Learning Seed-Adaptive Mutation Strategies for Greybox Fuzzing

Myungho Lee, Sooyoung Cha, Hakjoo Oh

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

3 Citations (Scopus)

Abstract

In this paper, we present a technique for learning seed-adaptive mutation strategies for fuzzers. The performance of mutation-based fuzzers highly depends on the mutation strategy that specifies the probability distribution of selecting mutation methods. As a result, developing an effective mutation strategy has received much attention recently, and program-adaptive techniques, which observe the behavior of the target program to learn the optimized mutation strategy per program, have become a trending approach to achieve better performance. They, however, still have a major limitation; they disregard the impacts of different characteristics of seed inputs which can lead to explore deeper program locations. To address this limitation, we present SEAMFUZZ, a novel fuzzing technique that automatically captures the characteristics of individual seed inputs and applies different mutation strategies for different seed inputs. By capturing the syntactic and semantic similarities between seed inputs, SEAMFUZZ clusters them into proper groups and learns effective mutation strategies tailored for each seed cluster by using the customized Thompson sampling algorithm. Experimental results show that SEAMFUZZ improves both the path-discovering and bug-finding abilities of state-of-the-art fuzzers on real-world programs.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/ACM 45th International Conference on Software Engineering, ICSE 2023
PublisherIEEE Computer Society
Pages384-396
Number of pages13
ISBN (Electronic)9781665457019
DOIs
Publication statusPublished - 2023
Event45th IEEE/ACM International Conference on Software Engineering, ICSE 2023 - Melbourne, Australia
Duration: 2023 May 152023 May 16

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference45th IEEE/ACM International Conference on Software Engineering, ICSE 2023
Country/TerritoryAustralia
CityMelbourne
Period23/5/1523/5/16

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Fuzzing
  • Software Testing

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Learning Seed-Adaptive Mutation Strategies for Greybox Fuzzing'. Together they form a unique fingerprint.

Cite this