Machine learning for enhancing transportation security: A comprehensive analysis of electric and flying vehicle systems

Abstract

This paper delves into the transformative role of machine learning (ML) techniques in revolutionizing the security of electric and flying vehicles (EnFVs). By exploring key domains such as predictive maintenance, cyberattack detection, and intelligent decision-making, the study uncovers pivotal insights that will shape the future of this technology.From a theoretical perspective, ML emerges as a cornerstone for fortifying EnFV safety, offering real-time threat detection, predictive maintenance capabilities, and enhanced anomaly detection. In practical terms, ML-based solutions are envisioned as instrumental in preventing cyberattacks, reducing downtime, and improving overall safety.The research contributions of this study encompass a comprehensive overview of ML applications in EnFV security, identification of challenges, and paving the way for future research directions. While acknowledging research limitations, particularly the need for real-world implementation, the study emphasizes the crucial yet underexplored ethical considerations in ML for EnFV security. Future research suggestions focus on Explainable AI techniques, real-time ML algorithms for resource-constrained environments, and privacy-preserving ML techniques, aiming for a transparent, efficient, and privacy-aware integration of ML in EnFV security. By addressing key security challenges, ML can potentially revolutionize the EnFV domain, paving the way for a future of efficient, sustainable, and connected transportation systems.