Guang-Hong Liu's research works | University of Science and Technology of China and other places

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Vulnerability Analysis for X86 Executables Using Genetic Algorithm and Fuzzing

Article

November 2008

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23 Citations

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Fuzzing was successfully used to discover security bugs in popular programs, though released without source code. It becomes a major tool in security analysis, but needs large input space, ineffective. This paper presents a new method for the identification of vulnerabilities in executable program called GAFuzzing (Genetic Algorithm Fuzzing), which combines static and dynamic analysis to extend random Fuzzing. First, it uses static analysis to obtain the structural behavior, interface and interest region of code, then formally describes test requirement. Second, it uses genetic algorithm to intelligently direct test data generation and improve the testing objective. Unlike many software testing tools, our implementation analyzes the executables without source code directly. Our evaluation shows that GAFuzzing is superior to random Fuzzing for vulnerabilty analysis.

... Table 7 summarizes all the metaheuristics and fitness functions related to code coverage testing using metaheuristics. The used meta-heuristics are the genetic algorithm (GA) (Fraser and Arcuri 2011;Charmchi and Cami 2021;Michael et al. 2001;Bottaci 2002;Sparks et al. 2007;Liu et al. 2008;Cao et al. 2009a, b;Rauf et al. 2010;Andrews et al. 2014;Shuai et al. 2013Shuai et al. , 2015aPałka et al. 2016;Paduraru et al. 2017;Arcuri 2017;Wei et al. 2018;Zhu et al. 2018;Wang et al. 2019b), evolutionary algorithm (EA) (Harman et al. 2002;Tlili et al. 2006;Baresel and Sthamer 2003;Afshan et al. 2013; LD(N(pa, AL(pa, i)), i)) Harman et al. (2002) Evolutionary algorithm Branch distance Bottaci (2002) Genetic algorithm Relational and logical predicate Baresel and Sthamer (2003) Evolutionary algorithm Node-node oriented fitness function Evolutionary algorithm The fitness of the sequence is determined based on the closest path to the test aim ...
Reference:
A systematic literature review on software security testing using metaheuristics

Vulnerability Analysis for X86 Executables Using Genetic Algorithm and Fuzzing

Citing Article
November 2008

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Guang-Hong Liu’s research while affiliated with University of Science and Technology of China and other places

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