模糊测试
计算机科学
生成对抗网络
编码(集合论)
特征(语言学)
过程(计算)
模糊逻辑
生成语法
算法
人工智能
机器学习
深度学习
软件
集合(抽象数据类型)
程序设计语言
哲学
语言学
作者
Aoshuang Ye,Lina Wang,Lei Zhao,Jianpeng Ke,Wenqi Wang,Qinliang Liu
标识
DOI:10.1016/j.neucom.2021.06.082
摘要
We implement a Generative Adversarial Network (GAN) based fuzzer called RapidFuzz to generate synthetic testcase, which can precisely catch the data structure feature in a relatively shorter time than the state-of-art fuzzers. RapidFuzz provides potential seeds generated by GAN. i.e., The generated seeds with similar but different numerical distributions accelerate the mutation process. An algorithm is elaborately designed to locate the hot-points generated by GAN. The generated testcases make structural features easier to be identified, which makes the whole process faster. In our experiment, RapidFuzz considerably improves the performance of American Fuzzy Lop(AFL) in speed, coverage, and mapsize. We select 9 open-sourced programs with different highly-structured inputs to demonstrate the effectiveness of RapidFuzz. As a result, code coverage is significantly improved. For tiff2pdf and tiffdump, coverage increase exceeds over 20%. We also observe that RapidFuzz achieves the same coverage with less time than AFL. Furthermore, AFL absorbs 21% of generated seed files in tiff2pdf with an average absorption rate around 15% in other programs.
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