X-GEVON- a novel explainable intelligent network to detect the multiple attacks in vanet systems

Vehicular Ad-Hoc Networks (VANETs) play a vital role in the advancement of Intelligent Transportation Systems (ITS), offering secure and efficient communication between vehicles to support smart and traffic-independent road systems. With the integration of Internet of Things (IoT) technologies, vehicles have become capable of exchanging critical information over the internet, enabling real-time decision-making. However, this rapid digitization has also introduced significant security and privacy vulnerabilities, leading to increased susceptibility to malicious attacks that may cause catastrophic consequences. To address these security concerns, this study aims to develop a real-time, lightweight, and efficient deep learning model for detecting intrusions in VANET environments. The proposed framework, named X-GEVON, combines Gated Recurrent Units (GRU) for temporal feature extraction with an Enhanced Energy Valley Optimization (EVO) algorithm for hyperparameter tuning, ensuring high detection performance with minimal computational overhead. Furthermore, Explainable Artificial Intelligence (XAI) techniques, particularly the Local Interpretable Model-Agnostic Explanations (LIME), are integrated to provide transparent and interpretable classification results. The model is trained and tested on approximately 400,000 real-time data traces, including both normal and attack scenarios, generated using simulation tools such as SUMO, OMNET + + , and Python 3.19. Experimental results demonstrate that the proposed method significantly outperforms existing deep learning techniques, achieving a detection accuracy of 99.6%, a precision of 99.2%, and a recall of 99.4%. Additionally, the use of LIME enhances the interpretability of the model by explaining its prediction logic, making it more reliable for real-world applications. In conclusion, the X-GEVON framework offers a powerful, accurate, and explainable solution for intrusion detection in VANETs, bridging the gap between high-performance security models and transparent, interpretable AI systems.