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Uses of Active and Passive Learning in Stateful Fuzzing

Year: 2024

Type: preprint

Abstract: This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software over the past decade, but stateful systems are still difficult to fuzz. Passive and active techniques can help in a variety of ways: to compare and benchmark different fuzzers, to discover differences between various implementations of the same protocol, and to improve fuzzers.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2406.08077

Authors: Daniele, Cristian, Andarzian, Seyed Behnam, Poll, Erik

Institution: Radboud University Nijmegen

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Award: An Internet of Secure Things - INTERSECT

OpenAlex ID: W4399657476

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