Evaluating and improving neural program-smoothing-based fuzzing-Reference-Cited by-同舟云学术

Evaluating and improving neural program-smoothing-based fuzzing

Published:2022-05-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 44th International Conference on Software Engineering
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Author:

Wu Mingyuan¹,Jiang Ling²,Xiang Jiahong²,Zhang Yuqun²,Yang Guowei³,Ma Huixin⁴,Nie Sen⁴,Wu Shi⁴,Cui Heming⁵,Zhang Lingming⁶

Affiliation:

1. Southern University of Science and Technology, Shenzhen, China and University of Hong Kong, Hong Kong, China

2. Southern University of Science and Technology, Shenzhen, China

3. The University of Queensland, Brisbane, Australia

4. Tencent Security Keen Lab, Shanghai, China

5. The University of Hong Kong, Hong Kong, China

6. University of Illinois

Funder

Guangdong Provincial Key Laboratory of Urology

National Natural Science Foundation of China

National Science Foundation

Shenzhen Peacock Plan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3510003.3510089

Reference55 articles.

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2. Marcel Böhme , Cristian Cadar , and Abhik Roychoudhury . 2020 . Fuzzing: Challenges and Reflections . IEEE Software ( 2020). Marcel Böhme, Cristian Cadar, and Abhik Roychoudhury. 2020. Fuzzing: Challenges and Reflections. IEEE Software (2020).

3. Coverage-based Greybox Fuzzing as Markov Chain

4. Charles E Brown . 1998. Coefficient of variation . In Applied multivariate statistics in geohydrology and related sciences . Springer , 155--157. Charles E Brown. 1998. Coefficient of variation. In Applied multivariate statistics in geohydrology and related sciences. Springer, 155--157.

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