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Fuzzing测试中样本优化算法的分析与改进 被引量:5

Improvement and Analysis of Sample Optimization Algorithm for Fuzzing
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摘要 Fuzzing测试是一种基于缺陷注入的自动软件测试技术。近几年来,广泛应用于软件测试、安全漏洞挖掘等领域。Peach是基于Fuzzing技术实现的跨平台测试框架,具有灵活性、可重用等特点,是Fuzzing测试的代表工具之一。Fuzzing测试要求对测试用例进行优化来提高测试效率,对Peach的样本优化工具minset的工作过程进行了分析,并对其进行了改进。实验证明,改进后的minset可以提高后续测试用例的有效性,从而提高Fuzzing的测试效率。 Fuzzing is a new automatic software testing technology based on fault-injection technique and it is widely used on software testing and vulnerability mining field.Peach is a cross-platform testing framework based on Fuzzing technology and it is a representative Fuzzing tool for its flexibility and reuse quality.In order to improve efficiency,it needs to optimize Fuzzing testing cases.This paper analyses the function process of minset,which is the sample optimization tool for Peach,and modifies it.It has proved that by modifying minset we can improve the efficiency of testing cases and,consequently,enhance the testing efficiency of Fuzzing.
作者 于璐 沈毅
出处 《计算机安全》 2011年第4期17-20,共4页 Network & Computer Security
关键词 FUZZING技术 PEACH 漏洞挖掘 测试用例 Fuzzing Peach vulnerability mining testing cases
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共引文献5

同被引文献48

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