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Guan-fuzz: Argument Selection with Mean Shift Clustering for Multi-Argument Fuzzing
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Dissertation
Guan-fuzz: Argument Selection with Mean Shift Clustering for Multi-Argument Fuzzing
2022
Overview
Recently, software security issues become more and more important, and fuzz testing is an efficient tool to test software for malicious vulnerabilities. It generates a large number of randomly inputs and guild fuzz with coverage guide. For example, the well-known fuzz testing tool, American Fuzzy Lop (AFL). However, most of fuzz testing tool do not take into account the issue of multiple parameters. And, SQ-Fuzz,multi-parameter fuzz testing tool, dealt with this issue. It is based on AFL and selects parameters randomly. In this paper, we propose a new way of selecting parameters, using the same seeds to execute different parameters to obtain code coverage and then using cluster, MeanShift, to generalize the relationship between parameters. It can reduce the number of execution of similar parameters. In addition, Guan-Fuzz is based on AFL and optimizes forkserver in multi-parameter situation.The experimental results show that Guan-fuzz has 86% and 12% higher program coverage than AFL and SQ-Fuzz in average. Gu
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9798383657041
