Development of Hybrid Explainable Artificial Intelligence With Swin Vision Transformer Intrusion Detection for Securing <scp>VANETs</scp> From Attacks

ABSTRACT Vehicular Ad‐hoc Networks (VANETs) are a cornerstone of Intelligent Transportation Systems (ITS), enabling efficient vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication. However, their open and dynamic nature makes them highly susceptible to security threats such as Distributed Denial of Service (DDoS) attacks and the injection of false data by malicious nodes. Existing security mechanisms often fall short in addressing these challenges due to the real‐time and mobile characteristics of VANETs. This paper proposes a Hybrid Explainable Artificial Intelligence (XAI) framework integrated with a Swin Vision Transformer for robust intrusion detection in VANET environments. The proposed model leverages the Swin Transformer's hierarchical feature extraction capabilities and the interpretability of XAI to accurately classify network nodes based on behavioral and transmission characteristics. Key features such as packet transmission duration, communication regularity, and node status are analyzed to detect anomalies and differentiate between benign and malicious nodes. The inclusion of explainability allows for transparent decision‐making, facilitating trust and understanding in critical automotive applications. Simulation results validate the model's effectiveness in detecting a wide range of attack vectors while maintaining high accuracy and low false‐positive rates. This study contributes to the development of adaptive, intelligent, and trustworthy security solutions for next‐generation vehicular networks operating in complex urban traffic scenarios.