BandFuzz: A Practical Framework for Collaborative Fuzzing with Reinforcement Learning

Wenxuan Shi, Hongwei Li, Jiahao Yu, Wenbo Guo, Xinyu Xing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, the technique of collaborative fuzzing has gained prominence as an efficient method for identifying software vulnerabilities. This paper introduces BandFuzz, a distinctive collaborative fuzzing framework designed to intelligently coordinate the use of multiple fuzzers. Unlike previous tools, our approach employs reinforcement learning to enhance both the efficiency and effectiveness of fuzz testing.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE/ACM International Workshop on Search-Based and Fuzz Testing, SBFT 2024
PublisherAssociation for Computing Machinery, Inc
Pages55-56
Number of pages2
ISBN (Electronic)9798400705625
DOIs
StatePublished - Apr 14 2024
Event17th International Workshop on Search-Based and Fuzz Testing, SBFT 2024 - Lisbon, Portugal
Duration: Apr 14 2024Apr 16 2024

Publication series

NameProceedings - 2024 IEEE/ACM International Workshop on Search-Based and Fuzz Testing, SBFT 2024

Conference

Conference17th International Workshop on Search-Based and Fuzz Testing, SBFT 2024
Country/TerritoryPortugal
CityLisbon
Period4/14/244/16/24

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Software

Fingerprint

Dive into the research topics of 'BandFuzz: A Practical Framework for Collaborative Fuzzing with Reinforcement Learning'. Together they form a unique fingerprint.

Cite this