MEUZZ

MEUZZ is a machine learning-based hybrid fuzzer which employs supervised machine learning for adaptive and generalizable seed scheduling -- a prominent factor in determining the yields of hybrid fuzzing. MEUZZ determines which new seeds are expected to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions made on the same or similar programs. MEUZZ's learning is based on a series of features extracted via code reachability and dynamic analysis, which incurs negligible runtime overhead (in microseconds). Moreover, MEUZZ automatically infers the data labels by evaluating the fuzzing performance of each selected seed.