UltraADV: An Unsupervised Deep Learning Lightweight Framework for Anomaly Detection in V2X

Designing anomaly detection systems for vehicle-to-everything (V2X) is a challenge. Deep learning has shown strong advantages in anomaly detection. However, labeling anomalies is often difficult and expensive, and deep neural networks often prioritize performances by incorporating a large number of parameters. Therefore, there is an urgent need for a lightweight anomaly detection framework which can be built with unsupervised learning methods. In this article, we propose a framework, the UltraADV, which is suitable for anomaly detection systems in V2X. In the framework, the autoencoder facilitates an unsupervised setting, while the knowledge distillation transfers the knowledge of a larger network to a smaller network. Ultimately, the compressed model can be deployed onto the resource-constrained devices for real-world applications in V2X. We evaluate the performances of multiple architectures of UltraADV on two publicly available datasets. Our best performing architecture demonstrably surpasses the benchmark in terms of performance metrics, parameter efficiency, and prediction speed.