Misbehavior Detection in Vehicular Networks: An Ensemble Learning Approach

Emerging vehicle-to-everything (V2X) systems call for a diverse set of novel mechanisms to address vulnerabilities and security breaches. In this context, misbehavior detection approaches aim to detect malicious behavior of rogue V2X entities and possible attacks that may originate from them. In this paper, we introduce a data-driven ensemble framework which jointly leverages clustering and reinforcement learning to detect misbehaviors in unlabeled vehicular data. A rigorous detection assessment using an open-source dataset reveals meaningful performance trends for various attacks. In particular, while the majority of attacks can be effectively detected, detection may be curtailed for certain misbehavior types due to partly inaccurate clustering and erratic activity of the attacker over time. Performance comparison against benchmark detectors reveals the robustness of our approach in the presence of potentially inconsistent or mislabeled training data. The real-time detection capabilities of our framework are also explored in an effort to evaluate its practical feasibility in mission-critical V2X scenarios.