The growing reliance on the Internet of Vehicles (IoV) has intensified the need for robust Intrusion Detection Systems (IDS), as recent studies reveal that over 60% of vehicular network breaches result from DoS and spoofing attacks, contributing to annual losses exceeding 1. 2 billion globally. Despite advancements, conventional IDS frameworks still suffer from limited interpretability, high false alarm rates, and inadequate adaptability to evolving attack patterns. To address these challenges, this research introduces a novel eXplainable Artificial Intelligence (XAI) -based IDS framework utilizing the CICIoV-2024 dataset, which comprehensively represents real-time vehicular communication including Benign, DoS, and Spoofing traffic. The data undergoes rigorous preprocessing to remove noise and standardize features. A hybrid Fuzzy Probability Clustering with SHapley Additive Explanations (FPC-SHAP) is employed for granular feature extraction by estimating uncertainty and relevance simultaneously. Subsequently, DeepLIFT-Autoencoder (DeepLIFT-AE) is applied for feature ranking, capturing both low- and high-level semantic patterns. For classification, a Local Interpretable Model-agnostic Explanations-based Globally Randomized Ensemble (LIME-GRE) model is implemented, enabling highly interpretable decision boundaries across the three attack classes. The proposed XAI-IDS method achieved excellent performance in the evaluation, with an Accuracy of 99. 966% and a Precision of 99. 987%. It also demonstrated strong results in Recall at 99. 786% and an F1 Score of 99. 886%, indicating highly effective classification, which are increased over state of art methodologies.
Supriya et al. (Sun,) studied this question.