled task, the executed command is spawned as a child process of `svchost.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 svchost.exe that are part of the LOLBAS project can help defenders identify lateral movement activity.
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 commandline can collect system info of the target host. This technique was seen in remcos, adversaries and other malware to collect information as part of recon or collection phase of attack. Even this behavior is rarely seen in a corporate network this commandline can be used by network administrator to audit host machine specification. Better to check what it did after it pipes out the result to a file for further processing.
action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious dxdiag.exe process commandline can collect system info of the target host. This technique was seen in remcos, adversaries and other malware to collect information as part of recon or collection phase of attack. Even this behavior is rarely seen in a corporate network this commandline can be used by network administrator to audit host machine specification. Better to check what it did after it pipes out the result to a file for further processing.
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 = this commandline can be used by network administrator to audit host machine specification.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 = []
action.escu.analytic_story = ["Remcos"]
action.risk = 1
action.risk.param._risk_message = dxdiag.exe process with commandline $process$ 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.verbose = 0
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"], "confidence": 50, "context": ["source:endpoint", "stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
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 dxdiag.exe process commandline can collect system info of the target host. This technique was seen in remcos, adversaries and other malware to collect information as part of recon or collection phase of attack. Even this behavior is rarely seen in a corporate network this commandline can be used by network administrator to audit host machine specification. Better to check what it did after it pipes out the result to a file for further processing.
action.notable.param.rule_title = System Info Gathering Using Dxdiag 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 `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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "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 = 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 = 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 = []
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.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 6", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Recon", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Attacker"], "type": "User"}]}
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 processes 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = 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 = 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 = []
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware", "Masquerading - Rename System Utilities"]
action.risk = 1
action.risk.param._risk_message = System process running from unexpected location on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 49}, {"threat_object_field": "Processes.process_name", "threat_object_type": "process"}]
action.risk.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"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_name", "role": ["Attacker"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = System Processes Run From Unexpected Locations
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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_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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System user discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.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 User Discovery With Query - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = System User Discovery With Query
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"]}
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 = 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 = 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 Whoami - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System user discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.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 User Discovery With Whoami - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Discovery"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 `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.notable.param.rule_title = System User Discovery With 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_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 - TOR Traffic - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes.
action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071", "T1071.001"], "nist": ["DE.AE"]}
action.escu.data_models = ["Network_Traffic"]
action.escu.eli5 = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes.
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 at this time
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
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 = []
action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control", "NOBELIUM Group"]
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", "Command and Control", "NOBELIUM Group"], "cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071", "T1071.001"], "nist": ["DE.AE"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes.
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 - Time Provider Persistence Registry - Rule]
action.escu = 0
action.escu.enabled = 1
description = This analytic is to detect a suspiciouos 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.003", "T1547"]}
action.escu.data_models = []
action.escu.eli5 = This analytic is to detect a suspiciouos 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 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 = 2021-09-29
action.escu.modification_date = 2021-09-29
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Privilege Escalation"]
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.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": ["Windows Persistence Techniques", "Windows Privilege Escalation"], "confidence": 100, "context": ["source:endpoint", "stage:Privilege Escalation Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.003", "T1547"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This analytic is to detect a suspiciouos 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  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*" 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)` | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
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 = []
action.escu.analytic_story = ["Trickbot"]
action.risk = 1
action.risk.param._risk_message = Possible Trickbot namedpipe created on $Computer$ by $Image$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 42}, {"threat_object_field": "Image", "threat_object_type": "process"}]
action.risk.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "Image", "role": ["Attacker"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
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 Computer 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = []
action.escu.analytic_story = ["Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = Suspicious unsigned $ImageLoaded$ loaded by $Image$ on endpoint $Computer$ with EventCode $EventCode$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
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 Computer 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.003"]}
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 = []
action.escu.analytic_story = ["Ransomware"]
action.risk = 1
action.risk.param._risk_message = The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $Computer$ by user $user$.
action.risk.param._risk = [{"risk_object_field": "user", "risk_object_type": "user", "risk_score": 48}, {"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 48}, {"threat_object_field": "ImageLoaded", "threat_object_type": "other"}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "ImageLoaded", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
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 Computer EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_with_colorui_com_object_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 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]}
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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
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 = []
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.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 6", "CIS 8", "CIS 10"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - 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 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = 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.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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment.
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 = []
action.escu.analytic_story = ["Windows Privilege Escalation", "Unusual Processes"]
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"], "cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Uncommon Processes 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 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` 

[ESCU - Unified Messaging Service Spawning a Process - Rule]
action.escu = 0
action.escu.enabled = 1
description = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor.
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. Tune out child processes as needed to limit volume of false positives.
action.escu.creation_date = 2021-03-02
action.escu.modification_date = 2021-03-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Unified Messaging Service Spawning a Process - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["HAFNIUM Group"]
action.risk = 1
action.risk.param._risk_message = Possible CVE-2021-26857 exploitation on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 56}]
action.risk.param.verbose = 0
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Unified Messaging Service Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "cve": ["CVE-2021-26857"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor.
action.notable.param.rule_title = Unified Messaging Service 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="umworkerprocess.exe" OR Processes.parent_process_name="UMService.exe" (Processes.process_name!="wermgr.exe" OR Processes.process_name!="werfault.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)` | `unified_messaging_service_spawning_a_process_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.007", "T1218"]}
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 = 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 = 2021-08-09
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 = []
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 name"}]
action.risk.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.007", "T1218"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]}
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 - 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 8"], "kill_chain_phases": ["Actions on Objectives"], "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 = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives.
action.escu.known_false_positives = 
action.escu.creation_date = 2020-07-22
action.escu.modification_date = 2020-07-22
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 = []
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.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 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562"]}
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 = 2021-06-09
action.escu.modification_date = 2021-06-09
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = Possible AMSI Unloading via Reflection using PowerShell on $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 49}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 Message=*system.management.automation.amsi* | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unloading_amsi_via_reflection_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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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 - Deprecated - Unsigned Image Loaded by LSASS - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.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 Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `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": ["PR.IP"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Unsuccessful Netbackup 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 = `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 - 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 = {"kill_chain_phases": ["Reconnaissance", "Exploitation", "Lateral Movement"], "mitre_attack": ["T1078"]}
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = None
action.risk.param._risk = [{"risk_object_field": "Client_Address", "risk_object_type": "system", "risk_score": 42}]
action.risk.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 Computer Service Tickets Requested - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Reconnaissance", "Exploitation", "Lateral Movement"], "mitre_attack": ["T1078"], "observable": [{"name": "Client_Address", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Unusual Number of Computer Service Tickets Requested
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 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 = {"kill_chain_phases": ["Reconnaissance", "Lateral Movement"], "mitre_attack": ["T1078"]}
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 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 = 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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
action.risk = 1
action.risk.param._risk_message = None
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 42}]
action.risk.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 Remote Endpoint Authentication Events - Rule
action.correlationsearch.annotations = {"analytic_story": ["Active Directory Lateral Movement"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Reconnaissance", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Reconnaissance", "Lateral Movement"], "mitre_attack": ["T1078"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Unusual Number of Remote Endpoint Authentication Events
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 = Command lines that are extremely long may be indicative of malicious activity on your hosts.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Command lines that are extremely long may be indicative of malicious activity on your hosts.
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. The command-line arguments are mapped to the process field in the Endpoint data model.
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 = []
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 $Processes.process_name$ on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 42}, {"threat_object_field": "Processes.process_name", "threat_object_type": "process"}]
action.risk.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 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_name", "role": ["Attacker"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_description = Command lines that are extremely long may be indicative of malicious activity on your hosts.
action.notable.param.rule_title = Unusually Long 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 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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an 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.
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 = []
action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"]
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 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Unusually Long Command Line - MLTK
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
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 - 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 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
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 = []
action.escu.analytic_story = ["Apache Struts Vulnerability"]
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 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search looks for unusually long strings in the Content-Type http header that the client sends the server.
action.notable.param.rule_title = Unusually Long Content-Type Length
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` | 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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System user discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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.notable.param.rule_title = User Discovery With Env Vars 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="*$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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"]}
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 = 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 Script Block - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = System user discovery on $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 15}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = User Discovery With Env Vars 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 (Message = "*$env:UserName*" OR Message = "*[System.Environment]::UserName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `user_discovery_with_env_vars_powershell_script_block_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.005", "T1059"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["FIN7", "Remcos"]
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.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.005", "T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.012", "T1218"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Unusual Processes"]
action.risk = 1
action.risk.param._risk_message = process $process_name$ to execute possible clsid commandline $process$ in $dest$
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.verbose = 0
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"], "confidence": 50, "context": ["source:endpoint", "stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.012", "T1218"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Verclsid CLSID 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_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505", "T1505.003"]}
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 = 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 = 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 = 2021-03-03
action.escu.modification_date = 2021-03-03
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 = []
action.escu.analytic_story = ["HAFNIUM Group", "ProxyShell"]
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.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": ["HAFNIUM Group", "ProxyShell"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "cve": ["CVE-2021-34473", "CVE-2021-34523", "CVE-2021-31207"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505", "T1505.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
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 = 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. Tune based on parent process names.
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 = []
action.escu.analytic_story = ["Ryuk Ransomware", "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.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"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - WMI Permanent Event Subscription - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for the creation of WMI permanent event subscriptions.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for the creation of WMI permanent event subscriptions.
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 = []
action.escu.analytic_story = ["Suspicious WMI Use"]
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 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search looks for the creation of WMI permanent event subscriptions.
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 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.003", "T1546"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
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 = []
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.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 3", "CIS 5"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Persistence"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.003", "T1546"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "host", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"]}
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 = 2021-06-14
action.escu.modification_date = 2021-06-14
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 = []
action.escu.analytic_story = ["Malicious PowerShell"]
action.risk = 1
action.risk.param._risk_message = Suspicious powerShell script execution by $user$ on $ComputerName$ via EventCode 4104, where WMI is performing an event query looking for running processes or running services
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 30}, {"risk_object_field": "User", "risk_object_type": "user", "risk_score": 30}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1592"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = WMI Recon Running Process Or 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 = `powershell` EventCode=4104 Message= "*SELECT*" AND (Message="*Win32_Process*" OR Message="*Win32_Service*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `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 = This search looks for the creation of WMI temporary event subscriptions.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for the creation of WMI temporary event subscriptions.
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 = []
action.escu.analytic_story = ["Suspicious WMI Use"]
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 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = This search looks for the creation of WMI temporary event subscriptions.
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1220"]}
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 = 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 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1220"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"]}
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 = 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 = 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 - WSReset UAC Bypass - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 $registry_value_name$ in $dest$
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 63}]
action.risk.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"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002", "T1548"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.Registry where Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "DelegateExecute") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter`

[ESCU - Wbemprox COM Object Execution - Rule]
action.escu = 0
action.escu.enabled = 1
description = this search is designed to detect potential malicious process loading COM object to wbemprox.dll,
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.003"]}
action.escu.data_models = []
action.escu.eli5 = this search is designed to detect potential malicious process loading COM object to wbemprox.dll,
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 = []
action.escu.analytic_story = ["Ransomware", "Revil Ransomware"]
action.risk = 1
action.risk.param._risk_message = Suspicious COM Object Execution on $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 35}]
action.risk.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218", "T1218.003"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = this search is designed to detect potential malicious process loading COM object to wbemprox.dll,
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 Computer EventCode Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Web Fraud Detection"]
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 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.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 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Web Fraud Detection"]
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 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = Web Fraud - Anomalous User Clickspeed
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* | 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 16"], "nist": ["DE.DP"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Web Fraud Detection"]
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 16"], "nist": ["DE.DP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user']
action.notable.param.rule_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.notable.param.rule_title = Web Fraud - Password Sharing Across Accounts
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=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 - Web Servers Executing Suspicious Processes - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for suspicious processes on all systems labeled as web servers.
action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for suspicious processes on all systems labeled as web servers.
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 also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security.
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 = []
action.escu.analytic_story = ["Apache Struts Vulnerability"]
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 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = This search looks for suspicious processes on all systems labeled as web servers.
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1590", "T1590.005"]}
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 = 2021-04-19
action.escu.modification_date = 2021-04-19
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 = []
action.escu.analytic_story = ["Trickbot"]
action.risk = 1
action.risk.param._risk_message = Wermgr.exe process connecting IP location web services on $ComputerName$
action.risk.param._risk = [{"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 56}]
action.risk.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1590", "T1590.005"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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  process_path process_name process_id QueryName QueryStatus QueryResults Computer EventCode | `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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027"]}
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 = []
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Trickbot"]
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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
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 = 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 for Linux, you will need to ensure mapping is occurring correctly. If the EDR is not parsing the pipe bash in the command-line, modifying the analytic will be required. Add parent process name (Processes.parent_process_name) as needed to filter. The following Splunk SOAR playbook can be used to respond to this detection: Log4j Investigate and Respond
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 = []
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - WinEvent Scheduled Task Created Within Public Path - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed from a user writeable file path.\
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 is 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 was it 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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1053.005", "T1053"]}
action.escu.data_models = []
action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed from a user writeable file path.\
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 is 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 was it 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 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 = 2021-04-08
action.escu.modification_date = 2021-04-08
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware", "Ryuk Ransomware", "IcedID", "Active Directory Lateral Movement"]
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}, {"threat_object_field": "Command", "threat_object_type": "command"}]
action.risk.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": ["Windows Persistence Techniques", "Ransomware", "Ryuk Ransomware", "IcedID", "Active Directory Lateral Movement"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Persistence", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1053.005", "T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Command", "role": ["Target"], "type": "Command"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed from a user writeable file path.\
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 is 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 was it 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 = 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\\*") | 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 Scheduled Task Created to Spawn Shell - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a native Windows shell (PowerShell, Cmd, Wscript, Cscript).\
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 is 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 = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1053.005", "T1053"]}
action.escu.data_models = []
action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a native Windows shell (PowerShell, Cmd, Wscript, Cscript).\
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 is 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 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 = 2021-04-12
action.escu.modification_date = 2021-04-12
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 = []
action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware", "Ryuk Ransomware"]
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}, {"threat_object_field": "Command", "threat_object_type": "command"}]
action.risk.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": ["Windows Persistence Techniques", "Ransomware", "Ryuk Ransomware"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Persistence", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1053.005", "T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Command", "role": ["Target"], "type": "Command"}]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a native Windows shell (PowerShell, Cmd, Wscript, Cscript).\
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 is 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 = 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 Windows Task Scheduler Event Action Started - Rule]
action.escu = 0
action.escu.enabled = 1
description = The following hunting analytic assists with identifying suspicious tasks that have been registered and ran in Windows using EventID 200 (action run) and 201 (action completed). It is recommended to filter based on ActionName by specifying specific paths not used in your environment. After some basic tuning, this may be effective in capturing evasive ways to register tasks on Windows. Review parallel events related to tasks being scheduled. EventID 106 will generate when a new task is generated, however, that does not mean it ran. Capture any files on disk and analyze.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053.005"]}
action.escu.data_models = []
action.escu.eli5 = The following hunting analytic assists with identifying suspicious tasks that have been registered and ran in Windows using EventID 200 (action run) and 201 (action completed). It is recommended to filter based on ActionName by specifying specific paths not used in your environment. After some basic tuning, this may be effective in capturing evasive ways to register tasks on Windows. Review parallel events related to tasks being scheduled. EventID 106 will generate when a new task is generated, however, that does not mean it ran. Capture any files on disk and analyze.
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 = 2021-10-19
action.escu.modification_date = 2021-10-19
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 = []
action.escu.analytic_story = ["IcedID", "Windows Persistence Techniques"]
action.risk = 1
action.risk.param._risk_message = A Scheduled Task was scheduled and ran on $dest$.
action.risk.param._risk = [{"risk_object_field": "dest", "risk_object_type": "system", "risk_score": 80}]
action.risk.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 Windows Task Scheduler Event Action Started - Rule
action.correlationsearch.annotations = {"analytic_story": ["IcedID", "Windows Persistence Techniques"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_description = The following hunting analytic assists with identifying suspicious tasks that have been registered and ran in Windows using EventID 200 (action run) and 201 (action completed). It is recommended to filter based on ActionName by specifying specific paths not used in your environment. After some basic tuning, this may be effective in capturing evasive ways to register tasks on Windows. Review parallel events related to tasks being scheduled. EventID 106 will generate when a new task is generated, however, that does not mean it ran. Capture any files on disk and analyze.
action.notable.param.rule_title = WinEvent Windows Task Scheduler Event Action Started
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 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 - 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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation", "Denial of Service"], "mitre_attack": ["T1190"]}
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 = 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 = 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 = []
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 - WinRM Spawning a Process - Rule
action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cve": ["CVE-2021-31166"], "kill_chain_phases": ["Exploitation", "Privilege Escalation", "Denial of Service"], "mitre_attack": ["T1190"]}
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 - 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. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1018"], "nist": ["PR.PT", "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. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, 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 = administrators rarely use adfind, usually not used for legitimate reasons
action.escu.creation_date = 2021-11-03
action.escu.modification_date = 2021-11-03
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 = []
action.escu.analytic_story = ["NOBELIUM Group", "Domain Trust Discovery"]
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": ["NOBELIUM Group", "Domain Trust Discovery"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1018"], "nist": ["PR.PT", "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. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller.
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
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 = 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 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 = []
action.escu.analytic_story = ["IceID", "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 $user$ 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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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": ["IceID", "Ingress Tool Transfer"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1105"]}
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 = 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 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
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 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 = 2021-11-25
action.escu.modification_date = 2021-11-25
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 = []
action.escu.analytic_story = ["Remcos", "Windows Defense Evasion Tactics"]
action.risk = 1
action.risk.param._risk_message = exclusion registry $registry_path$  modified or added 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.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]}
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 min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_defender_exclusion_registry_entry_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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["PR.PT", "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 = 2021-03-02
action.escu.modification_date = 2021-03-02
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 = []
action.escu.analytic_story = ["Ryuk Ransomware", "Windows Defense Evasion Tactics"]
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.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": ["Ryuk Ransomware", "Windows Defense Evasion Tactics"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1562.001", "T1562"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1486"]}
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 = 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 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 = []
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 encrypt disks.
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1486"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Windows DiskCryptor 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="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 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 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]}
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 = []
action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"]
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.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"], "cis20": ["CIS 3", "CIS 5", "CIS 6"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070", "T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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 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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"]}
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 = 2021-11-12
action.escu.modification_date = 2021-11-12
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 = []
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil"]
action.risk = 1
action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ loading samlib.dll and vaultcli.dll to potentially capture credentials in memory.
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": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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  process_name=installutil.exe ImageLoaded IN ("*\\samlib.dll", "*\\vaultcli.dll") | stats count min(_time) as firstTime max(_time) as lastTime by Computer, process_name, ImageLoaded, OriginalFileName, process_id | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_credential_theft_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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"]}
action.escu.data_models = []
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 = 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 `Ports` 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 = 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 Remote Network Connection - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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_guid 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_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_guid Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename  dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_guid connection_to_CNC dest_port | `windows_installutil_remote_network_connection_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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"]}
action.escu.data_models = []
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 = 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 `Ports` 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 = 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 Uninstall Option with Network - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Signed Binary Proxy Execution InstallUtil"]
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1218.004", "T1218"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['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.process_guid 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_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_guid Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename  dest as connection_to_CNC] | table _time dest parent_process_name process_name original_file_name process_path process process_guid connection_to_CNC dest_port | `windows_installutil_uninstall_option_with_network_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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. Restrict the analytic to publicly facing endpoints to reduce false positives. Add any additional identified web application process name to the query. Add any further Windows process names to the macro (ex. LOLBins) to further expand this query.
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 = 2021-12-13
action.escu.modification_date = 2021-12-13
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.param.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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "cve": ["CVE-2021-44228"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1489"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 = You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes 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 = 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 = []
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: $processs$
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}, {"threat_object_field": "process", "threat_object_type": "process"}]
action.risk.param.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 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1489"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process", "role": ["Target"], "type": "Process"}]}
schedule_window = auto
action.notable = 1
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 Service Created With Suspicious Service 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 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 = {"kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1569", "T1569.002"]}
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 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 = 2021-11-22
action.escu.modification_date = 2021-11-22
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 = []
action.escu.analytic_story = ["Clop Ransomware", "Active Directory Lateral Movement"]
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 = [{"threat_object_field": "Service_File_Name", "threat_object_type": "other"}, {"threat_object_field": "Service_Name", "threat_object_type": "other"}]
action.risk.param.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"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1569", "T1569.002"], "observable": [{"name": "Service_File_Name", "role": ["Other"], "type": "Other"}, {"name": "Service_Name", "role": ["Other"], "type": "Other"}]}
schedule_window = auto
action.notable = 1
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 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 ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"]}
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 = []
action.escu.analytic_story = ["Active Directory Lateral Movement"]
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 = [{"threat_object_field": "Service_File_Name", "threat_object_type": "other"}, {"risk_object_field": "ComputerName", "risk_object_type": "system", "risk_score": 54}]
action.risk.param.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"], "observable": [{"name": "Service_File_Name", "role": ["Other"], "type": "Other"}, {"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]}
schedule_window = auto
action.notable = 1
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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1543", "T1543.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.how_to_implement = You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes 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 = 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 = []
action.escu.analytic_story = ["Ryuk Ransomware"]
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 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.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 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
action.escu.analytic_story = ["Host Redirection"]
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 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['dest']
action.notable.param.rule_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.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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
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 = 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 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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 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 = []
action.escu.analytic_story = ["Spearphishing Attachments"]
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]}
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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"]}
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 = 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 = 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 = []
action.escu.analytic_story = ["Spearphishing Attachment"]
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 Attachment"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566", "T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]}
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 - 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 = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"]}
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 = 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 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 = []
action.escu.analytic_story = ["Active Directory Discovery"]
action.risk = 1
action.risk.param._risk_message = Local group discovery 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.verbose = 0
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"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1069", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
schedule_window = auto
action.notable = 1
action.notable.param.nes_fields = ['user', 'dest']
action.notable.param.rule_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.notable.param.rule_title = Wmic Group 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=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 is to detect a suspicious wmic commandlined that uninstall application non interactively. This technique was seen in IceID to uninstall av products to the compromised host to bypassed and evade detections. 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect a suspicious wmic commandlined that uninstall application non interactively. This technique was seen in IceID to uninstall av products to the compromised host to bypassed and evade detections. 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 = 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 = third party application may use this approach to uninstall there application
action.escu.creation_date = 2021-10-20
action.escu.modification_date = 2021-10-20
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 = []
action.escu.analytic_story = ["IceID"]
action.risk = 1
action.risk.param._risk_message = wmic $process$ with commandline $process$ in $dest$
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}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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 NonInteractive App Uninstallation - Rule
action.correlationsearch.annotations = {"analytic_story": ["IceID"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001", "T1562"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]}
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 wmic commandlined that uninstall application non interactively. This technique was seen in IceID to uninstall av products to the compromised host to bypassed and evade detections. 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.notable.param.rule_title = Wmic NonInteractive App Uninstallation
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
allow_skew = 100%
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 - 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1047"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1047"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 is to detect 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 script like powershell or create 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055", "T1543", "T1134.004", "T1134"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This analytic is to detect 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 script like powershell or create 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 = 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 = user may create vbs or js script that use several tool as part of its execution.
action.escu.creation_date = 2021-10-06
action.escu.modification_date = 2021-10-06
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 = []
action.escu.analytic_story = ["FIN7", "Remcos", "Unusual Processes"]
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.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": ["FIN7", "Remcos", "Unusual Processes"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055", "T1543", "T1134.004", "T1134"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]}
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 spawned process by wscript or cscript process. This technique was a common technique used by adversaries and malware to execute different LOLBIN, other script like powershell or create 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"]}
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 = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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 = []
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.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"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 90, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021", "T1021.006"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]}
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 - 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543.003", "T1543"]}
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 = []
action.escu.analytic_story = ["XMRig"]
action.risk = 1
action.risk.param._risk_message = A driver $ImageLoaded$ related to xmrig crytominer loaded in host $Computer$
action.risk.param._risk = [{"risk_object_field": "Computer", "risk_object_type": "system", "risk_score": 80}, {"threat_object_field": "ImageLoaded", "threat_object_type": "imageloaded"}]
action.risk.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"], "confidence": 100, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543.003", "T1543"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "ImageLoaded", "role": ["Attacker"], "type": "ImageLoaded"}]}
schedule_window = auto
action.notable = 1
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  Computer 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 = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1220"]}
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 = 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 = 2021-09-13
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 = []
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}, {"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}]
action.risk.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"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1220"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]}
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 - 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = aws detect attach to role policy
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 = `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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = aws detect permanent key creation
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 = `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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = aws detect role creation
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 = `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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = aws detect sts assume role abuse
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` 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1550"]}
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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1550"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = aws detect sts get session token abuse
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 = `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 - 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 = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
action.escu.data_models = []
action.escu.eli5 = 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.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 = []
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"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
schedule_window = auto
action.notable = 1
action.notable.param.rule_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.notable.param.rule_title = gcp detect oauth token abuse
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` 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`

### END ESCU DETECTIONS ###


### ESCU BASELINES ###

[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 = 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 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 = 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 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 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 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 = 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 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 = 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 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 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 = 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 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 = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"]
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 = 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 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 = 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 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 = 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 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 = 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 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 = 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 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 - 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 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=10m, 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 - 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 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", "Command and Control", "Suspicious AWS Traffic"]
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 - 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 = []
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 - 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 - 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 - 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 - 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 - 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 = []
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 = []
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 = []
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 1 1 1,4,7,10 *
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.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 | 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 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 = 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 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 = 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 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.csv | stats count

[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.csv | 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 1 1 1,4,7,10 *
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 = 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 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 1 1 1,4,7,10 *
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 1 1 1,4,7,10 *
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 1 1 1,4,7,10 *
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 1 1 1,4,7,10 *
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 1 1 1,4,7,10 *
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 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 = 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 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 = 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 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.csv | 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 = 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 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 = 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 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.csv | 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 = 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 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 = 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 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.csv | stats count

[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 1 1 1,4,7,10 *
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 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 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 1 1 1,4,7,10 *
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 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 1 1 1,4,7,10 *
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 - 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 = 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 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 = 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 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 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 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 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 = 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 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 = 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 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 - 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 = []
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 - 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 = 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 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 = 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 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 - 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 = []
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 = []
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 - 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 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", "Command and Control", "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"]
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 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 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", "Command and Control", "Suspicious AWS Traffic"]
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", "Command and Control", "Suspicious AWS Traffic"]
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 - 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 - 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", "Command and Control", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Cloud Instance 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 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", "Command and Control", "DNS Hijacking", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Host Redirection", "Prohibited Traffic Allowed or Protocol Mismatch", "Suspicious AWS Traffic", "Suspicious DNS Traffic"]
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", "Command and Control", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Suspicious AWS Traffic", "Suspicious DNS Traffic"]
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", "Command and Control", "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"]
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 = []
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", "Command and Control", "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"]
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 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", "Command and Control", "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"]
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 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", "Command and Control", "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"]
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", "Command and Control", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Suspicious AWS Traffic", "Suspicious DNS Traffic"]
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 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 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 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 = []
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 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 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 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=$src$

[ESCU - Rundll32 LockWorkStation - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = ESCU - Rundll32 LockWorkStation - Response Task
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.creation_date = 2021-08-09
action.escu.modification_date = 2021-08-09
action.escu.analytic_story = ["Ransomware"]
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 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 = 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=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`



### END ESCU RESPONSE TASKS ###