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基于控制流序位比对的智能Fuzzing测试方法
引用本文:王颖,杨义先,钮心忻,谷利泽.基于控制流序位比对的智能Fuzzing测试方法[J].通信学报,2013,34(4):13-121.
作者姓名:王颖  杨义先  钮心忻  谷利泽
作者单位:北京邮电大学 信息安全中心,北京100876
基金项目:国家自然科学基金资助项目(61121061)
摘    要:在国际前沿技术EFS(evolutionary fuzzing system)的研究基础上,提出基于控制流序位比对算法的智能Fuzzing测试方法。根据遗传算法的内在属性演算得到基于序列比对的适应度函数,并有效地计算出需要搜索的程序逻辑空间。最后给出了该方法与2种传统Fuzzing方法的测试性能的实验结果比对,证明了该方法能够充分利用遗传算法属性中并行性进行智能地程序逻辑学习,具有逻辑覆盖面广、搜索导向性强的优点,能够提高漏洞挖掘能力。

关 键 词:智能Fuzzing  控制流  遗传算法  漏洞
收稿时间:7/4/2012 12:00:00 AM

Smart fuzzing method based on comparison algorithm of control flow sequences
WANG Ying,YANG Yi-xian,NIU Xin-xin,GU Li-ze.Smart fuzzing method based on comparison algorithm of control flow sequences[J].Journal on Communications,2013,34(4):13-121.
Authors:WANG Ying  YANG Yi-xian  NIU Xin-xin  GU Li-ze
Affiliation:Information Security Center,School of Computer,Beij ng University of Posts and Telecommunications,Beijing 100876,China
Abstract:Flowing the way introduced in the research of evolutionary fuzzing system (EFS), a smart fuzzing method was proposed based on the node comparison algorithm among the control flow sequences. Through mapping program execution flow sequences onto the control flow sequences, the isomorphism relationship between dada search space and program logic space was established. The analyzed results prove that the method is capable of mining a mass of information from group data effectively, and is able to fully utilize the parallelism of genetic algorithm to guide the fuzzing test.
Keywords:smart Fuzzing  control flow  gene algorithm  vulnerability
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