Monotonic Malware Classifiers

Training monotonic PDF malware classifier

From XGBoost Doc. Left: fitting the data without enforcing monotonic constraint. Right: fitting the data by enforcing monotonic increasing constraint.
Left: the parsed PDF tree structure of a real PDF malware. Right: the path to every node

Is the monotonic PDF malware classifier more robust against evasion attacks?

Can we do better to evade the monotonic PDF malware classifier?

Takeaways

  • Monotonicity property is very meaningful for malware classification tasks, which eliminates insertion-only attacks.
  • For some datasets, it is possible to achieve high accuracy and low false positive rate to train the monotonic malware classifiers.
  • It is very hard to evade the monotonic malware classifier by doing deletion-only attacks, which may remove malicious behavior. In my experiments, it removes malicious behavior 50% of the time.
  • However, it is possible to design new attacks that utilize the deletion operation while keeping the malicious behavior. Using PDF malware as the example, I designed a new move exploit attack to evade the monotonic classifier.

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Security Researcher http://surrealyz.github.io/

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Yizheng Chen

Yizheng Chen

Security Researcher http://surrealyz.github.io/

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