#############
# Automatically generated by generator.py in splunk/security_content
# On Date: 2021-03-12T17:19:06 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############

### ESCU DETECTIONS ###

[ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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.  This search is deprecated and have been translated to use the latest Authentication Datamodel.
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.  This search is deprecated and have been translated to use the latest Authentication Datamodel.
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - AWS Cross Account Activity From Previously Unseen Account - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Authentication Activities"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator 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 which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator 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 cloudtrail logs
action.escu.known_false_positives = unknown
action.escu.creation_date = 2021-01-11
action.escu.modification_date = 2021-01-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Ransomware Cloud"]
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"], "mitre_attack": ["T1486"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Ransomware Cloud"]
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"], "mitre_attack": ["T1486"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 EKS Kubernetes cluster sensitive object access - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Network Access Control List Created with All Open Ports - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for 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"], "nist": ["DE.DP", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = The search looks for 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 CloudTrail inputs.
action.escu.known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment.
action.escu.creation_date = 2021-01-11
action.escu.modification_date = 2021-01-11
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Network Access Control List Created with All Open Ports - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.risk = 1
action.risk.param._risk_object = userName
action.risk.param._risk_object_type = user
action.risk.param._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 Network Access Control List Created with All Open Ports - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Network ACL Activity"], "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 CloudTrail logs to detect users deleting network ACLs.
action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "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 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 CloudTrail inputs.
action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL.
action.escu.creation_date = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["AWS Network ACL Activity"]
action.risk = 1
action.risk.param._risk_object = userName
action.risk.param._risk_object_type = user
action.risk.param._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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 cloudtrail logs
action.escu.known_false_positives = Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks.
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS SAML Access by Provider User and Principal - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - AWS SAML Access by Provider User and Principal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Federated Credential Abuse"], "mitre_attack": ["T1078"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 cloudtrail logs.
action.escu.known_false_positives = Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored.
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - AWS SAML Update identity provider - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - AWS SAML Update identity provider - Rule
action.correlationsearch.annotations = {"analytic_story": ["Cloud Federated Credential Abuse"], "mitre_attack": ["T1078"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Abnormally High AWS Instances Launched by User - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for 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 = This search looks for 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 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_object = userName
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = This search looks for 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 = This search looks for 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 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_object = src_usr
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = This search looks for 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 = This search looks for 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = This search looks for 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 = This search looks for 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Abnormally High Number Of Cloud Infrastructure API Calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Abnormally High Number Of Cloud Security Group API Calls - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -30m@m
dispatch.latest_time = now
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"], "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 = Detect memory dumping of the LSASS process.
action.notable.param.rule_title = Access LSASS Memory for Dump Creation Notable
action.notable.param.security_domain = endpoint
action.notable.param.severity = high
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1059.001"]}
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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe) Processes.process=*DownloadFile* 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)`| `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": ["T1059.001"]}
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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe Processes.process=*.DownloadString*  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)`| `any_powershell_downloadstring_filter`

[ESCU - Attempt To Add Certificate To Untrusted Store - Rule]
action.escu = 0
action.escu.enabled = 1
description = Attempt to add a certificate to the certificate store
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = Attempt to add a certificate to the certificate 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 = 2020-11-03
action.escu.modification_date = 2020-11-03
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"]
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"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = 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=*certutil* (Processes.process=*-addstore*) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter`

[ESCU - Attempt To 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": ["T1059.001"], "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 - Attempt To 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"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Attempt To 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"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_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"], "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 = 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. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service.,
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"]
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"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = net.exe OR  Processes.process_name = sc.exe) Processes.process="* stop *" 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)` |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"], "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 = 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 = None identified.
action.escu.creation_date = 2019-12-02
action.escu.modification_date = 2019-12-02
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"]
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"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) 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.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 - 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": ["T1204.002"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = The search looks for a batch file (.bat) written to the Windows system directory tree.
action.escu.how_to_implement = 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 = 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 = 2018-12-14
action.escu.modification_date = 2018-12-14
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"]
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"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1204.002"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_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 = 
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"]
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"], "kill_chain_phases": ["Installation"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = 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=certutil.exe Processes.process = "* -exportPFX *" by Processes.parent_process Processes.process_name Processes.process Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Clients Connecting to Multiple DNS Servers - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.
action.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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` 

[ESCU - Cloud 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Cloud API Calls From Previously Unseen User Roles - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 2020-08-21
action.escu.modification_date = 2020-08-21
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._risk_score = 20
action.risk.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"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._risk_score = 20
action.risk.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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._risk_score = 20
action.risk.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"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._risk_score = 20
action.risk.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"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Cloud Instance Modified By Previously Unseen User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Instance Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Cloud Provisioning Activity From Previously Unseen City - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Cloud Provisioning Activity From Previously Unseen Country - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Cloud Provisioning Activity From Previously Unseen IP Address - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - Cloud Provisioning Activity From Previously Unseen Region - Rule
action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1055"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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*) | 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 - 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) 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": ["T1070.005"], "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 = 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 or delete network shares. You should verify that the activity was intentional and 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 - 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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"], "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.
action.escu.known_false_positives = Legtimate administrator usage of wmic to create a shadow copy.
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 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter`

[ESCU - 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 prcoess 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"], "nist": ["DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 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 = unknown
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\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.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"], "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 = 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 = unknown
action.escu.creation_date = 2019-12-10
action.escu.modification_date = 2019-12-10
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* 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)` | `credential_dumping_via_symlink_to_shadow_copy_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"], "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"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 2021-01-18
action.escu.modification_date = 2021-01-18
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"]
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"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query |  `drop_dm_object_name("DNS")` | 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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"]
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"], "cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = This search looks for 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 = This search looks for 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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", "T1557.002"], "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", "T1557.002"], "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
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 = This search looks for successful 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 = This search looks for successful 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.
action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
action.escu.creation_date = 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "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 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 CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 earliest(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 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 CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 earliest(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 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 CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour
action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 earliest(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": ["T1550.002"], "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 = ["Lateral Movement"]
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": ["Lateral Movement"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1550.002"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1210"], "nist": ["DE.AE", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 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": ["T1078.002"], "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"]
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"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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": ["T1078.003"], "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"]
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"], "mitre_attack": ["T1078.003"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will have a randomized name of 8 characters long. Review the file for suspect commands. Identify additional log sources, IIS included, to review source and other potential exploitation.
action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["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. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will have a randomized name of 8 characters long. Review the file for suspect commands. Identify additional log sources, IIS included, to review source and other potential exploitation.
action.escu.how_to_implement = To successfully implement this search you need to be 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 = 2021-03-09
action.escu.modification_date = 2021-03-09
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as aspx_pid | join aspx_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=w3wp.exe  by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as aspx_pid | fields _time aspx_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, aspx_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 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 hh.exe may be used.
action.escu.known_false_positives = Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed.
action.escu.creation_date = 2021-02-11
action.escu.modification_date = 2021-02-11
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `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": ["T1218.001"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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": ["T1218.001"], "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.
action.escu.known_false_positives = Although unlikely, some legitimate applications may retrieve a CHM remotely, 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 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* 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_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": ["T1218.001"], "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.
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 = 2021-02-11
action.escu.modification_date = 2021-02-11
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe 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", "T1557.002"], "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", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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": ["T1218.005"], "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.
action.escu.known_false_positives = It is possible legitimate applications may perform this behavior and will need to be filtered.
action.escu.creation_date = 2021-01-20
action.escu.modification_date = 2021-01-20
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") 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)` | `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"], "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"]
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"], "cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.AE", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?<privs>\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process |  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter`

[ESCU - Detect New 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1136.001"], "nist": ["PR.AC", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 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 = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Open S3 Buckets over AWS CLI - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_object = src
action.risk.param._risk_object_type = system
action.risk.param._risk_score = 20
action.risk.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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventSource="s3.amazonaws.com" 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 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 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 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 = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Suspicious AWS S3 Activities"]
action.risk = 1
action.risk.param._risk_object = src
action.risk.param._risk_object_type = system
action.risk.param._risk_score = 20
action.risk.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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 userName userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` 

[ESCU - Detect Oulook 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": ["T1566.001"], "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 Oulook 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 = ["Phishing Payloads"]
cron_schedule = 0 * * * *
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
action.correlationsearch.enabled = 1
action.correlationsearch.label = ESCU - Detect Oulook exe writing a  zip file - Rule
action.correlationsearch.annotations = {"analytic_story": ["Phishing Payloads"], "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.001"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_oulook_exe_writing_a__zip_file_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"], "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"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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", "T1557.002"], "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", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1059.003"], "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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=cmd.exe 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)` |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": ["T1021.002"], "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 = 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 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 = 2020-11-10
action.escu.modification_date = 2020-11-10
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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=*psexec* Processes.process=*accepteula* by Processes.process_name Processes.dest  Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter`

[ESCU - Detect Rare Executables - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search will return a table of 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1218.009"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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": ["T1218.009"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1218.009"], "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 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 or 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 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `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": ["T1218.009"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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": ["T1218.009"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1218.009"], "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 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 = 2021-02-12
action.escu.modification_date = 2021-02-12
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `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": ["T1218.010"], "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 = 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 modifying/removing the !=regsv32.exe.
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* 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_regsvr32_application_control_bypass_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
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": ["T1218.011"], "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.
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*advpack* by Processes.user Processes.process_name Processes.process Processes.dest | `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": ["T1218.011"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*setupapi* by Processes.user Processes.process_name Processes.process Processes.dest | `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": ["T1218.011"], "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.
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*syssetup* by Processes.user Processes.process_name Processes.process Processes.dest | `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": ["T1218.005"], "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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) 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)` | `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_object = src_ip
action.risk.param._risk_object_type = system
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`

[ESCU - Detect Software Download To Network Device - Rule]
action.escu = 0
action.escu.enabled = 1
description = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.
action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005"], "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"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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 = 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 = 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
action.escu.known_false_positives = None
action.escu.creation_date = 2021-01-26
action.escu.modification_date = 2021-01-26
action.escu.confidence = high
action.escu.full_search_name = ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 EC2 Instance - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Security Hub Alerts"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._risk_score = 20
action.risk.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 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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_object = src_ip
action.risk.param._risk_object_type = system
action.risk.param._risk_score = 20
action.risk.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
alert.digest_mode = 1
disabled = true
enableSched = 1
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", "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", "T1498", "T1020.001"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1059.003"], "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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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"]
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"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1189"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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": ["T1218.005"], "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.
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 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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) 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)` | `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": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 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 use a moved copy of mshta.exe, but never renamed, 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 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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `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 = 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 = 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 CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = This search looks for 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 = This search looks for 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 CloudTrail inputs. Run the "Previously seen users in 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]
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"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1016"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 web traffic to dynamic domain providers - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"], "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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1548.002"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_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"], "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 = 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 = 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_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 (OriginalFileName=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"], "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 (OriginalFileName=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.
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 - 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=procdump.exe OR Processes.process_name=procdump64.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process 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 = 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 = 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"]
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 Rename - Rule
action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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_object = user
action.risk.param._risk_object_type = user
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = This search looks for 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 = This search looks for 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 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_object = src
action.risk.param._risk_object_type = system
action.risk.param._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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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 - EC2 Instance Started With Previously Unseen AMI - Rule
action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining"], "cis20": ["CIS 1"], "nist": ["ID.AM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1114.001"]}
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": ["T1114.001"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1114.002"], "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": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"]}
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"]
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"], "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548.002"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"], "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"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Execution of File With Spaces Before Extension - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.
action.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 = 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"]
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 Spaces Before Extension - Rule
action.correlationsearch.annotations = {"analytic_story": ["Windows File Extension and Association Abuse"], "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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1036.003"], "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"]
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"], "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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 = 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 = 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 - Extended Period Without Successful Netbackup Backups - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor Backup Solution"], "cis20": ["CIS 10"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"]
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"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 - 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1569.002"], "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": ["T1569.002"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]}
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"]
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"], "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1112", "T1548.002"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 - GCP Detect accounts with high risk roles by project - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - GCP GCR container uploaded - Rule
action.correlationsearch.annotations = {"analytic_story": ["Container Implantation Monitoring and Investigation"], "mitre_attack": ["T1525"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - GCP Kubernetes cluster scan detection - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1222.001"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1222.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"], "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"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"], "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"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Identify New User Accounts - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["Actions on Objectives"], "mitre_attack": ["T1558.003"], "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 = ["Lateral Movement"]
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": ["Lateral Movement"], "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Kubernetes AWS detect RBAC authorization by account - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Kubernetes AWS detect sensitive role access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Kubernetes Azure detect sensitive role access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Kubernetes Azure pod scan fingerprint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "kill_chain_phases": ["Reconnaissance"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Kubernetes Azure scan fingerprint - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Kubernetes GCP detect sensitive role access - Rule
action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1498.002"], "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": ["T1498.002"], "nist": ["PR.PT", "DE.AE", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient.
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 = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient.
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 = 2020-11-20
action.escu.modification_date = 2020-11-20
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"]
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"], "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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `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 = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script 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": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
action.escu.data_models = ["Endpoint"]
action.escu.eli5 = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line.
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 = System administrators may use this option, but it's not common.
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 - 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"]
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"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `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": ["T1059.001"], "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 = 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 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"]
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"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Processes.process_name=powershell.exe (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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1059.001"], "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 = 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 = 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"]
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"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 - Monitor DNS For Brand Abuse - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - Monitor DNS For Brand Abuse - Rule
action.correlationsearch.annotations = {"analytic_story": ["Brand Monitoring"], "kill_chain_phases": ["Delivery", "Actions on Objectives"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.010"], "nist": ["PR.PT", "DE.CM", "PR.AC"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1078.001"], "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": ["T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 - 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 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1059.001"]}
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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe (Processes.process=*Net.Sockets.TCPClient* AND Processes.process=*System.Text.ASCIIEncoding*) 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)`| `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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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"]}
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 Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "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 - O365 Add App Role Assignment Grant User - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]}
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 Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "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 - O365 Added Service Principal - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]}
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 = 2021-01-12
action.escu.modification_date = 2021-01-12
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Bypass MFA via Trusted IP - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - O365 Bypass MFA via Trusted IP - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1562.007"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 vendor_product 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 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 - O365 Disable MFA - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 - O365 Excessive Authentication Failures Alert - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "kill_chain_phases": ["Not Applicable"], "mitre_attack": ["T1110"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "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 - O365 Excessive SSO logon errors - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]}
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 Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
action.escu.analytic_story = ["Office 365 Detections", "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 - O365 New Federated Domain Added - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections", "Cloud Federated Credential Abuse"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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 - O365 PST export alert - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1114"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Suspicious Admin Email Forwarding - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - O365 Suspicious Admin Email Forwarding - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 
action.escu.known_false_positives = Service Accounts
action.escu.creation_date = 2020-12-15
action.escu.modification_date = 2020-12-15
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Suspicious Rights Delegation - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - O365 Suspicious Rights Delegation - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 
action.escu.known_false_positives = unknown
action.escu.creation_date = 2020-12-16
action.escu.modification_date = 2020-12-16
action.escu.confidence = high
action.escu.full_search_name = ESCU - O365 Suspicious User Email Forwarding - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - O365 Suspicious User Email Forwarding - Rule
action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003"], "nist": ["DE.DP", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1078.001"], "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": ["T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1078.001"], "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": ["T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1078.001"], "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": ["T1078.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Open Redirect in Splunk Web - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Enterprise Vulnerability"], "cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1546.008"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.008"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 - 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": ["T1566.002"], "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 = 2021-01-28
action.escu.modification_date = 2021-01-28
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 = ["Phishing Payloads"]
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": ["Phishing Payloads"], "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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:\\Temp*"  by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*  by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, 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 Execution via WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for processes 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 = []
action.escu.eli5 = This search looks for processes 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 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `process_execution_via_wmi_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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 must be ingesting data that records process activity from your hosts to populate the endpoint data model
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 = 2020-07-10
action.escu.modification_date = 2020-07-10
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
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"], "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"], "nist": ["DE.AE", "PR.AC"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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 = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases.
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 = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases.
action.escu.how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model.
action.escu.known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare.
action.escu.creation_date = 2020-11-04
action.escu.modification_date = 2020-11-04
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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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="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 - 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"], "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"]
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"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1574.011"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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 = 2020-11-27
action.escu.modification_date = 2020-11-27
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 "]
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 "], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.001"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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\\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=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"], "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"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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"], "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", "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", "Lateral Movement"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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"], "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", "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", "Lateral Movement"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 WMI - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system.
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 wmic.exe being launched with parameters to spawn a process on a remote system.
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 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 = 2020-11-30
action.escu.modification_date = 2020-11-30
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"]
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"], "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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" 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)` | `remote_process_instantiation_via_wmi_filter`

[ESCU - Remote Registry Key modifications - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = []
action.escu.eli5 = This search monitors for remote modifications to registry keys.
action.escu.how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right.
action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out.
action.escu.creation_date = 2020-03-02
action.escu.modification_date = 2020-03-02
action.escu.confidence = high
action.escu.full_search_name = ESCU - Remote Registry Key modifications - Rule
action.escu.search_type = detection
action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
action.escu.providing_technologies = []
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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 WMI Command Attempt - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for wmic.exe being launched with parameters to operate on remote systems.
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 wmic.exe being launched with parameters to operate on remote systems.
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. 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.
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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=wmic.exe  AND Processes.process= */node* 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)` | `remote_wmi_command_attempt_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": ["T1218.011"], "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 = 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 = 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"]
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"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = rundll32.exe by Processes.process_name Processes.parent_process_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 - 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"]
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"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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": ["T1059.003"]}
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"]
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"], "kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1059.003"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 - 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"], "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"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]
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"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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"], "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"]
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"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1543.003"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 tasks used in BadRabbit ransomware - 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 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 scheduling job on remote system - 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 job is being scheduled on a remote system.
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 = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system.
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 jobs 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 = 2020-07-21
action.escu.modification_date = 2020-07-21
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 = ["Lateral Movement", "NOBELIUM Group"]
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": ["Lateral Movement", "NOBELIUM Group"], "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR 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"], "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 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 = 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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 = []
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.
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = "scrcons.exe" by Processes.user Processes.dest Processes.process_name  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `script_execution_via_wmi_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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"], "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"]
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"], "mitre_attack": ["T1136.001"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1204.002"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
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 = 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 = 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 - Spectre and Meltdown Vulnerable Systems - Rule
action.correlationsearch.annotations = {"analytic_story": ["Spectre And Meltdown Vulnerabilities"], "cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Splunk Enterprise Information Disclosure - Rule
action.correlationsearch.annotations = {"analytic_story": ["Splunk Enterprise Vulnerability CVE-2018-11409"], "cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = []
action.escu.eli5 = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area.
action.escu.how_to_implement = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1105"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 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 = 2021-02-23
action.escu.modification_date = 2021-02-23
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (process_name=dllhost.exe OR OriginalFileName=dllhost.exe) | regex CommandLine="(dllhost\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_dllhost_no_command_line_arguments_filter`

[ESCU - Suspicious Email - UBA Anomaly - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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"], "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"], "nist": ["DE.AE", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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 File Write - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 2021-02-23
action.escu.modification_date = 2021-02-23
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (process_name=gpupdate.exe OR OriginalFileName=GPUpdate.exe) | regex CommandLine="(gpupdate\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_gpupdate_no_command_line_arguments_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
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 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 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (OriginalFileName=msbuild.exe OR process_name=msbuild.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `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": ["T1127.001"], "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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process 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"]}
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"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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"]}
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"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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": ["T1218.010"], "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.
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"]
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"], "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (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.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 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 OriginalFileName from Sysmon, 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": ["T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 OriginalFileName from Sysmon, 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 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 = 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `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": ["T1218.011"], "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.
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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*start* 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_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": ["T1218.011"], "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.
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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=*dllregisterserver* 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_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": ["T1218.011"], "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 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 use a moved copy of rundll32, triggering a false positive.
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 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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"]}
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"]
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"], "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1053.005"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 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 = 2021-02-23
action.escu.modification_date = 2021-02-23
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (process_name=searchprotocolhost.exe OR OriginalFileName=SearchProtocolHost.exe) | regex CommandLine="(searchprotocolhost\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_searchprotocolhost_no_command_line_arguments_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": ["T1127, T1036.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
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 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 use a moved copy of microsoft.workflow.compiler.exe, 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 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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127, T1036.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = `sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_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": ["T1127.001", "T1036.003"], "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.
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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest 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": ["T1218.005"], "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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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": ["T1218.005"], "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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user | `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, or system event logs.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "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, 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 = 2020-07-22
action.escu.modification_date = 2020-07-22
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"]
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"], "cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") 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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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"]
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"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = 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 - 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 = 2020-10-12
action.escu.modification_date = 2020-10-12
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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `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 normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location.  This can indicate a malicious process that is trying to hide as a legitimate process.
action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["PR.PT", "DE.CM"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location.  This can indicate a malicious process that is trying to hide as a legitimate process.
action.escu.how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable.
action.escu.known_false_positives = None identified
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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name 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 - 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": ["T1071.001"], "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": ["T1071.001"], "nist": ["DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
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 - 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 - 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"], "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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = 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 - Unsigned Image Loaded by LSASS - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Unsuccessful Netbackup backups - Rule
action.correlationsearch.annotations = {"analytic_story": ["Monitor Backup Solution"], "cis20": ["CIS 10"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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": ["T1505.003"]}
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.
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"]
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"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 Processes.process_name=cmd.exe OR Processes.process_name=powershell.exe by Processes.dest Processes.parent_process 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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": ["T1546.003"], "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 collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. 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"]
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"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.003"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - Web Fraud - Account Harvesting - Rule]
action.escu = 0
action.escu.enabled = 1
description = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - Web Fraud - Password Sharing Across Accounts - Rule
action.correlationsearch.annotations = {"analytic_story": ["Web Fraud Detection"], "cis20": ["CIS 16"], "nist": ["DE.DP"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 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 - 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 = 2020-12-16
action.escu.modification_date = 2020-12-16
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"]
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"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1018"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 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"], "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"]
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"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000001)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`

[ESCU - Windows Event Log Cleared - Rule]
action.escu = 0
action.escu.enabled = 1
description = This search looks for Windows events that indicate one of the Windows event logs has been purged.
action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]}
action.escu.data_models = []
action.escu.eli5 = This search looks for Windows events that indicate one of the Windows event logs has been purged.
action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts.
action.escu.known_false_positives = It is possible that these logs may be legitimately cleared by Administrators.
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"]
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"], "cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
is_visible = false
search = (`wineventlog_security` (EventCode=1102 OR EventCode=1100)) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_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"]
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"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1489"], "nist": ["PR.PT", "DE.CM"]}
schedule_window = auto
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 connhost exe started forcefully - Rule]
action.escu = 0
action.escu.enabled = 1
description = The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. 
action.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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 0
realtime_schedule = 0
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
counttype = number of events
relation = greater than
quantity = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 = 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 = 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 - 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
alert.digest_mode = 1
disabled = true
enableSched = 1
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 - 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 = 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
disabled = true
is_visible = false
search = `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count

[ESCU - Baseline of Command Line Length - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Command Line Length - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.
action.escu.creation_date = 2019-05-08
action.escu.modification_date = 2019-05-08
action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Unusual Processes"]
action.escu.data_models = []
cron_schedule = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = `cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1

[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = support
action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK
description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window.
action.escu.creation_date = 2019-11-14
action.escu.modification_date = 2019-11-14
action.escu.analytic_story = ["Suspicious AWS EC2 Activities"]
action.escu.data_models = []
cron_schedule = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
disabled = true
is_visible = false
search = `cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1

[ESCU - 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 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 = 0 * * * *
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 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 All_Changes.status=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 = 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 = 0 * * * *
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 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 = 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 = 0 * * * *
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 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 = 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 = 0 * * * *
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 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 = 0 * * * *
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 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 = 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count

[ESCU - Previously seen 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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"]
action.escu.data_models = ["Endpoint"]
cron_schedule = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail | stats count

[ESCU - 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@m
dispatch.latest_time = -10m@m
schedule_window = auto
action.escu.providing_technologies = []
action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.
disabled = true
is_visible = false
search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail

[ESCU - 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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 = 0 * * * *
enableSched = 1
dispatch.earliest_time = -70m@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 ###

[response - 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 = response - 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 = sourcetype="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

[response - 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 = response - 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 = | search sourcetype=aws:cloudtrail | search user=$user$| table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType

[response - 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 = response - 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 = | search sourcetype=aws: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

[response - 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 = response - 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 = | search sourcetype=aws:description| rename id as networkAclId | search  networkAclId=$networkAclId$ | table id account_id vpc_id network_acl_entries{}.*

[response - 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 = response - 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 = | search sourcetype=aws:config resourceId=$resourceId$ | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId  configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp

[response - 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 = response - 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 = | search sourcetype=aws:config | rename resourceId as bucketName |search bucketName=$bucketName$ | table resourceCreationTime bucketName vendor_region action aws_account_id supplementaryConfiguration.AccessControlList

[response - All backup logs for host - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 sourcetype="netbackup_logs" dest=$dest$

[response - 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 = response - 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 = sourcetype="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

[response - 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 = response - 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 = sourcetype="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

[response - 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 = response - 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 = | search sourcetype=aws: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

[response - 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 = response - 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 = | search sourcetype=aws: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

[response - 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 = response - 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 = | search sourcetype=aws: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

[response - 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 = response - 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 = | search sourcetype=aws: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

[response - Get Backup Logs For Endpoint - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 = | search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature

[response - 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 = response - 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 `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)`

[response - 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 = response - 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

[response - Get DNS traffic ratio - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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

[response - 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 = response - 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 = | search sourcetype="aws:description" source="*:ec2_instances"| 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

[response - Get EC2 Launch Details - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 = | search sourcetype=aws: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

[response - Get Email Info - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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=$message_id$

[response - Get Emails From Specific Sender - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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=$src_user$

[response - 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 = response - 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.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`

[response - Get History Of Email Sources - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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=$src$

[response - 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 = response - 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 = | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature

[response - 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 = response - 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 = | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature

[response - Get Notable History - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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

[response - 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 = response - 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)`

[response - Get Parent Process Info - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 `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)`

[response - Get Process File Activity - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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

[response - Get Process Info - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 `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)`

[response - 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 = response - 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]

[response - 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 = response - 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]

[response - 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 = response - 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 = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 | rename host as dest | search dest=$dest$| table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter

[response - 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 = response - 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 = | search sourcetype=stream:http session_id  = $session_id$ | stats values(url) values(http_user_agent) by src_ip status

[response - 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 = response - 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 = | search sourcetype=aws:cloudtrail user=$user$ | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType 

[response - 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 = response - 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 = | search sourcetype=aws:cloudtrail vendor_region=$vendor_region$| rename requestParameters.instancesSet.items{}.instanceId as instanceId | stats values(eventName) by user instanceId vendor_region

[response - 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 = response - 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=$user$

[response - 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 = response - 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=$src_ip$

[response - 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 = response - 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 = eventtype=okta_log 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

[response - Investigate Okta Activity by app - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 = eventtype=okta_log 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

[response - Investigate Pass the Hash Attempts - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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=$dest$

[response - Investigate Pass the Ticket Attempts - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 

[response - Investigate Previous Unseen User - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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=$dest$

[response - Investigate Successful Remote Desktop Authentications - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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", "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

[response - 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 = response - 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 = | search sourcetype=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

[response - Investigate User Activities In Okta - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - 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 = eventtype=okta_log 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

[response - Investigate Web POSTs From src - Response Task]
action.escu = 0
action.escu.enabled = 1
action.escu.search_type = investigative
action.escu.full_search_name = response - Investigate Web POSTs From src - Response Task
description = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. 
action.escu.creation_date = 2018-12-06
action.escu.modification_date = 2018-12-06
action.escu.analytic_story = ["Apache Struts Vulnerability"]
action.escu.earliest_time_offset = 3600
action.escu.latest_time_offset = 86400
action.escu.providing_technologies = []
action.escu.data_models = ["Web"]
action.escu.eli5 = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. 
action.escu.how_to_implement = none
action.escu.known_false_positives = None at this time
disabled = true
schedule_window = auto
is_visible = false
search = | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search http_method, "POST" | search src=$src$



### END ESCU RESPONSE TASKS ###

### USAGE DASHBOARD CONFIGURATIONS ###

[escu-metrics-usage]
action.email.useNSSubject = 1
alert.digest_mode = True
alert.suppress = 0
alert.track = 0
auto_summarize.dispatch.earliest_time = -1d@h
dispatchAs = user
search = index=_audit sourcetype="audittrail" \
"ESCU - "\
| stats count(search) by search savedsearch_name user\
| eval usage=(if(savedsearch_name=="","Adhoc","Scheduled")) \
| rex field=search "\"(?<savedsearch_name>.*)\""\
| table savedsearch_name count(search) usage user | join savedsearch_name max=0 type=left [search sourcetype="manifests" | spath searches{} | mvexpand searches{} | spath input=searches{} | table category search_name | rename search_name as savedsearch_name | dedup savedsearch_name] | search category=*

[escu-metrics-search]
action.email.useNSSubject = 1
alert.suppress = 0
alert.track = 0
auto_summarize.dispatch.earliest_time = -1d@h
enableSched = 1
cron_schedule = 0 0 * * *
dispatch.earliest_time = -4h@h
dispatch.latest_time = -1h@h
search = index=_audit action=search | transaction search_id maxspan=3m | search ESCU | stats sum(total_run_time) avg(total_run_time) max(total_run_time) sum(result_count)

[escu-metrics-search-events]
action.email.useNSSubject = 1
alert.digest_mode = True
alert.suppress = 0
alert.track = 0
auto_summarize.dispatch.earliest_time = -1d@h
cron_schedule = 0 0 * * *
enableSched = 1
dispatch.earliest_time = -4h@h
dispatch.latest_time = -1h@h
search = [search index=_audit sourcetype="audittrail" \"ESCU NOT "index=_audit" | where search !="" | dedup search_id | rex field=search "\"(?<search_name>.*)\"" | rex field=_raw "user=(?<user>[a-zA-Z0-9_\-]+)" | eval usage=if(savedsearch_name!="", "scheduled", "adhoc") | eval savedsearch_name=if(savedsearch_name != "", savedsearch_name, search_name) | table savedsearch_name search_id user _time usage | outputlookup escu_search_id.csv | table search_id] index=_audit total_run_time event_count result_count NOT "index=_audit" | lookup escu_search_id.csv search_id | stats count(savedsearch_name) AS search_count avg(total_run_time) AS search_avg_run_time sum(total_run_time) AS search_total_run_time sum(result_count) AS search_total_results earliest(_time) AS firsts latest(_time) AS lasts by savedsearch_name user usage| eval first_run=strftime(firsts, "%B %d %Y") | eval last_run=strftime(lasts, "%B %d %Y")

[escu-metrics-search-longest-runtime]
action.email.useNSSubject = 1
alert.digest_mode = True
alert.suppress = 0
alert.track = 0
auto_summarize.dispatch.earliest_time = -1d@h
enableSched = 1
cron_schedule = 0 0 * * *
disabled = 1
dispatch.earliest_time = -4h@h
dispatch.latest_time = -1h@h
search = index=_* ESCU [search index=_* action=search latest=-2h earliest=-1d| transaction search_id maxspan=3m | search ESCU | stats values(total_run_time) AS run by search_id | sort -run | head 1| table search_id] | table search search_id

[escu-metrics-usage-search]
action.email.useNSSubject = 1
alert.digest_mode = True
alert.suppress = 0
alert.track = 0
auto_summarize.dispatch.earliest_time = -1d@h
cron_schedule = 0 0 * * *
dispatch.earliest_time = -4h@h
dispatch.latest_time = -1h@h
enableSched = 1
dispatchAs = user
search = index=_audit sourcetype="audittrail" \
"ESCU - "\
| stats count(search) by search savedsearch_name user\
| eval usage=(if(savedsearch_name=="","Adhoc","Scheduled")) \
| rex field=search "\"(?<savedsearch_name>.*)\""\
| table savedsearch_name count(search) usage user | join savedsearch_name max=0 type=left [search sourcetype="manifests" | spath searches{} | mvexpand searches{} | spath input=searches{} | table category search_name | rename search_name as savedsearch_name | dedup savedsearch_name] | search category=*

### END OF USAGE DASHBOARD CONFIGURATIONS ###