TY - GEN
T1 - BandFuzz
T2 - 17th International Workshop on Search-Based and Fuzz Testing, SBFT 2024
AU - Shi, Wenxuan
AU - Li, Hongwei
AU - Yu, Jiahao
AU - Guo, Wenbo
AU - Xing, Xinyu
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/4/14
Y1 - 2024/4/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85205805583&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85205805583&partnerID=8YFLogxK
U2 - 10.1145/3643659.3648563
DO - 10.1145/3643659.3648563
M3 - Conference contribution
AN - SCOPUS:85205805583
T3 - Proceedings - 2024 IEEE/ACM International Workshop on Search-Based and Fuzz Testing, SBFT 2024
SP - 55
EP - 56
BT - Proceedings - 2024 IEEE/ACM International Workshop on Search-Based and Fuzz Testing, SBFT 2024
PB - Association for Computing Machinery, Inc
Y2 - 14 April 2024 through 16 April 2024
ER -