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Fuzzing is widely used for software vulnerability detection. There arevarious kinds of fuzzers with different fuzzing strategies, and most of themperform well on their targets. However, in industry practice and empiricalstudy, the performance and generalization ability of those well-designedfuzzing strategies are challenged by the complexity and diversity of real-worldapplications.  In this paper, inspired by the idea of ensemble learning, we first propose anensemble fuzzing approach EnFuzz, that integrates multiple fuzzing strategiesto obtain better performance and generalization ability than that of anyconstituent fuzzer alone. First, we define the diversity of the base fuzzersand choose those most recent and well-designed fuzzers as base fuzzers. Then,EnFuzz ensembles those base fuzzers with seed synchronization and resultintegration mechanisms. For evaluation, we implement EnFuzz , a prototypebasing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), andtest them on Google's fuzzing test suite, which consists of widely usedreal-world applications. The 24-hour experiment indicates that, with the sameresources usage, these four base fuzzers perform variously on differentapplications, while EnFuzz shows better generalization ability and alwaysoutperforms others in terms of path coverage, branch coverage and crashdiscovery. Even compared with the best cases of AFL, AFLFast, AFLGo andFairFuzz, EnFuzz discovers 26.8%, 117%, 38.8% and 39.5% more unique crashes,executes 9.16%, 39.2%, 19.9% and 20.0% more paths and covers 5.96%, 12.0%,21.4% and 11.1% more branches respectively.

EnFuzz: Ensemble Fuzzing with Seed Synchronization among Diverse Fuzzers