The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Learn&Fuzz: Machine learning for input fuzzing
Open accessAbstract
Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF parser embedded in Microsoft's new Edge browser. We discuss and measure the tension between conflicting learning and fuzzing goals: learning wants to capture the structure of well-formed inputs, while fuzzing wants to break that structure in order to cover unexpected code paths and find bugs. We also present a new algorithm for this learn&fuzz challenge which uses a learnt input probability distribution to intelligently guide where to fuzz inputs.
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Cites
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Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Graves, A., & Jaitly, N. (2014) · Cited by 1,861
Automated Whitebox Fuzz Testing.
Godefroid, P., Levin, M. Y., & Molnar, D. A. (2008) · Cited by 1,095
Self-citationQuickCheck: a lightweight tool for random testing of Haskell programs
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Fuzzing: Brute Force Vulnerability Discovery
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Grammar-based whitebox fuzzing
Godefroid, P., Kiezun, A., & Levin, M. Y. (2008) · Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/1375581.1375607 · Cited by 462
Self-citationDeepFix: Fixing Common C Language Errors by Deep Learning
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Cited by
Artificial intelligence for cybersecurity: Literature review and future research directions
Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101804 · Cited by 615
(2021) · doi:10.1109/tse.2019.2946563 · Cited by 574
Zhu, X., Wen, S., Camtepe, S., & Xiang, Y. (2022) · ACM Computing Surveys · doi:10.1145/3512345 · Cited by 334
Deng, Y., Xia, C. S., Peng, H., Yang, C., & Zhang, L. (2023) · Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3597926.3598067 · Cited by 326
Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection
Wang, H., Ye, G., Tang, Z., Tan, S. H., Huang, S., Fang, D., Feng, Y., Bian, L., & Wang, Z. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3044773 · Cited by 308
Metadata
- Authors
- Godefroid, P., Peleg, H., & Singh, R.
- Venue
- 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 50-59
- OpenAlex
- W2583649498
- Cited by
- 344
- References
- 30
- FWCI
- 30.69
- Type
- conference paper
- Language
- en
- Open access
- green
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