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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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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）


Analysis of Malicious Injection Attack on CAN Data in In-Vehicle Network Based on Driving Behavior and Velocity
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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.

    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.

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丁男,梁文斌,许力,宋彩霞,谭国真.基于驾驶行为和速度的车内网CAN数据防注入攻击.软件学报,2017,28(s1):1-10

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  • 收稿日期:2017-05-15
  • 在线发布日期: 2017-12-15
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