VADGAN: An Unsupervised GAN Framework for Enhanced Anomaly Detection in Connected and Autonomous Vehicles

Abstract

The utilization of Connected and Autonomous Vehicles (CAVs) is on the rise, driven by their ability to provide vehicular services such as enhancing vehicle safety, aiding in intelligent decision-making, and ensuring continuous operation. CAVs achieve their objectives by employing wireless Vehicle-to-Everything (V2X) communication within Intelligent Transportation Systems (ITS) to establish connections with vehicles within the same network and roadside units. However, it has been observed that certain vehicles violate network constraints by transmitting erroneous messages, resulting in abnormal behaviour. Consequently, there is a growing need for a system that can verify the accuracy of information broadcast by each vehicle regarding its vehicle coordinates (along with relevant data depending on the application) at designated frequencies and under authorized pseudo-identities. Addressing the limitations faced by prior generative AI model applications, such as Variational Autoencoders (VAEs), this paper presents an unsupervised anomaly detection framework using Generative Adversarial Networks (GANs) optimized for CAVs. Our framework tested across LSTM, RNN, and GRU architectures shows superior performance with LSTM, focusing on vehicle dynamics–position, speed, acceleration, and heading–to effectively identify 11 specific attack types, marking a significant advancement in anomaly detection for CAVs.