publication . Conference object . Preprint . 2018

deep reinforcement fuzzing

Böttinger, K.; Godefroid, P.; Singh, R.;
Open Access
  • Published: 14 Jan 2018
  • Publisher: IEEE
  • Country: Germany
Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q -learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and prelim...
ACM Computing Classification System: TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS
free text keywords: Markov process, symbols.namesake, symbols, Empirical evidence, Markov decision process, Machine learning, computer.software_genre, computer, Computer security, Artificial intelligence, business.industry, business, Reinforcement, Computer science, Reinforcement learning, Grammar, media_common.quotation_subject, media_common, Fuzz testing, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
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publication . Conference object . Preprint . 2018

deep reinforcement fuzzing

Böttinger, K.; Godefroid, P.; Singh, R.;