Deep Learning in Fuzzing: A Literature Survey
Author:
Affiliation:
1. China Electrical Power Research Institute,Information and Communication Department,Beijing,China
2. School of Cyber Science and Engineering Wuhan University,Wuhan,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9832023/9832028/09832143.pdf?arnumber=9832143
Reference24 articles.
1. SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective
2. GANFuzz
3. DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing
4. Montage: A neural network language model-guided javascript engine fuzzer[C];lee;29th USENIX Security Symposium USENIX Security 20,0
5. Automated conformance testing for JavaScript engines via deep compiler fuzzing
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1. Vulnerability detection through machine learning-based fuzzing: A systematic review;Computers & Security;2024-08
2. Ethchecker: a context-guided fuzzing for smart contracts;The Journal of Supercomputing;2024-03-13
3. DiffusionFuzz: Fuzzing Framework of Industrial Control Protocols Based on Denoising Diffusion Probabilistic Model;IEEE Access;2024
4. Machine Learning-Based Fuzz Testing Techniques: A Survey;IEEE Access;2024
5. A Seed Scheduling Method With a Reinforcement Learning for a Coverage Guided Fuzzing;IEEE Access;2023
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