Ross Wolf
5fcece8416
Co-Authored-By: Brent Murphy <56412096+bm11100@users.noreply.github.com> Co-Authored-By: Craig Chamberlain <randomuserid@users.noreply.github.com> Co-Authored-By: David French <56409778+threat-punter@users.noreply.github.com> Co-Authored-By: Derek Ditch <dcode@users.noreply.github.com> Co-Authored-By: Justin Ibarra <brokensound77@users.noreply.github.com>
37 lines
1.5 KiB
TOML
37 lines
1.5 KiB
TOML
[metadata]
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creation_date = "2020/03/25"
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ecs_version = ["1.5.0"]
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maturity = "production"
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updated_date = "2020/03/25"
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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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Identifies unusual parent-child process relationships that can indicate malware execution or persistence mechanisms.
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Malicious scripts often call on other applications and processes as part of their exploit payload. For example, when a
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malicious Office document runs scripts as part of an exploit payload, Excel or Word may start a script interpreter
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process, which, in turn, runs a script that downloads and executes malware. Another common scenario is Outlook running
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an unusual process when malware is downloaded in an email. Monitoring and identifying anomalous process relationships is
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a method of detecting new and emerging malware that is not yet recognized by anti-virus scanners.
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"""
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false_positives = [
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"""
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Users running scripts in the course of technical support operations of software upgrades could trigger this signal.
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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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signal.
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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"
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machine_learning_job_id = "windows_anomalous_process_creation"
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name = "Anomalous Windows Process Creation"
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references = ["https://www.elastic.co/guide/en/siem/guide/current/prebuilt-ml-jobs.html"]
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risk_score = 21
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rule_id = "0b29cab4-dbbd-4a3f-9e8e-1287c7c11ae5"
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severity = "low"
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tags = ["Elastic", "ML", "Windows"]
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type = "machine_learning"
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