Guardians of the Grid: A Collaborative AI System for DDoS Detection in Autonomous Vehicles Infrastructure

Distributed Denial-of-Service (DDoS) attacks represent a pervasive and critical threat to autonomous vehicles, jeopardizing their operational functionality and passenger safety. The ease with which these attacks can be launched contrasts sharply with the difficulty of their detection and mitigation, necessitating advanced defensive solutions. This study proposes a novel deep-learning framework for accurate DDoS detection within automotive networks. We implement and compare multiple artificial neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, and Deep Neural Networks, enhanced with an active learning component to maximize data efficiency. The most performant model is subsequently deployed within a federated learning paradigm to facilitate collaborative, privacy-preserving training across distributed clients. The study is evaluated against three primary DDoS attack vectors: volumetric, state-exhaustion, and amplification. Experimental results on the CIC-DDoS2019 benchmark dataset demonstrate the efficacy of our approach, achieving a 99.98% accuracy in attack classification. This indicates a promising solution for real-time DDoS detection in the safety-critical context of autonomous driving.