Deep Reinforcement Fuzzing

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, the authors formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows the application of state-of-the-art deep Q-learning algorithms that optimize rewards, which are defined from runtime properties of the program under test.


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