A systematic review of fuzzing based on machine learning techniques

TL;DR

This paper systematically reviews how machine learning techniques have been integrated into fuzzing for vulnerability detection, analyzing models, performance, and future challenges in enhancing fuzzing effectiveness.

Contribution

It provides a comprehensive analysis of machine learning-based fuzzing models, including their methodologies, evaluation, and comparison with traditional fuzzing tools, highlighting recent advancements and limitations.

Findings

Machine learning improves fuzzing performance in vulnerability discovery.

ML models show acceptable predictive capabilities for fuzzing.

Challenges remain in data imbalance and feature extraction for vulnerabilities.

Abstract

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 fuzzing techniques have many challenges, such as how to mutate input seed files, how to increase code coverage, and how to effectively bypass verification. Machine learning technology has been introduced as a new method into fuzzing test to alleviate these challenges. This paper reviews the research progress of using machine learning technology for fuzzing test in recent years, analyzes how machine learning improve the fuzz 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 six different stages in which machine learning have been used. Then this paper systematically study the machine learning based fuzzing models from selection of machine learning algorithm, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics. The results of the evaluation prove that machine learning technology has an acceptable capability of categorize predictive for fuzzing. Finally, the comparison on capability of discovering vulnerabilities between traditional fuzzing tools and machine learning based fuzzing tools is analyzed. The results depict that the introduction of machine learning technology can improve the performance of fuzzing. However, there are still some limitations, such as unbalanced training samples and difficult to extract the characteristics related to vulnerabilities.