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Machine Learning Detected a Suspicious Windows Event with a High Malicious Probability Score

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Machine Learning Detected a Suspicious Windows Event with a High Malicious Probability Score

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A supervised machine learning model (ProblemChild) has identified a suspicious Windows process event with high probability of it being malicious activity. Alternatively, the model’s blocklist identified the event as being malicious.

Rule type: eql

Rule indices:

  • endgame-*
  • logs-endpoint.events.process-*
  • winlogbeat-*

Severity: low

Risk score: 21

Runs every: 5m

Searches indices from: now-10m (Date Math format, see also Additional look-back time)

Maximum alerts per execution: 100

References:

Tags:

  • OS: Windows
  • Data Source: Elastic Endgame
  • Use Case: Living off the Land Attack Detection
  • Rule Type: ML
  • Rule Type: Machine Learning
  • Tactic: Defense Evasion

Version: 2

Rule authors:

  • Elastic

Rule license: Elastic License v2

Rule query

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process where ((problemchild.prediction == 1 and problemchild.prediction_probability > 0.98) or
blocklist_label == 1) and not process.args : ("*C:\\WINDOWS\\temp\\nessus_*.txt*", "*C:\\WINDOWS\\temp\\nessus_*.tmp*")

Framework: MITRE ATT&CKTM

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