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

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Опубликовано в::arXiv.org (Dec 11, 2024), p. n/a
Главный автор: Upadhyay, Saket
Опубликовано:
Cornell University Library, arXiv.org
Предметы:
Testing time
Machine learning
Harnesses
Computer programming
Software testing
Online-ссылка:Citation/Abstract
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100 1 |a Upadhyay, Saket 
245 1 |a FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning 
260 |b Cornell University Library, arXiv.org  |c Dec 11, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Testing time 
653 |a Machine learning 
653 |a Harnesses 
653 |a Computer programming 
653 |a Software testing 
773 0 |t arXiv.org  |g (Dec 11, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3143450942/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.08100