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Much of next-gen AV relies on machine learning to generalize to never-before-seen malware. Less well appreciated, however, is that machine learning can be susceptible to attack by, ironically, other machine learning models. In this talk, we demonstrate an AI agent trained through reinforcement learning to modify malware to evade machine learning malware detection. Reinforcement learning has produced game-changing AI's that top human level performance in the game of Go and a myriad of hacked retro Atari games (e.g., Pong). In an analogous fashion, we demonstrate an AI agent that has learned through thousands of "games" against a next-gen AV malware detector which sequence of functionality-preserving changes to perform on a Windows PE malware file so that it bypasses the detector. No math or machine learning background is required; fundamental understanding of malware and Windows PE files is a welcome; and previous experience hacking Atari Pong is a plus.
- Def Con 25