Enhancing Driver Identification with a Crow Search-Optimized Stacking Ensemble

Driver identification systems play a crucial role in enhancing vehicle security and delivering personalized experiences for drivers. Traditional identification methods typically use individual machine learning models, which often struggle to perform well due to their limited ability to adapt to diverse driving behaviors. In this study, we present a novel stacking ensemble framework optimized using the Crow Search Algorithm (CSA) to overcome these challenges. The CSA-optimized ensemble combines the strengths of several base models—Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbour (KNN)—with a meta-learner designed to boost both accuracy and robustness. CSA is used to fine-tune the ensemble’s hyperparameters, ensuring optimal performance. Experimental results on a driving dataset demonstrated that the proposed method significantly outperforms existing approaches in terms of identification accuracy, precision, and recall. This framework holds promise for a wide range of applications, including intelligent transportation systems and automotive cybersecurity.