基于深度学习的混合模糊测试方法
摘 要
随着软件系统日益复杂，模糊测试成为检测程序漏洞的重要手段。传统模糊测试方法在面对复杂输入结构和深层次逻辑时存在局限性，基于深度学习的混合模糊测试方法应运而生。该研究旨在结合深度学习与传统模糊测试的优势，通过构建神经网络模型对输入样本进行智能变异和优化选择，以提高模糊测试的有效性和效率。具体而言，利用深度学习算法分析程序执行路径特征，指导测试用例生成，并引入强化学习机制动态调整测试策略。实验结果表明，相较于传统方法，该方法能够显著提升漏洞发现率，在多个开源项目中成功挖掘出若干未知漏洞。此方法创新性地将深度学习融入模糊测试领域，不仅增强了对复杂程序结构的理解能力，还为自动化安全测试提供了新思路，具有重要的理论意义和实际应用价值。
关键词：模糊测试；深度学习；混合模糊测试
Abstract
As software systems become increasingly complex, fuzz testing has emerged as a critical approach for detecting program vulnerabilities. Traditional fuzzing methods face limitations when dealing with complex input structures and deep logical layers, leading to the development of hybrid fuzzing approaches based on deep learning. This study aims to integrate the strengths of deep learning with traditional fuzzing techniques by constructing neural network models to perform intelligent mutation and optimized selection of input samples, thereby enhancing both the effectiveness and efficiency of fuzz testing. Specifically, deep learning algorithms are employed to analyze program execution path characteristics, guiding the generation of test cases, while reinforcement learning mechanisms are introduced to dynamically adjust testing strategies. Experimental results demonstrate that this method significantly improves vulnerability detection rates compared to traditional approaches and has successfully uncovered several unknown vulnerabilities in multiple open-source projects. This innovative integration of deep learning into the field of fuzz testing not only enhances the understanding of complex program structures but also provides new insights for automated security testing, holding significant theoretical implications and practical application value.
Keywords: Fuzz Testing；Deep Learning；Hybrid Fuzz Testing
目 录
摘 要 I
Abstract II
引言 1
一、深度学习与模糊测试基础 1
（一）模糊测试原理概述 1
（二）深度学习技术简介 1
（三）混合方法的必要性 2
二、混合模糊测试框架设计 2
（一）测试目标定义 2
（二）框架架构设计 3
（三）关键技术实现 4
三、深度学习模型在模糊测试中的应用 4
（一）输入生成优化 4
（二）异常检测机制 5
（三）测试用例优先级排序 5
四、实验评估与结果分析 5
（一）实验环境搭建 5
（二）性能对比分析 6
（三）应用案例研究 6
结 论 7
致 谢 8
参考文献 9
摘 要
随着软件系统日益复杂，模糊测试成为检测程序漏洞的重要手段。传统模糊测试方法在面对复杂输入结构和深层次逻辑时存在局限性，基于深度学习的混合模糊测试方法应运而生。该研究旨在结合深度学习与传统模糊测试的优势，通过构建神经网络模型对输入样本进行智能变异和优化选择，以提高模糊测试的有效性和效率。具体而言，利用深度学习算法分析程序执行路径特征，指导测试用例生成，并引入强化学习机制动态调整测试策略。实验结果表明，相较于传统方法，该方法能够显著提升漏洞发现率，在多个开源项目中成功挖掘出若干未知漏洞。此方法创新性地将深度学习融入模糊测试领域，不仅增强了对复杂程序结构的理解能力，还为自动化安全测试提供了新思路，具有重要的理论意义和实际应用价值。
关键词：模糊测试；深度学习；混合模糊测试
Abstract
As software systems become increasingly complex, fuzz testing has emerged as a critical approach for detecting program vulnerabilities. Traditional fuzzing methods face limitations when dealing with complex input structures and deep logical layers, leading to the development of hybrid fuzzing approaches based on deep learning. This study aims to integrate the strengths of deep learning with traditional fuzzing techniques by constructing neural network models to perform intelligent mutation and optimized selection of input samples, thereby enhancing both the effectiveness and efficiency of fuzz testing. Specifically, deep learning algorithms are employed to analyze program execution path characteristics, guiding the generation of test cases, while reinforcement learning mechanisms are introduced to dynamically adjust testing strategies. Experimental results demonstrate that this method significantly improves vulnerability detection rates compared to traditional approaches and has successfully uncovered several unknown vulnerabilities in multiple open-source projects. This innovative integration of deep learning into the field of fuzz testing not only enhances the understanding of complex program structures but also provides new insights for automated security testing, holding significant theoretical implications and practical application value.
Keywords: Fuzz Testing；Deep Learning；Hybrid Fuzz Testing
目 录
摘 要 I
Abstract II
引言 1
一、深度学习与模糊测试基础 1
（一）模糊测试原理概述 1
（二）深度学习技术简介 1
（三）混合方法的必要性 2
二、混合模糊测试框架设计 2
（一）测试目标定义 2
（二）框架架构设计 3
（三）关键技术实现 4
三、深度学习模型在模糊测试中的应用 4
（一）输入生成优化 4
（二）异常检测机制 5
（三）测试用例优先级排序 5
四、实验评估与结果分析 5
（一）实验环境搭建 5
（二）性能对比分析 6
（三）应用案例研究 6
结 论 7
致 谢 8
参考文献 9
