A systematic review of fuzzing based on machine learning techniques

• Published in: PloS one Vol. 15; no. 8; p. e0237749
• Main Authors: Wang, Yan, Jia, Peng, Liu, Luping, Huang, Cheng, Liu, Zhonglin
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 18.08.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Computer and Information Sciences; Computer security; Cybersecurity; Data encryption; Data security; Deep learning; Engineering and Technology; Engineering research; Fuzzy logic; Learning; Learning algorithms; Machine learning; Methods; Mutation; Performance enhancement; Physical Sciences; Research and Analysis Methods; Security; Security systems; Software; Systematic review; Technology; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0237749

Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzz testing faces many challenges, such as how to mutate input seed files, how to increase code coverage, and how to bypass the format verification effectively. Therefore machine learning techniques have been introduced as a new method into fuzz testing to alleviate these challenges. This paper reviews the research progress of using machine learning techniques for fuzz testing in recent years, analyzes how machine learning improves the fuzzing process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies five different stages in which machine learning has been used. Then this paper systematically studies machine learning-based fuzzing models from five dimensions of selection of machine learning algorithms, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Secondly, this paper assesses the performance of the machine learning techniques in existing research for fuzz testing. The results of the evaluation prove that machine learning techniques have an acceptable capability of prediction for fuzzing. Finally, the capability of discovering vulnerabilities both traditional fuzzers and machine learning-based fuzzers is analyzed. The results depict that the introduction of machine learning techniques can improve the performance of fuzzing. We hope to provide researchers with a systematic and more in-depth understanding of fuzzing based on machine learning techniques and provide some references for this field through analysis and summarization of multiple dimensions.