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Integration of machine learning (ML) in 5G-based Internet of Vehicles (IoV)networks has enabled intelligent transportation and smart traffic management.Nonetheless, the security against adversarial poisoning attacks is alsoincreasingly becoming a challenging task. Specifically, Deep ReinforcementLearning (DRL) is one of the widely used ML designs in IoV applications. Thestandard ML security techniques are not effective in DRL where the algorithmlearns to solve sequential decision-making through continuous interaction withthe environment, and the environment is time-varying, dynamic, and mobile. Inthis paper, we propose a Gated Recurrent Unit (GRU)-based federated continuallearning (GFCL) anomaly detection framework against Sybil-based data poisoningattacks in IoV. The objective is to present a lightweight and scalableframework that learns and detects the illegitimate behavior without havinga-priori training dataset consisting of attack samples. We use GRU to predict afuture data sequence to analyze and detect illegitimate behavior from vehiclesin a federated learning-based distributed manner. We investigate theperformance of our framework using real-world vehicle mobility traces. Theresults demonstrate the effectiveness of our proposed solution in terms ofdifferent performance metrics.

GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV