Connected vehicles (CVs), because of the external connectivity with other CVs and connected infrastructure, are vulnerable to cyberattacks that can instantly compromise the safety of the vehicle itself and other connected vehicles and roadway infrastructure. One such cyberattack is the false information attack, where an external attacker injects inaccurate information into the connected vehicles and eventually can cause catastrophic consequences by compromising safety-critical applications like the forward collision warning. The occurrence and target of such attack events can be very dynamic, making real-time and near-real-time detection challenging. Change point models, can be used for real-time anomaly detection caused by the false information attack. In this paper, we have evaluated three change point-based statistical models; Expectation Maximization, Cumulative Summation, and Bayesian Online Change Point Algorithms for cyberattack detection in the CV data. Also, data-driven artificial intelligence (AI) models, which can be used to detect known and unknown underlying patterns in the dataset, have the potential of detecting a real-time anomaly in the CV data. We have used six AI models to detect false information attacks and compared the performance for detecting the attacks with our developed change point models. Our study shows that change points models performed better in real-time false information attack detection compared to the performance of the AI models. Change point models having the advantage of no training requirements can be a feasible and computationally efficient alternative to AI models for false information attack detection in connected vehicles.

中文翻译：

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用于联网车辆的基于统计和人工智能的虚假信息网络攻击检测模型的有效性

联网车辆 (CV) 由于与其他 CV 和联网基础设施的外部连接，容易受到网络攻击，这些攻击会立即危及车辆本身以及其他联网车辆和道路基础设施的安全。其中一种网络攻击是虚假信息攻击，外部攻击者将不准确的信息注入联网车辆，最终可能通过危及安全关键应用程序（如前向碰撞警告）而导致灾难性后果。此类攻击事件的发生和目标可能非常动态，这使得实时和近实时检测具有挑战性。变化点模型，可用于虚假信息攻击引起的实时异常检测。在本文中，我们评估了三种基于变化点的统计模型；用于 CV 数据中网络攻击检测的期望最大化、累积求和和贝叶斯在线变化点算法。此外，数据驱动的人工智能 (AI) 模型可用于检测数据集中已知和未知的潜在模式，具有检测 CV 数据中实时异常的潜力。我们使用了六个 AI 模型来检测虚假信息攻击，并将检测攻击的性能与我们开发的变化点模型进行了比较。我们的研究表明，与 AI 模型的性能相比，变化点模型在实时虚假信息攻击检测方面的表现更好。具有无需训练的优势的变化点模型可以成为人工智能模型的可行且计算效率高的替代方案，用于联网车辆中的虚假信息攻击检测。