Zero-Trust Marine Cyberdefense for IoT-Based Communications: An Explainable Approach

Abstract

Integrating Explainable Artificial Intelligence (XAI) into marine cyberdefense systems can address the lack of trustworthiness and low interpretability inherent in complex black-box Network Intrusion Detection Systems (NIDS) models. XAI has emerged as a pivotal focus in achieving a zero-trust cybersecurity strategy within marine communication networks. This article presents the development of a zero-trust NIDS framework designed to detect contemporary marine cyberattacks, utilizing two modern datasets (2023 Edge-IIoTset and 2023 CICIoT). The zero-trust NIDS model achieves an optimal Matthews Correlation Coefficient (MCC) score of 97.33% and an F1-score of 99% in a multi-class experiment. The XAI approach leverages visual and quantitative XAI methods, specifically SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-agnostic Explanations (LIME) algorithms, to enhance explainability and interpretability. The research results indicate that current black-box NIDS models deployed for marine cyberdefense can be made more reliable and interpretable, thereby improving the overall cybersecurity posture of marine organizations.