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A Cascade Approach for APT Campaign Attribution in System Event Logs: Technique Hunting and Subgraph Matching

Yi-Ting Huang, Ying-Ren Guo, Guo-Wei Wong, Meng Chang Chen

TL;DR

This study addresses the challenge of identifying APT campaign attacks through system event logs with a cascading approach, name SFM, that combines Technique hunting and APT campaign attribution.

Abstract

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. Our approach assumes that real-world system event logs contain a vast majority of normal events interspersed with few suspiciously malicious ones and that these logs are annotated with Techniques of MITRE ATT&CK framework for attack pattern recognition. Then, we attribute APT campaign attacks by aligning detected Techniques with known attack sequences to determine the most likely APT campaign. Evaluations on five real-world APT campaigns indicate that the proposed approach demonstrates reliable performance.

A Cascade Approach for APT Campaign Attribution in System Event Logs: Technique Hunting and Subgraph Matching

TL;DR

This study addresses the challenge of identifying APT campaign attacks through system event logs with a cascading approach, name SFM, that combines Technique hunting and APT campaign attribution.

Abstract

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. Our approach assumes that real-world system event logs contain a vast majority of normal events interspersed with few suspiciously malicious ones and that these logs are annotated with Techniques of MITRE ATT&CK framework for attack pattern recognition. Then, we attribute APT campaign attacks by aligning detected Techniques with known attack sequences to determine the most likely APT campaign. Evaluations on five real-world APT campaigns indicate that the proposed approach demonstrates reliable performance.

Paper Structure

This paper contains 18 sections, 2 equations, 7 figures, 2 tables.

Figures (7)

  • Figure 1: SharpPanda: motivating example
  • Figure 2: Example of audit log events captured by Procmon.
  • Figure 3: SFM workflow
  • Figure 4: Embedding of an audit log event.
  • Figure 5: One-class SVM for anomaly detection in event logs.
  • ...and 2 more figures