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DEzzer: Efficient Fuzzing Mutation Scheduling Based on Differential Evolution

delete2025-12-09
delete0
PRE
AI
陈
陈锦富 (Jinfu Chen)
W
Wenjun Feng
S
Saihua Cai
X
Xingquan Mao
Z
Zhixiang Zhang
J
Jingyi Chen
Y
Yisong Liu
DOI：10.1016/j.jss.2025.112740delete
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摘要

摘要

En 中文
• 新型变异调度方法 (DEzzer): 介绍了DEzzer，一种通过定制化的差分进化策略和多种反馈信号的平衡来优化变异算子选择的变异调度方法，提高了对多样化输入空间的探索能力。 • 在模糊测试中的卓越性能: DEzzer在覆盖率方面显著优于现有的变异调度器以及AFL基准，成功地在模糊测试基准中识别出更多独特的崩溃，包括GNU Binutils套件、五个真实世界程序以及LAVA-M数据集。 • 崩溃分析与漏洞检测: 该研究包括对DEzzer识别的崩溃进行详细分析，定位了特定的漏洞位置，并展示了其有效发现先前未检测到的软件缺陷的能力。

期刊

Journal of Systems and Software 封面图
Journal of Systems and Software
IF:
4.1
论文数:
5.5K
被引数:
8.4K

机构

S
school of computer science and communication engineering
学者数:
45
论文数: 12
被引数: 0
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