Beetle‐Optimized Hybrid Ensemble for Multi‐Attack Classification in <scp>VANETs</scp>

ABSTRACT Smart connected, and autonomous vehicles represent a revolutionary advancement in transportation by incorporating cutting‐edge technologies like Internet of Things (IoT), Artificial Intelligence (AI), and 5G/6G wireless communication. These technologies enhance efficiency, reliability, and sustainability in modern vehicular networks. However, the rapid expansion of intelligent vehicles has also led to rising cyber risks, introducing new forms of attacks that threaten security, privacy, and safety. Even minor anomalies in vehicular units may cause severe consequences, including fatalities. To resolve these issues, this study proposes an effective Intrusion Detection System (IDS) using Siamese Gated Memory Networks. The model learns traffic behaviors and generates proximity scores, which are processed through dense feedforward layers for classifying multiple vehicular threats. To further optimize detection, a Modified Beetle Optimization (MBO) technique is integrated into the feedforward layers. The approach is trained and examined on benchmark datasets, comprising NSL‐KDD 2019, UNSW‐NB‐15, and VeReMi, using key metrics like precision, specificity, accuracy, F1 score, and recall. Recommended experimental analysis demonstrates superior performance examined with conventional techniques, achieving 0.993 accuracy, 0.991 precision, 0.99 recall, and 0.992 F1 score. The findings validate the robustness of hybrid Siamese networks with ensemble meta‐heuristic optimization in securing Intelligent Transportation Systems.