An Intelligent Intrusion Detection System for VANETs Using Adaptive Fusion Models

Abstract

Vehicular Ad Hoc Networks (VANETs) play a vital role in the development of Cyber-Physical Systems (CPS) to enable real-time communication for improving road safety and traffic efficiency. Due to the VANETs' decentralized and dynamic nature, they are prone to various types of cyber-attacks, including intrusion, spoofing, and denial-of-service (DoS) attacks. This article presents an Adaptive Fusion Intrusion Detection Model (AFIDM), a multi-level framework that uses machine learning techniques, such as Random Forest, XGBoost, Decision Trees, and K-Nearest Neighbor (KNN), to deal with such vulnerabilities. AFIDM also employs a dynamic weight adjusting mechanism and an adaptive feedback loop to adapt to the evolving threats and achieve better detection accuracy. AFIDM achieved 98.7% accuracy, 96.5% precision, and recall of 95.8% on the VeReMi dataset used for training and validation and outperformed other baseline models. With low latency and scalability, the proposed model presents a robust solution for real-time intrusion detection in VANETs for the secure and efficient operation of intelligent transportation systems.