A Review of Machine Learning Applications in Fuzzing

TL;DR

This paper surveys how machine learning techniques are being applied to enhance fuzzing, highlighting successes, challenges, and future research directions in this evolving field.

Contribution

It provides a comprehensive overview of current ML applications in fuzzing, identifying key successes and outlining future research challenges.

Findings

ML improves fuzzing efficiency and effectiveness

Successful ML-based fuzzing tools have been developed

Challenges include data quality and generalization issues

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.