Myles Foley

Myles Foley

Ph.D. Student in Computing

Myles is a PhD student at Imperial College London under the supervision of Dr. Sergio Maffeis in the Applied Computer Security (ACS) lab. Prior to this he received a Masters of Engineering from University College London, earning the ‘Outstanding MEng Graduating Student’ prize from the Department of Electronic and Electrical Engineering. His research focus is on applying Reinforcement Learning to Web Application Security. This involves the fuzz testing using deep reinforcement learning techniques to find vulnerabilties.

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RL, I Choose You: Using RL to learn mutation strategies in fuzzing

Lightning Talk

In this talk I will motivate some of the key challenges in fuzzing, particularly related to increasing code coverage and finding bugs and vulnerabilities in programs. I will then discuss how this problem can be consider an exploration/exploitation problem particularly with relation Reinforcement Learning (RL). I will then propose an approach to use a special case of RL to select the strategy by selecting a location for mutating a test case. I will then demonstrate how this can be applied in practice and share some preliminary results.