The integration of ML in 5G-based Internet of Vehicles (IoV) networks has enabled intelligent transportation and smart traffic management. Nonetheless, the security against adversarial attacks is also increasingly becoming a challenging task. Specifically, Deep Reinforcement Learning (DRL) is one of the widely used ML designs in IoV applications. The standard ML security techniques are not effective in DRL where the algorithm learns to solve sequential decision-making through continuous interaction with the environment, and the environment is time-varying, dynamic, and mobile. In this paper, we propose a Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly detection framework against adversarial attacks in IoV. The objective is to present a lightweight and scalable framework that learns and detects the illegitimate behavior without having a-priori training dataset consisting of attack samples. We use GRU to predict a future data sequence to analyze and detect illegitimate behavior from vehicles in a federated learning-based distributed manner. We investigate the performance of our framework using real-world vehicle mobility traces. The results demonstrate the effectiveness of our proposed solution for different performance metrics.

翻译：将ML纳入基于5G的车辆互联网(IoV)网络,使得智能交通和智能交通管理成为了智能交通和智能交通管理。然而,防止对抗性攻击的安全也日益成为一项具有挑战性的任务。具体地说,深强化学习(DRL)是IoV应用中广泛使用的ML设计之一。标准ML安全技术在DRL中并不有效,因为算法学会通过与环境的持续互动解决顺序决策,而环境是分时间、动态和移动的。在本文中,我们提议建立一个基于Ged Compater的经常性单位(GRU)的联结式持续学习(GFCL)异常探测框架,以对付IoV中的对抗性攻击。目标是提出一个轻量度和可扩展的框架,在没有攻击性样品的优先培训数据集的情况下学习和检测非法行为。我们使用GRU来预测未来数据序列,以便用基于加速学习的分布方式分析和检测来自车辆的非法行为。我们用真实世界车辆移动痕迹来调查我们框架的运作情况。结果表明我们提议的解决方案对于不同性表现的有效性。