MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer

Coverage-based Greybox Fuzzing (CGF) is a practical and effective solution for finding bugs and vulnerabilities in software. A key challenge of CGF is how to select conducive seeds and allocate accurate energy. To address this problem, we propose a novel many-objective optimization solution, MooFuzz...

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Bibliographic Details
Published inMathematics (Basel) Vol. 9; no. 3; p. 205
Main Authors Zhao, Xiaoqi, Qu, Haipeng, Lv, Wenjie, Li, Shuo, Xu, Jianliang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2021
Subjects
bug detection
Decision making
Energy
Energy consumption
Energy recovery
Fault diagnosis
Food science
fuzzing
Genetic algorithms
many-objective optimization
Multiple objective analysis
Mutation
Objectives
Optimization
path discovery
Risk allocation
Schedules
seed schedule
Seeds
Software
Software testing
Source code
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Summary:Coverage-based Greybox Fuzzing (CGF) is a practical and effective solution for finding bugs and vulnerabilities in software. A key challenge of CGF is how to select conducive seeds and allocate accurate energy. To address this problem, we propose a novel many-objective optimization solution, MooFuzz, which can identify different states of the seed pool and continuously gather different information about seeds to guide seed schedule and energy allocation. First, MooFuzz conducts risk marking in dangerous positions of the source code. Second, it can automatically update the collected information, including the path risk, the path frequency, and the mutation information. Next, MooFuzz classifies seed pool into three states and adopts different objectives to select seeds. Finally, we design an energy recovery mechanism to monitor energy usage in the fuzzing process and reduce energy consumption. We implement our fuzzing framework and evaluate it on seven real-world programs. The experimental results show that MooFuzz outperforms other state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, and PerfFuzz, in terms of path discovery and bug detection.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9030205