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Improving Fuzz Testing using Game Theory

Abstract : We propose a game theoretical model for fuzz testing, consisting in generating unexpected input to search for software vulnerabilities. As of today, no performance guarantees or assessment frameworks for fuzzing exist. Our paper addresses these issues and describes a simple model that can be used to assess and identify optimal fuzzing strategies, by leveraging game theory. In this context, payoff functions are obtained using a tainted data analysis and instrumentation of a target application to assess the impact of different fuzzing strategies.
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Submitted on : Monday, December 13, 2010 - 8:09:03 PM
Last modification on : Wednesday, February 2, 2022 - 3:51:44 PM
Long-term archiving on: : Monday, March 14, 2011 - 2:35:41 AM

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Sheila Becker, Humberto Abdelnur, Jorge Lucangeli Obes, Radu State, Olivier Festor. Improving Fuzz Testing using Game Theory. 4th International Conference on Network and System Security - NSS'2010, Sep 2010, Mebourne, Australia. ⟨inria-00546174⟩

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