A TeachingLearning-Based Marine Predators Algorithm for Feature Selection in Machine Learning-Driven Misbehavior Detection in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANETs) underpin the safety-messaging layer of Intelligent Transportation Systems, but their open wireless medium leaves them exposed to insider attacks that survive cryptographic authentication. Position falsification and Sybil attacks are particularly harmful because they corrupt the Basic Safety Messages (BSMs) on which collision-avoidance and platooning applications rely. This paper proposes a roadside-unit (RSU)-resident misbehavior detection framework that couples a teaching-learning-enhanced Marine Predators Algorithm (TL-MPA) for feature selection with k-Nearest Neighbors, Support Vector Machine, and fully connected deep neural network classifiers. BSMs from the VeReMi dataset are deduplicated, merged with ground truth, cleaned with a median absolute deviation estimator, and encoded, and two consecutive BSMs from each sender are fused into a single feature vector. TL-MPA then selects the most informative kinematic and positional features, namely velocity, position coordinates, and sender-receiver range. On VeReMi, the proposed pipeline achieves binary F1 scores above 99 percent for all five position-falsification attack types and raises multiclass precision and recall to 99.59 percent and 99.49 percent, respectively, outperforming unoptimized kNN and SVM baselines and a prior consecutive-BSM method on the same dataset. Ablation results show that two-BSM fusion is the main source of this gain, while feature selection reduces input dimensionality with comparable or better detection performance. The RSU-side design keeps feature selection and inference off resource-constrained on-board units. Limitations of single-dataset synthetic evaluation and directions for cross-dataset validation and edge deployment are discussed.