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Related Experiment Videos

Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness

Hao Peng1,2, Zehao Yu1, Dandan Zhao1

  • 1College of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang, China.

Scientific Reports
|March 18, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces MalAOI, a novel method for creating adversarial malware by inserting opcodes into portable executable control flow graphs. MalAOI effectively evades graph neural network detection models, demonstrating a 93.73% evasion rate.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Machine learning advancements drive new malware detection techniques.
  • Existing adversarial attacks struggle to modify portable executable (PE) control flow graphs (CFGs).
  • Graph neural network (GNN) models using CFGs pose detection challenges for adversarial malware.

Purpose of the Study:

  • To develop a novel method for generating adversarial malware that evades GNN detection.
  • To address limitations in current function-preserving adversarial attacks on PE malware CFGs.
  • To introduce an autonomous approach for creating effective adversarial malware.

Main Methods:

  • Introduced active opcode insertion, a novel base modification technique for PE CFGs.
  • Utilized reinforcement learning within the MalAOI framework to identify optimal insertion points and opcode sequences.
Keywords:
Control Flow GraphsFunction-preservingGraph Neural NetworksMalware DetectionReinforcement Learning

Related Experiment Videos

  • Generated adversarial malware by modifying PE CFGs while preserving functionality.

    • Main Results:

    • MalAOI achieved an average evasion rate of 93.73% against GNN detection models.
    • The generated adversarial malware showed only a 12.87% increase in byte size.
    • Tested on BODMAS and SOREL-20M datasets, confirming effectiveness.

    Conclusions:

    • Active opcode insertion is an effective strategy for generating functional adversarial malware.
    • MalAOI successfully evades GNN-based malware detection by modifying PE CFGs.
    • The method offers a promising solution for bypassing advanced malware detection systems.