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FastAFLGo: Toward a Directed Greybox Fuzzing

Chunlai Du1, Tong Jin1, Yanhui Guo2,*, Binghao Jia1, Bin Li3

1 School of Information Science and Technology, North China University of Technology, Beijing, 100144, China
2 Department of Computer Science, University of Illinois Springfield, Springfield, 62703, IL, USA
3 Civil Aviation Management Institute of China, Beijing, 100102, China

* Corresponding Author: Yanhui Guo. Email: email

Computers, Materials & Continua 2021, 69(3), 3845-3855. https://doi.org/10.32604/cmc.2021.017697

Abstract

While the size and complexity of software are rapidly increasing, not only is the number of vulnerabilities increasing, but their forms are diversifying. Vulnerability has become an important factor in network attack and defense. Therefore, automatic vulnerability discovery has become critical to ensure software security. Fuzzing is one of the most important methods of vulnerability discovery. It is based on the initial input, i.e., a seed, to generate mutated test cases as new inputs of a tested program in the next execution loop. By monitoring the path coverage, fuzzing can choose high-value test cases for inclusion in the new seed set and capture crashes used for triggering vulnerabilities. Although there have been remarkable achievements in terms of the number of discovered vulnerabilities, the reduction of time cost is still inadequate. This paper proposes a fast directed greybox fuzzing model, FastAFLGo. A fast convergence formula of temperature is designed, and the energy scheduling scheme can quickly determine the best seed to make the program execute toward the target basic blocks. Experimental results show that FastAFLGo can discover more vulnerabilities than the traditional fuzzing method in the same execution time.

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APA Style
Du, C., Jin, T., Guo, Y., Jia, B., Li, B. (2021). Fastaflgo: toward a directed greybox fuzzing. Computers, Materials & Continua, 69(3), 3845-3855. https://doi.org/10.32604/cmc.2021.017697
Vancouver Style
Du C, Jin T, Guo Y, Jia B, Li B. Fastaflgo: toward a directed greybox fuzzing. Comput Mater Contin. 2021;69(3):3845-3855 https://doi.org/10.32604/cmc.2021.017697
IEEE Style
C. Du, T. Jin, Y. Guo, B. Jia, and B. Li, “FastAFLGo: Toward a Directed Greybox Fuzzing,” Comput. Mater. Contin., vol. 69, no. 3, pp. 3845-3855, 2021. https://doi.org/10.32604/cmc.2021.017697



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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