  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 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 Sybil-based data poisoning
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 in terms of
different performance metrics.
