Jonhnathan
b4c84e8a40
* Update Tags * Bump updated date separately to be easy to revert if needed * Update resource_development_ml_linux_anomalous_compiler_activity.toml * Apply changes from the discussion * Update persistence_init_d_file_creation.toml * Update defense_evasion_timestomp_sysmon.toml * Update defense_evasion_application_removed_from_blocklist_in_google_workspace.toml * Update missing Tactic tags * Update unit tests to match new tags * Add missing IG tags * Delete okta_threat_detected_by_okta_threatinsight.toml * Update command_and_control_google_drive_malicious_file_download.toml * Update persistence_rc_script_creation.toml * Mass bump * Update persistence_shell_activity_by_web_server.toml * . --------- Co-authored-by: Mika Ayenson <Mika.ayenson@elastic.co> Co-authored-by: Mika Ayenson <Mikaayenson@users.noreply.github.com>
59 lines
3.0 KiB
TOML
59 lines
3.0 KiB
TOML
[metadata]
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creation_date = "2020/03/25"
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maturity = "production"
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updated_date = "2023/06/22"
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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 = ["Domain: Endpoint", "OS: Linux", "Use Case: Threat Detection", "Rule Type: ML", "Rule Type: Machine Learning", "Tactic: 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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