Bobby Filar
9a739b7e4c
* modifying rules assoc w/ deprecation of v2 ML jobs * modified updated_date field * fixed machine_learning_job_id and added min_stack_version * replacing rest of deprecated jobs with new naming convention * Update ml_suspicious_login_activity.toml * removing rules assoc w/ deprecated ML jobs * Update rules/ml/ml_linux_anomalous_compiler_activity.toml Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com> * Update rules/ml/ml_linux_anomalous_compiler_activity.toml Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com> * updated ml job rules to reflect 8.3 changes * updating min_stack_version for ml detection rules Co-authored-by: Craig Chamberlain <randomuserid@users.noreply.github.com> Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com> Co-authored-by: Apoorva Joshi <30438249+ajosh0504@users.noreply.github.com>
46 lines
2.6 KiB
TOML
46 lines
2.6 KiB
TOML
[metadata]
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creation_date = "2020/03/25"
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maturity = "production"
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updated_date = "2022/05/12"
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min_stack_comments = "Supports latest version of ML job introduced in 8.3"
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min_stack_version = "8.3.0"
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[rule]
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anomaly_threshold = 50
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author = ["Elastic"]
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description = """
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A machine learning job detected activity for a username that is not normally active, which can indicate unauthorized
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changes, activity by unauthorized users, lateral movement, or compromised credentials. In many organizations, new
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usernames are not often created apart from specific types of system activities, such as creating new accounts for new
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employees. These user accounts quickly become active and routine. Events from rarely used usernames can point to
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suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when
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personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for
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malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move
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laterally from one host to another.
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"""
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false_positives = [
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"""
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Uncommon user activity can be due to an engineer logging onto a server instance in order to perform manual
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troubleshooting or reconfiguration.
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""",
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]
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from = "now-45m"
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interval = "15m"
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license = "Elastic License v2"
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machine_learning_job_id = ["v3_linux_anomalous_user_name"]
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name = "Unusual Linux Username"
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note = """## Triage and analysis
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### Investigating an Unusual Linux User
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Detection alerts from this rule indicate activity for a Linux user name that is rare and unusual. Here are some possible avenues of investigation:
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- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host? Could this be related to troubleshooting or debugging activity by a developer or site reliability engineer?
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- Examine the history of user activity. If this user only manifested recently, it might be a service account for a new software package. If it has a consistent cadence (for example if it runs monthly or quarterly), it might be part of a monthly or quarterly business process.
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- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks that the user is performing."""
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references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
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risk_score = 21
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rule_id = "b347b919-665f-4aac-b9e8-68369bf2340c"
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severity = "low"
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tags = ["Elastic", "Host", "Linux", "Threat Detection", "ML"]
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type = "machine_learning"
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