Omslagsbillede

FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning

Guardado en:
Udgivet i:arXiv.org (Dec 11, 2024), p. n/a
Hovedforfatter: Upadhyay, Saket
Udgivet:
Cornell University Library, arXiv.org
Fag:
Testing time
Machine learning
Harnesses
Computer programming
Software testing
Online adgang:Citation/Abstract
Full text outside of ProQuest
Tags: Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!
Resumen:Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning to refine fuzzing targets. By analyzing compile-time information, such as function call graphs' features, loop information, and memory operations, FuzzDistill identifies high-priority areas of the codebase that are more probable to contain vulnerabilities. I demonstrate the efficacy of my approach through experiments conducted on real-world software, demonstrating substantial reductions in testing time.
ISSN:2331-8422
Fuente:Engineering Database