Machine Learning‐Driven Intrusion Detection Systems for Anomaly Detection in Vehicular Ad‐Hoc Networks

ABSTRACT In Intelligent Transportation Systems, Vehicular Ad‐hoc Networks (VANETs) provide real‐time vehicle‐infrastructure communication. VANETs may be attacked via denial‐of‐service, Sybil, and spoofing due to its open wireless medium and changeable topology. In dynamic vehicle contexts, signature‐based intrusion detection systems (IDSs) fail to identify novel threats, while anomaly‐based techniques have high false alarm rates and limited scalability. The Machine Learning‐based Vehicular Intrusion Detection System (ML‐VIDS) combines unsupervised clustering for zero‐day anomaly detection and supervised learning for known attack categorization. The method improves detection accuracy and edge deployment efficiency via temporal traffic analysis and feature selection. On benchmark VANET datasets, ML‐VIDS beats comparable IDS systems with 97.8% detection accuracy, 6% false positive rate, 15 ms latency, 65% lower computational overhead, and 30 W energy utilization. In next‐generation intelligent transportation systems, ML‐VIDS enables adaptive and resource‐efficient intrusion detection for VANETs to provide strong performance and real‐time security.