Deep Learning-Driven Cyber Attack Detection Framework in DC Shipboard Microgrids System for Enhancing Maritime Transportation Security

Enhancing cybersecurity in DC shipboard microgrid (SMG) systems is crucial for maintaining the resilience of energy operation in maritime transportation systems (MTS). However, increasing cyber threats pose significant challenges to deploying resilient technologies in intelligent DC SMGs. These intricate cyber-physical systems, comprising power electronics, distributed generation units, sensors, and monitoring technologies, are managed remotely, making them vulnerable to attacks that jeopardize their stability and security. Existing solutions often require additional support due to low detection and high false alarm rates, primarily from manual analysis. To address these issues, this study presents a sophisticated deep learning-driven cyber-attack detection and identification model designed to effectively enhance the security of DC SMGs in MTS. The proposed framework leverages Long Short-Term Memory (LSTM) with variational autoencoder (VAE) architectures for deep feature extraction (DFE) under attack scenarios. VAE schemes automatically uncover hidden patterns within the DC SMG network, and their outputs are utilized by deep learning (DL) schemes for accurate attack detection. The proposed model incorporates a deep artificial neural network (ANN)-based encoder-decoder scheme to identify attacks in DC SMGs, contributing to an efficient operation. Additionally, DL-based LSTM-VAE and ANN are meticulously designed with appropriate hyperparameters to counter cyber threats. The data-driven DL method achieved the testing accuracy of 99.81%, outperforming various state-of-the-art (SOTA) DL and machine learning (ML) methods. Extensive testing scenarios have been conducted to demonstrate the performance and robustness of the proposed DL methods under different levels of attacks, ensuring their efficacy in enhancing the cybersecurity of DC shipboard microgrids.