Zero Trust Architecture for Electric Transportation Systems: A Systematic Survey and Deep Learning Framework for Replay Attack Detection - Grace Muriithi; Behnaz Papari; Ali Arsalan; Laxman Timilsina; Alex Muriithi; Elutunji Buraimoh; Asif Khan; Gokhan Ozkan; Christopher Edrington; Akram Papari (IEEE Open Journal of Vehicular Technology)
Modern and autonomous hybrid electric vehicles (HEVs), as complex cyber-physical systems, represent a key innovation in the future of transportation. However, the increasing interconnectivity and reliance on digital components expose these vehicles to significant cybersecurity risks. To address these challenges, Zero Trust Architecture (ZTA) has emerged as a promising security framework. Operating on the principle of ‘never trust, always verify,’ ZTA offers a comprehensive approach to ensuring continuous trust verification in HEV systems. Despite its potential, the application of ZTA within cyber-physical vehicular systems remains underexplored, and its practical benefits and limitations are not yet fully understood by the engineering community. To bridge this gap, this article presents a detailed survey of ZTA tailored specifically to the needs of vehicular CPSs, highlighting existing technologies, security challenges, and the application of zero-trust principles in HEVs. Additionally, this work proposes a deep learning-based replay attack detection scheme for the battery management system (BMS) of HEVs. The approach leverages a deep learning model to estimate the battery's State of Charge (SoC), analyzing the Error of Estimation using the Inter-Quartile Range (IQR) technique. The detection system analyzes the Error of Estimation using the IQR technique, demonstrating a 74.25% containment ratio and detecting deviations up to 2.39 units during attack scenarios. The system maintains a balanced detection sensitivity with 25.75% detection density. While the proposed method demonstrates high effectiveness in detecting stealth replay attacks through simulation results, it faces certain limitations including computational overhead for real-time processing, dependence on high-quality training data, and potential vulnerability to adversarial attacks on the underlying deep learning model. These challenges highlight the need for careful consideration in practical implementations while opening avenues for future research.