This repo is the released code of FuzzGuard in USENIX Security 2020.
We propose FuzzGuard, a deep-learning-based approach to predict the reachability of inputs (i.e., miss the target or not) before executing the target program, helping DGF filtering out the unreachable ones to boost the performance of fuzzing. To apply deep learning with DGF, we design a suite of new techniques (e.g., step-forwarding approach, representative data selection) to solve the problems of unbalanced labeled data and insufficient time in the training process. Further, we implement the proposed approach called FuzzGuard and equip it with the state-of-the-art DGF (e.g., AFLGo). Evaluations on 45 real vulnerabilities show that FuzzGuard boosts the fuzzing efficiency of the vanilla AFLGo up to 17.1 times.