Fuzz testing for binary program based on genetic algorithm

Long Long Jiao, Sen Lin Luo, Wang Tong Liu, Li Min Pan, Ji Zhang*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

A genetic algorithm-based fuzz testing method for binary program was proposed aiming at the low code coverage problem caused by high execution path repetition rate of the test data generated from mutation in binary program fuzz testing. The method transformed test data to individuals in genetic algorithm. Quick Emulator was used to instrument a binary program for extracting program execution path. The evolution process in genetic algorithm was guided by an execution-path-based fitness function, so that the generated test data could cover more program execution paths. Experimental results show that the average code coverage of the method is 25.4% higher than fuzzing tool American Fuzzy Lop (AFL) within the same time. The method can detect all crashes in vulnerability detection experiment and the efficiency is at least 10% higher than AFL. The method is helpful for improving the efficiency of fuzz testing.

源语言英语
页(从-至)1014-1019
页数6
期刊Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science)
52
5
DOI
出版状态已出版 - 5月 2018

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