Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing

被引:33
作者
Wang, Jinghan [1 ]
Song, Chengyu [1 ]
Yin, Heng [1 ]
机构
[1] Univ Calif Riverside, Riverside, CA 92521 USA
来源
28TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2021) | 2021年
基金
美国国家科学基金会;
关键词
D O I
10.14722/ndss.2021.24486
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Coverage metrics play an essential role in greybox fuzzing. Recent work has shown that fine-grained coverage metrics could allow a fuzzer to detect bugs that cannot be covered by traditional edge coverage. However, fine-grained coverage metrics will also select more seeds, which cannot be efficiently scheduled by existing algorithms. This work addresses this problem by introducing a new concept of multi-level coverage metric and the corresponding reinforcement-learning-based hierarchical scheduler. Evaluation of our prototype on DARPA CGC showed that our approach outperforms AFL and AFLFAST significantly: it can detect 20% more bugs, achieve higher coverage on 83 out of 180 challenges, and achieve the same coverage on 60 challenges. More importantly, it can detect the same number of bugs and achieve the same coverage faster. On FuzzBench, our approach achieves higher coverage than AFL++ (Qemu) on 10 out of 20 projects.
引用
收藏
页数:17
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