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Unusual Process Writing Data to an External Device

Elastic Detection Rules

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Summary
This detection rule utilizes machine learning to identify unusual processes that exhibit potentially malicious behavior by writing data to external devices. The logic behind the rule is based on the premise that attackers often utilize benign-looking processes to discreetly exfiltrate sensitive information. The key metric monitored is the anomaly score, and here it's set with a threshold of 75, indicating a significant deviation from normal behavior. The rule triggers when a process that is rarely observed performing such actions is detected, resulting in alerts to analysts who can investigate further. The setup requires the integration of Data Exfiltration Detection, which necessitates a proper configuration of data sources, including network and file events via the Elastic Defend integration. This ensures comprehensive monitoring and data collection necessary for machine learning analysis. The risk score is rated as low (21), suggesting a moderate level of concern, but the nature of the anomaly lays the groundwork for serious implications concerning data breaches if not investigated promptly. The rule is particularly useful in corporate networks where identifying and stopping unauthorized data transfers is critical to maintaining security integrity. The accompanying investigation guide encourages analysts to validate the legitimacy of the processes in question, assess user behavior, and perform a deeper analysis of the connections established with external devices to mitigate potential false positives from benign operations like backups or updates. The integration of this detection system within the overall cybersecurity posture is crucial for effective threat management and incident response.
Categories
  • Endpoint
  • Network
  • Cloud
Data Sources
  • Process
  • Network Traffic
  • File
ATT&CK Techniques
  • T1052
Created: 2023-09-22