论文标题
更改连接车辆环境中实时网络攻击检测的模型
Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment
论文作者
论文摘要
由于其不同组件（例如车辆，路边基础设施和交通管理中心）之间的连通性增加，因此连接的车辆（CV）系统具有潜在的网络攻击。但是，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，因此实时检测安全威胁并为简历系统开发适当或有效的对策是一个挑战。为了应对这些挑战，统计模型，尤其是变更点模型，具有实时异常检测的潜力。因此，这项研究的目的是研究两个变化点模型，预期最大化（EM）和两种形式的累积总和（CUSUM）算法（即典型和自适应）在CV环境中实时V2I网络攻击检测。为了证明这些模型的功效，我们使用通过模拟通过CVS生成的基本安全消息（BSMS）评估了三种不同类型的网络攻击，拒绝服务（DOS），模拟和虚假信息的模型。数值分析的结果表明，EM，CUSUM和自适应Cusum可以检测到这些网络攻击，DOS，模拟和虚假信息，精度分别为（99％，100％，100％）（98％，10％，100％）和（100％，98％，100％，100％）。
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 or 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 two forms of Cumulative Summation (CUSUM) algorithms (i.e., typical and adaptive), 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, CUSUM, and adaptive CUSUM could detect these cyber attacks, DOS, impersonation, and false information, with an accuracy of (99%, 100%, 100%), (98%, 10%, 100%), and (100%, 98%, 100%) respectively.
