Lightweight framework for misbehavior detection in internet of vehicles

The proliferation of connected vehicles in the Internet of Vehicles (IoV) ecosystem has introduced new security challenges, particularly in the context of internal network attacks. Traditional public key infrastructure (PKI) technologies are no longer sufficient to ensure secure communication within a network that experiences dynamic topology changes and high vehicle density. In response, there is a growing need for a lightweight misbehavior detection framework that offers fast computation and minimal space complexity. This paper presents a novel approach using continuous-time recurrent neural networks for detecting misbehavior in the IoV and assesses their performance against the Vehicular Reference Misbehavior (VeReMi) extension dataset. We compare two recently introduced models—the liquid time-constant (LTC) network and the closed-form continuous-time (CFC) neural network—with the established convolutional neural network-long short-term memory (CNN-LSTM) model. The results indicate that continuous-time neural networks marginally outperform CNN-LSTM on evaluation metrics. Despite LTC and CFC having significantly fewer parameters, making them less complex and more space-efficient than CNN-LSTM, the latter proves to be more time-efficient. Therefore, a careful balance between runtime cost and space complexity must be considered when deploying lightweight neural networks in practical applications.