基于深度学习的车身网络KWP2000协议漏洞挖掘  

The vulnerability mining method for KWP2000 protocol based on deep learning and fuzzing

作　　者：张成彬[1] 赵慧[2] 曹宗钰 ZHANG Chengbin;ZHAO Hui;CAO Zongyu(College of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, Jiangsu, China;National Trusted Embedded Software Engineering Technoloy Research Center, East China Normal University, Shanghai 200062, China)

机构地区：[1]盐城工学院信息工程学院,江苏盐城224051 [2]华东师范大学国家可信嵌入式软件工程技术研究中心,上海200062

出　　处：《山东大学学报（工学版）》2019年第2期17-22,共6页Journal of Shandong University（Engineering Science）

基　　金：江苏省前瞻性联合研究项目:基于物联网与深度学习的污水处理智能监控系统研究与开发(BY2016065-06)

摘　　要：为实现无需协议的任何结构知识进行网络安全漏洞检测,基于深度学习生成对抗式神经网络(generative adversarial nets, GAN),提出对车身网络关键字协议2000 (keyword protocol 2000, KWP2000)漏洞挖掘的方法。选用前向反馈网络作为生成模型,支持向量机作为判别模型。利用神经网络模型训练生成KWP2000协议数据的测试用例数据,再利用这些测试用例数据对KWP2000进行模糊测试。通过试验发现目标协议KWP2000的超长错误、编码错误等漏洞。试验研究表明,该模糊测试方法提高了效率和安全性。A kind of vehicle-onboard diagnosis Protocol standard, keyword protocol 2000(KWP2000) KWP2000, was investigated in details. KWP2000 was widely used in the automobile industry and the loophole of possible communication Protocol. We analyzed the current situations of the fuzzing, and based on this, we proposed a generative adversarial networks(GAN) by deep learning neural network for automobile body network KWP2000 protocol hole mining method. The forward feedback network was closeted as the generation model, and the support vector machine was used as the discriminant model. We used the neural network model to train the test case data of the KWP2000 protocol data, the fuzzing of KWP2000 was carried out by using these test case data. Through experiments, we found that the target protocol KWP2000 had long loopholes, coding errors and other vulnerabilities. Experimental results showed that this fuzzing method was efficient and safe.

关 键 词：协议2000 深度学习 生成对抗式网络 模糊测试 车载诊断 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]