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| 基于驾驶行为和速度的车内网CAN数据防注入攻击 |
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丁男1,2,3, 梁文斌1,2,3, 许力3, 宋彩霞1,2, 谭国真1,2
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1.大连理工大学 计算机科学与技术学院, 辽宁 大连 116023;2.辽宁省物联网与协同感知工程技术研究中心, 辽宁 大连 116023;3.软件架构国家重点实验室(东软集团股份有限公司), 辽宁 沈阳 110179
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| 摘要: |
| 由于车内网的开放性以及协议缺陷，其总线中数据的安全性及有效性分析是目前亟待解决的问题.利用车内CAN总线网络协议中车辆速度以及刹车油门等驾驶行为信息，提出了针对车内网CAN网络数据的防注入攻击模型.首先，基于攻击模型的分析与注入攻击特点，构建了基于驾驶行为-速度的结构模型.其次，基于该模型，利用朴素贝叶斯网络分类器，提出了面向车内网CAN数据防注入攻击分析模型，从而对接收到的车内网CAN协议中车辆行驶速度进行了有效性分析.最后通过实验仿真与验证，其结果表明，该方法能够有效地提高数据质量分析准确度. |
| 关键词: 车内网 CAN总线 贝叶斯网络 数据注入 攻击模型 |
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| 基金项目:国家自然科学基金（61471084）；软件架构国家重点实验室开放课题基金（SKLSAOP1602）；国家高技术研究发展计划（863）（2012AA111902） |
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| Analysis of Malicious Injection Attack on CAN Data in In-Vehicle Network Based on Driving Behavior and Velocity |
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DING Nan1,2,3, LIANG Wen-Bin1,2,3, XU Li3, SONG Cai-Xia1,2, TAN Guo-Zhen1,2
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1.School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China;2.Liaoning Engineering Technology Research Center of IoT and Cooperative Sensing, Dalian University of Technology, Dalian 116023, China;3.State Key Laboratory of Software Architecture(Neusoft Corporation), Shenyang 110179, China
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| Abstract: |
| Because of the opening of In-vehicle network, there are several important problems to be dealt with, such as the security and validity of data. Firstly, the article builds a construct model based on driving behavior and speed. Secondly, it makes an analysis of preventing data injection by using the construct model above and the naive Bayesian network classifier, so as to take effective measures to guarantee the vehicle security. In the end, an experimental simulation is carried out to prove that the proposed method can effectively improve the accuracy of data quality analysis and lower the false rate as well. |
| Key words: in-vehicle network CAN bus Bayesian network data injection attack model |
