Jonhnathan
38b8311482
* [Security Content] Expand Abbreviated Tags * . * Update privilege_escalation_gcp_kubernetes_rolebindings_created_or_patched.toml * Apply suggestions from code review Co-authored-by: Isai <59296946+imays11@users.noreply.github.com> * Revert changes to deprecated rules * Bump updated_date --------- Co-authored-by: Isai <59296946+imays11@users.noreply.github.com>
59 lines
2.9 KiB
TOML
59 lines
2.9 KiB
TOML
[metadata]
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creation_date = "2020/03/25"
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maturity = "production"
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updated_date = "2023/03/06"
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min_stack_comments = "New fields added: required_fields, related_integrations, setup"
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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", "Machine Learning", "Initial Access"]
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type = "machine_learning"
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[[rule.threat]]
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framework = "MITRE ATT&CK"
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[[rule.threat.technique]]
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id = "T1078"
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name = "Valid Accounts"
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reference = "https://attack.mitre.org/techniques/T1078/"
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[rule.threat.tactic]
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id = "TA0001"
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name = "Initial Access"
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reference = "https://attack.mitre.org/tactics/TA0001/"
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