Abstract
Generally, the present disclosure is directed to using machine learning to manage a trade-off between exploration and exploitation in a fuzzer system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict inputs that will cause a system being tested to malfunction or crash based on input data provided to the system and output from the system.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Dhillon, Neil and Wadhwa, Tanmay, "Reinforcement Learning for Fuzzing Testing Techniques", Technical Disclosure Commons, (December 07, 2017)
https://www.tdcommons.org/dpubs_series/892
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