Uses of Active and Passive Learning in Stateful Fuzzing

Fuente: arXiv
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Auteurs principaux: Daniele, Cristian, Andarzian, Seyed Behnam, Poll, Erik
Format: Preprint
Publié: 2024
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author Daniele, Cristian
Andarzian, Seyed Behnam
Poll, Erik
author_facet Daniele, Cristian
Andarzian, Seyed Behnam
Poll, Erik
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2406_08077
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Uses of Active and Passive Learning in Stateful Fuzzing
Daniele, Cristian
Andarzian, Seyed Behnam
Poll, Erik
Software Engineering
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.
title Uses of Active and Passive Learning in Stateful Fuzzing
topic Software Engineering
url https://arxiv.org/abs/2406.08077