基于机器学习的模糊测试研究综述

doi:10.3969/j.issn.1671-1122.2023.08.001

摘要/Abstract

摘要：

模糊测试是当今比较流行的漏洞挖掘技术之一。传统的模糊测试往往需要大量人工参与，测试周期较长且测试效果依赖于专家经验。近年来，机器学习应用广泛，这为软件安全测试技术注入了新活力。一些研究工作使用机器学习技术对模糊测试过程进行优化和改进，弥补了传统模糊测试技术的诸多缺陷。文章对基于机器学习的模糊测试技术进行了全面分析。首先，总结了常见的漏洞挖掘方法、模糊测试过程与分类以及传统模糊测试技术的不足；然后，从模糊测试的测试用例生成、变异、筛选和调度等角度入手，着重介绍了机器学习方法在模糊测试技术中的应用研究，并结合机器学习和模糊测试实现其他功能的研究工作；最后，基于现有的工作分析总结了目前研究的局限性和面临的挑战，并对该领域未来的发展方向进行了展望。

关键词: 模糊测试, 漏洞挖掘, 机器学习

Abstract:

Fuzzing is one of the most popular vulnerability discovering techniques today. Traditional fuzzing often requires a lot of labor, which increases the application cycle of fuzzing. Besides, expert experience determines the effect of fuzzing. The wide application of machine learning has enabled machine learning techniques to be applied to software security testing. Many research works use machine learning to optimize the fuzzing process, making up for many defects of traditional fuzzing technology. This paper provided a review of fuzzing based on machine learning. Firstly, common vulnerability discovery methods, fuzzing process and classification, and the shortcomings of traditional fuzzing were summarized. Then, from the perspective of test case generation, mutation, screening, and scheduling of fuzzing, this paper focused on the application research of machine learning methods in fuzzing, as well as the research work on combining machine learning and fuzzing to realize other functions. Finally, based on the existing work, this paper analyzed and summarized the limitations and challenges in the current research work, and prospected the future development directions of this field.

Key words: fuzzing, vulnerability discovery, machine learning

中图分类号:

TP309

王鹃, 张冲, 龚家新, 李俊娥. 基于机器学习的模糊测试研究综述[J]. 信息网络安全, 2023, 23(8): 1-16.

WANG Juan, ZHANG Chong, GONG Jiaxin, LI Jun’e. Review of Fuzzing Based on Machine Learning[J]. Netinfo Security, 2023, 23(8): 1-16.

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已有工作		发表时间	测试目标类型	模型输入	学习模型	模型功能
文件解析	文献[26]方法	2017	PDF文件解析工具	文件字符流	Char-RNN	生成PDF 文件
	文献[27]方法	2021	文件解析工具	文件字符流	RNN(LSTM)	学习文件结构并生成新文件
	文献[28]方法	2018	Web 浏览器	One-hot 编码向量	LSTM/GRU	生成HTML文件
	文献[29]方法	2018	文件解析工具	文件文本	Seq2Seq	生成多种格式的文件
	文献[30]方法	2019	PDF文件解析工具	指令序列	RNN	生成PDF页面指令流
	文献[31]方法	2019	PDF文件解析工具	PDF文件	RNN、Seq2Seq	生成PDF 文件
协议解析	文献[32]方法	2018	专有网络协议	网络数据包	LSTM、Seq2Seq	生成网络协议数据包
	文献[33]方法	2019	网络协议	网络数据包	Seq2seq-attention	解析协议规范
	文献[34]方法	2020	Modbus TCP	Modbus TCP网络层数据包	RNN	学习协议数据单元的语义
	文献[35]方法	2019	工业协议解析工具	协议数据包	Seq2Seq、LSTM	生成虚假可信的协议消息
	文献[36]方法	2018	工业协议解析工具	协议数据包	GAN(LSTM/CNN)	生成虚假可信的协议消息
代码解析	文献[37]方法	2018	编译器	程序代码	LSTM	推断代码结构
	文献[38]方法	2019	C编译器	C源程序	Seq2Seq	生成C 代码
	文献[39]方法	2020	JavaScript 引擎	AST序列	LSTM	生成JavaScript 程序
	文献[40]方法	2021	JavaScript 引擎	JS源代码中单词的统计向量	GPT-2	生成JavaScript 程序
其他	文献[41]方法	2017	移动应用程序	自定义的行为模型向量表示	RNN、Word2Vec	生成上下文相关文本
	文献[42]方法	2019	移动应用程序	GUI上下文的向量表示	LSTM	生成GUI行为输入
	文献[43]方法	2019	CPS	—	LSTM/SVR	指导生成CPS测试用例

已有工作	发表时间	测试目标类型	模型输入	学习模型	模型功能
文献[44]方法	2017	文件解析工具	文件字节序列	Seq2Seq、LSTM	预测种子变异位置
文献[45]方法	2019	二进制程序	待测程序输入和输出测试输入字节序列	NN	近似模拟目标程序的逻辑分支，指导模糊器的种子突变
文献[46]方法	2020			MTNN
文献[47]方法	2022			NN
文献[48]方法	2018	二进制程序	测试程序的输入	MAB	预测变异算子概率分布
文献[49]方法	2018	二进制程序	文件字节序列	DQN	优化变异选择策略
文献[50]方法	2018	—	包含状态、操作和奖励信息的向量	Q-Learning	指导生成高回报的输入
文献[51]方法	2022	—	测试程序的输入	UCB1-Tuned	预测Havoc堆叠次数和变异算子

已有工作		发表时间	模型输入	学习模型	模型功能
种子调度	文献[52]方法	2020	函数ACFG	Bi-LSTM	预测代码块的脆弱性
	文献[53]方法	2020	函数ACFG	图嵌入网络	预测脆弱性代码位置
	文献[54]方法	2019	指令字节码组成的执行路径	LSTM	预测路径脆弱性
	文献[55]方法	2020	种子特征向量和对应标签	有监督机器学习	预测测试用例的模糊测试效果
	文献[56]方法	2021	种子集合	UCB1	实现多级覆盖度的分层调度
	文献[57]方法	2021	任务集合和种子集合	MAB	进行种子选择
测试用例筛选	文献[58]方法	2018	AST	PCFG、Markov	辅助进化算法中的适应度函数计算
	文献[59]方法	2020	HPC事件集	MLP	预测输入是否能触发异常
	文献[60]方法	2017	AFL生成测试用例和执行结果	DL	分析变异得到的测试用例是否会到达新状态
	文献[61]方法	2018	AFL生成的测试用例	NN	分析不同类型软件的变异模式
	文献[62]方法	2019	包含状态、操作和奖励信息的向量	NN	指导生成高回报的输入
	文献[63]方法	2020	待测程序输入和可访问性	CNN	预测测试用例的目标可达性

已有研究工作	发表时间	学习模型	模型功能
文献[64]方法	2018	ML	对模糊测试得到的请求和响应进行分析识别
文献[65]方法	2017	SVM	识别崩溃的可利用性，确定要检查崩溃的优先级
文献[66]方法	2018	n-gram	识别崩溃的可利用性，确定要检查崩溃的优先级
文献[67]方法	2017	ML	静态评估软件可开发性
文献[68]方法	2017	NLP	从漏洞描述信息中提取脆弱性信息