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Ren Zezhong, Zheng Han, Zhang Jiayuan, Wang Wenjie, Feng Tao, Wang He, Zhang Yuqing. A Review of Fuzzing Techniques[J]. Journal of Computer Research and Development, 2021, 58(5): 944-963. DOI: 10.7544/issn1000-1239.2021.20201018
Citation: Ren Zezhong, Zheng Han, Zhang Jiayuan, Wang Wenjie, Feng Tao, Wang He, Zhang Yuqing. A Review of Fuzzing Techniques[J]. Journal of Computer Research and Development, 2021, 58(5): 944-963. DOI: 10.7544/issn1000-1239.2021.20201018

A Review of Fuzzing Techniques

Funds: This work was supported by the National Key Research and Development Program of China (2018YFB0804701), the National Natural Science Foundation of China (U1836210, 61762060), and the Key Research and Development Program of the Science and Technology Department of Gansu Province of China (20YF3GA016).
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  • Published Date: April 30, 2021
  • Fuzzing is a security testing technique, which is playing an increasingly important role, especially in detecting vulnerabilities. Fuzzing has experienced rapid development in recent years. A large number of new achievements have emerged, so it is necessary to summarize and analyze relevant achievements to follow fuzzing’s research frontier. Based on 4 top security conferences (IEEE S&P, USENIX Security, CCS, NDSS) about network and system security, we summarized fuzzing’s basic workflow, including preprocessing, input building, input selection, evaluation, and post-fuzzing. We discussed each link’s tasks, challenges, and the corresponding research results. We emphatically analyzed the fuzzing testing method based on coverage guidance, represented by the American Fuzzy Lop tool and its improvements. Using fuzzing testing technology in different fields will face vastly different challenges. We summarized the unique requirements and corresponding solutions for fuzzing testing in specific areas by sorting and analyzing the related literature. Mostly, we focused on the Internet of Things and the kernel security field because of their rapid development and importance. In recent years, the progress of anti-fuzzing testing technology and machine learning technology has brought challenges and opportunities to the development of fuzzing testing technology. These opportunities and challenges provide direction reference for the further research.
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