Cupid : Automatic Fuzzer Selection for Collaborative Fuzzing
Emre Güler, Philipp Görz, Elia Geretto, Andrea Jemmett, Sebastian Österlund, Herbert Bos, Cristiano Giuffrida, Thorsten Holz
Abstract
Combining the strengths of individual fuzzing methods is an appealing idea to find software faults more efficiently, especially when the computing budget is limited. In prior work, EnFuzz introduced the idea of ensemble fuzzing and devised three heuristics to classify properties of fuzzers in terms of diversity. Based on these heuristics, the authors manually picked a combination of different fuzzers that collaborate.
Topics & Concepts
Fuzz testingComputer scienceHeuristicsMachine learningSelection (genetic algorithm)SoftwareArtificial intelligenceProgramming languageOperating systemSoftware Testing and Debugging TechniquesAdvanced Malware Detection TechniquesSoftware Engineering Research