Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

Abstract

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.