Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment
Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment
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发表时间：
2018-11
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通讯作者：
G. Comert;Mizanur Rahman;Mhafuzul Islam;M. Chowdhury
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作者：
G. Comert;Mizanur Rahman;Mhafuzul Islam;M. Chowdhury
联网车辆 (CV) 系统能够识别潜在的网络攻击，因为其不同组件（例如车辆、路边基础设施和交通管理中心）之间的连接性不断增强。然而，由于此类攻击的动态行为、高计算能力要求以及训练检测模型的历史数据要求，实时检测安全威胁并为计算机视觉系统开发适当/有效的对策是一个挑战。为了应对这些挑战，统计模型，特别是变化点模型，具有实时异常检测的潜力。因此，本研究的目的是研究两种变化点模型（期望最大化 (EM) 和累积和 (CUSUM)）在 CV 环境中实时 V2I 网络攻击检测的有效性。为了证明这些模型的有效性，我们使用通过模拟从 CV 生成的基本安全消息 (BSM)，针对三种不同类型的网络攻击（拒绝服务 (DOS)、假冒和虚假信息）评估了这两个模型。数值分析结果表明，EM 和 CUSUM 可以检测这些网络攻击、DOS、冒充和虚假信息，准确率分别为 99\%、100\% 和 98\%，以及 100\%、100\% 和 98\%。
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate/effective countermeasures for a CV system because of the dynamic behavior of such attacks, high computational power requirement and a historical data requirement for training detection models. To address these challenges, statistical models, especially change point models, have potentials for real-time anomaly detections. Thus, the objective of this study is to investigate the efficacy of two change point models, Expectation Maximization (EM) and Cumulative Sum (CUSUM), for real-time V2I cyber attack detection in a CV Environment. To prove the efficacy of these models, we evaluated these two models for three different type of cyber attack, denial of service (DOS), impersonation, and false information, using basic safety messages (BSMs) generated from CVs through simulation. Results from numerical analysis revealed that EM and CUSUM could detect these cyber attacks, DOS, impersonation, and false information, with an accuracy of 99\%, 100\%, and 98\%, and 100\%, 100\% and 98\%, respectively.