Fuzz testing is an effective technique for finding software bugs and security vulnerabilities using randomized test-input generation. While many fuzzing tools perform coverage-guided fuzzing, more coverage doesn’t necessarily mean better bug detection capability. In this project, we aim to devise innovative fuzzing techniques that harness fault detection based on mutation analysis and refine it by means of machine learning methods. For this, we will develop two new fuzzers based on an elementary version of AFL fast fuzzer and contrast these with the conventional coverage-guided variant of the above.
ajitJJadhav/Amplifying-Mutation-Fuzzing
CS293C W22 Final Project: Amplifying Coverage Guided Fuzzing using Mutation Analysis and ML Methods
Python