Detection and Defense Against False Data Injection Attacks for Secure Energy Management in Hybrid Electric Ships
Reliable battery state awareness is essential for energy management and power allocation in hybrid electric ships. However, battery management systems are increasingly exposed to False Data Injection Attacks (FDIAs) in intelligent connected environments, which can distort State of Charge (SOC) estimation and compromise the operational reliability of shipboard power systems. To address this challenge, this paper proposes a closed-loop “Modeling-Detection-Defense” framework for secure SOC estimation in marine cyber-physical energy systems. First, a stealthy FDIA model is developed based on battery dynamics and physical consistency constraints. Second, a hybrid detection method combining unsupervised and supervised learning is proposed to identify attacks. Finally, a long short-term memory network is employed to reconstruct compromised measurements and provide reliable SOC information for continuous energy management. Experimental results demonstrate that the proposed framework mitigates SOC estimation deviations caused by FDIAs. In addition, it effectively reduces power allocation errors and energy losses, thereby improving the cyber-resilience, operational reliability, and energy efficiency of hybrid ship power systems.