Robust DoS Attack Detection for V2X Networks Using RNN Variants with Attention

Abstract

V2X communication is a foundational technology for connected and autonomous transportation, enabling vehicles, infrastructure, and pedestrians to exchange timely and reliable information essential for collision avoidance, traffic optimisation, and road safety. Denial-of-Service (DoS) attacks pose critical threats to Vehicle-to-Everything (V2X) communication by disrupting real-time safety and mobility functions. This study presents a comparative evaluation of SimpleRNN, LSTM, and GRU models for DoS detection using a balanced, normalized dataset of 120,000 samples derived from the VeReMi Extension. All models employ a unified architecture integrating attention mechanisms and dense layers, and are trained across two temporal window sizes (5 and 10) to capture varying time dependencies. Hyperparameter optimization is conducted using random search to enhance performance and training efficiency. Models are benchmarked on classification metrics (accuracy, precision, recall, F1-score), inference latency, memory and CPU usage, and model footprint (Keras/TFLite formats). Results show that GRU achieves the highest detection performance (F1-score up to 0.972, accuracy up to 97.2%), RNN offers the smallest model size (0.09 MB) and fastest inference (¡ 1 s), while LSTM provides high precision (up to 0.970) but requires tuning to improve recall. These insights support the development of secure, lightweight, and real-time intrusion detection systems for next-generation V2X environments.