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Research interests include adversarial machine learning, deep learning, large-scale malware classification, active learning, and early time-series classification.

Saturday at in 101 Track 45 minutes | Demo, Tool Follow me on a journey where we p0wn one of the most secure platforms on earth.

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.

Prior to joining Endgame he conducted information security and situational awareness research as a researcher at Fire Eye, Mandiant, Sandia National Laboratories and MIT Lincoln Laboratory.

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Saturday at in Track 4 20 minutes | Demo Much of next-gen AV relies on machine learning to generalize to never-before-seen malware.In this talk, I will present methods of privilege escalation on IBM z/OS: How to leverage a simple access to achieve total control over the machine and impersonate other users.If you are interested in mainframes or merely curious to see a what a shell looks like on MVS, you're welcome to tag along.No blobs, no hidden firmware features, and no secret closed source processors.This concept isn't "unhacakable", rather we believe it to be the most fixable; this is what users and hackers should ultimately be fighting for.

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