Vulnerability Assessment and Detection of Stealthy Sequential Cyberattacks in Hybrid Tracked Vehicles

This article presents an innovative approach to improve hybrid powertrains’ cyber-physical security for high-speed, tracked, off-road vehicles against subtle and sophisticated cyberattacks. While cyber threats against hybrid tracked vehicles (HTVs) have been acknowledged, exploring advanced cyberattacks targeting battery lifetime and energy efficiency within the powertrain remains underexplored. This article proposes a dual-pronged intelligent methodology to construct novel FDIAs targeting vehicle control decisions from the energy management system (EMS). The optimal cyberattack strategy by a minimally informed adversary is formulated as a partial observable Markov decision process (POMDP), employing deep reinforcement learning (DRL) for online learning and attacking. Simultaneously, a comprehensive reward system is devised that incorporates sniffing features to elevate the stealthiness and efficacy of the cyberattacks. This augmentation ensures that the orchestrated attack vectors remain inconspicuous to human drivers. Evaluation metrics are formulated to assess the impact and stealth characteristics of the cyberattack. Furthermore, a sliding window-based controller data monitoring scheme combining iForest and dynamic time warping (DTW) algorithms is proposed to automatically detect the stealthy FDIAs in real-time, ensuring secure control of energy-efficient powertrain systems. Through the proposed metrics and detection module, the article thoroughly examines the impact of cyberattacks on the energy consumption of the HTV. It also provides vital insights for defending vehicles that operate in austere environments against sophisticated controller attacks.