MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer

Coverage-based Greybox Fuzzing (CGF) is a practical and effective solution for finding bugs vulnerabilities in software. A key challenge of CGF how to select conducive seeds allocate accurate energy. To address this problem, we propose novel many-objective optimization solution, MooFuzz, which can identify different states the seed pool continuously gather information about guide schedule energy allocation. First, MooFuzz conducts risk marking dangerous positions source code. Second, it automatically update collected information, including path risk, frequency, mutation information. Next, classifies into three adopts objectives seeds. Finally, design an recovery mechanism monitor usage fuzzing process reduce consumption. We implement our framework evaluate on seven real-world programs. The experimental results show that outperforms other state-of-the-art fuzzers, AFL, AFLFast, FairFuzz, PerfFuzz, terms discovery bug detection.