Explainable Machine Learning Framework for Real‐Time Multi‐Attack Threat Detection in Edge‐Enabled VANET Environments

ABSTRACT Due to the shortage of explainable, resource‐efficient solutions and the lack of unified multi‐attack detection abilities, existing vehicular ad hoc networks (VANET) security frameworks fail to meet the critical requirements of real‐time vehicular environments. Most traditional models rely heavily on centralized processing, making them unsuitable for dynamic, latency‐sensitive distributed VANET architectures. These limitations create a serious threat to the safety and reliability of vehicular communication systems. To address these challenges, this study proposes an explainable machine learning framework for real‐time multi‐attack threat detection (EXMAT) in edge‐enabled VANET environments. The framework is designed specifically for edge‐enabled VANET platforms. EXMAT combines the novel XGBoost classifier with custom‐engineered behavioral features and post hoc explainability to provide accurate decisions directly at the vehicular edge. The novelty of the model lies in its combined feature space, which fuses behavioral dynamics, communication patterns, and lightweight Boolean anomaly flags. The simulation of the model is performed under the VeReMi dataset. To strengthen the dataset for precise analysis of the threat, we synthetically extended it with complex attack patterns. Experimental results show that the proposed model achieves an overall classification accuracy of 95.78% with an almost perfect F1‐score for standard behavior samples of 99.98% and 94.36% for replay attacks. These results highlight EXMAT's ability to be applied in real‐time vehicular networks, enhancing traffic safety and security against unknown cyberattacks.