Terrance DeJesus
e8c39d19a7
* initial commit with eggshell mitre mapping added * adding updated rules * [Rule Tuning] MITRE for GCP rules I've added Mitre references for the 4 GCP rules missing. Changed 3 of the rules from "Impact" to "Defense Evasion" based on the technique used and it's matched tactic. * [Rule Tuning] Endgame Rule name updates for Mitre Updated Endgame rule names for those with Mitre tactics to match the tactics. * Update rules/integrations/aws/persistence_redshift_instance_creation.toml Co-authored-by: Jonhnathan <jonhnathancesar@gmail.com> * Update rules/integrations/aws/exfiltration_rds_snapshot_restored.toml Co-authored-by: Jonhnathan <jonhnathancesar@gmail.com> * adding 10 updated rules for google_workspace, ml and o365 * adding 22 rule updates for mitre att&ck mappings * adding 24 rule updates related mainly to ML rules * adding 3 rules related to detection via ML * adding adjustments * adding adjustments with solutions to recent pytest errors * removed tabs from tags * adjusted mappings and added techniques * adjusted endgame rule mappings per review * adjusted names to match different tactics * added execution and defense evasion tag * adjustments to address errors from merging with main * added newlines to rules missing them at the end of the file Co-authored-by: imays11 <59296946+imays11@users.noreply.github.com> Co-authored-by: Jonhnathan <jonhnathancesar@gmail.com>
57 lines
2.9 KiB
TOML
57 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 = "2022/07/18"
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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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Searches for rare processes running on multiple hosts in an entire fleet or network. This reduces the detection of false
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positives since automated maintenance processes usually only run occasionally on a single machine but are common to all
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or many hosts in a fleet.
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"""
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false_positives = [
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"""
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A newly installed program or one that runs rarely as part of a monthly or quarterly workflow could trigger this
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alert.
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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_windows_anomalous_process_all_hosts"]
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name = "Anomalous Process For a Windows Population"
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note = """## Triage and analysis
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### Investigating an Unusual Windows Process
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Detection alerts from this rule indicate the presence of a Windows process that is rare and unusual for all of the Windows hosts for which Winlogbeat data is available. 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?
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- Examine the history of execution. If this process only manifested recently, it might be part of 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 metadata like the values of the Company, Description and Product fields which may indicate whether the program is associated with an expected software vendor or package.
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- Examine arguments and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.
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- Consider the same for the parent process. If the parent process is a legitimate system utility or service, this could be related to software updates or system management. If the parent process is something user-facing like an Office application, this process could be more suspicious.
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- If you have file hash values in the event data, and you suspect malware, you can optionally run a search for the file hash to see if the file is identified as malware by anti-malware tools. """
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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 = "6e40d56f-5c0e-4ac6-aece-bee96645b172"
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severity = "low"
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tags = ["Elastic", "Host", "Windows", "Threat Detection", "ML", "Persistence"]
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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 = "T1543"
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name = "Create or Modify System Process"
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reference = "https://attack.mitre.org/techniques/T1543/"
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[rule.threat.tactic]
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id = "TA0003"
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name = "Persistence"
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reference = "https://attack.mitre.org/tactics/TA0003/"
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