A Review of Machine Learning Applications in Fuzzing
arXiv:1906.11133
Abstract
Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.
References in corpus (12)
- Sequence to Sequence Learning with Neural Networks
- Playing Atari with Deep Reinforcement Learning
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- A Critical Review of Recurrent Neural Networks for Sequence Learning
- Learning a SAT Solver from Single-Bit Supervision
- Reinforcement Learning: A Survey
- Not all bytes are equal: Neural byte sieve for fuzzing
- Survey on Models and Techniques for Root-Cause Analysis
- A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor
- Graph Neural Networks and Boolean Satisfiability
- NEUZZ: Efficient Fuzzing with Neural Program Smoothing
- Efficient Loop Navigation for Symbolic Execution