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清华大学学报(自然科学版)  2016, Vol. 65 Issue (5): 478-483    DOI: 10.16511/j.cnki.qhdxxb.2016.25.004
  信息安全 本期目录 | 过刊浏览 | 高级检索 |
Fuzzing过程中的若干优化方法
马金鑫1, 张涛1, 李舟军2, 张江霄3
1. 中国信息安全测评中心, 北京 100085;
2. 北京航空航天大学 计算机学院, 北京 100191;
3. 邢台学院 数学与信息技术学院, 邢台 054001
Improved fuzzy analysis methods
MA Jinxin1, ZHANG Tao1, LI Zhoujun2, ZHANG Jiangxiao3
1. China Information Technology Security Evaluation Center, Beijing 100085, China;
2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
3. Mathematics and Information Technology Institute, Xingtai University, Xingtai 054001, China
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摘要 在软件漏洞挖掘领域, Fuzzing测试是使用最广泛、最有效的方法之一。传统Fuzzing测试方法存在工作效率低、盲目性强等不足。该文提出一种样本集精简算法和一种加权的测试时间模型, 能够在保证代码覆盖率不变的情况下减少测试样本的数量, 同时使优质的样本得到更多的测试时间片; 设计了一种基于污点传播的异常分析方法, 可评估异常信息的危害程度, 有助于提高漏洞分析的效率。实验结果表明: 与Peach实验进行对比, 该文提出的方法有效地改进了传统的Fuzzing测试方法。
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马金鑫
张涛
李舟军
张江霄
关键词 模糊测试精简集漏洞分析    
Abstract:Fuzzing testing is one of the most widely used and most effective methods for vulnerability detection. However, the traditional fuzzy analysis method is inefficient and works blindly. This paper describes a refining method that reduces the test sample size with the same code coverage. A weighted testing time model is used to give the better sample more time. A taint based exception analysis method is used to evaluate the severity of exceptions and to improve the vulnerability analysis efficiency. Comparisons with Peach show that this method improves the traditional fuzzy analysis method.
Key wordsFuzzing    refining set    vulnerability analysis
收稿日期: 2016-01-22      出版日期: 2016-05-19
ZTFLH:  TP311.1  
引用本文:   
马金鑫, 张涛, 李舟军, 张江霄. Fuzzing过程中的若干优化方法[J]. 清华大学学报(自然科学版), 2016, 65(5): 478-483.
MA Jinxin, ZHANG Tao, LI Zhoujun, ZHANG Jiangxiao. Improved fuzzy analysis methods. Journal of Tsinghua University(Science and Technology), 2016, 65(5): 478-483.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.25.004  或          http://jst.tsinghuajournals.com/CN/Y2016/V65/I5/478
  图1 精简集算法
  表1 异常分类
  图2 系统结构图
  表2 精简集处理的实验数据
  表3 动态样本策略调整选取的实验数据
  表4 异常分析与分类实验数据
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