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.

As the Metaverse progresses, its security measures must evolve to safeguard users from cyberattacks. To this end, Artificial Intelligence (AI)-powered Network Intrusion Detection Systems (NIDSs) have been implemented at the network layer to detect and respond to threats in real-time. Our survey -MetaWatch provides a comprehensive overview and analysis of relevant literature, focusing on studies that have explored NIDSs in the Metaverse. Unlike other surveys that have addressed Metaverse security issues more broadly, MetaWatch specifically provides a taxonomy of detection paradigms and defense mechanisms within Metaverse NIDS from 2021 to 2024. Additionally, MetaWatch identifies and discusses the key challenges and problems that impact the development of trustworthy, explainable, scalable, and robust Metaverse NIDS. Overall, the survey provides readers with a concise and informative summary of NIDS issues in the Metaverse and highlights open security problems worth exploring.

The Zero-trust security architecture is a paradigm shift toward resilient cyber warfare. Although Intrusion Detection Systems (IDS) have been widely adopted within military operations to detect malicious traffic and ensure instant remediation against attacks, this paper proposed an explainable adversarial mitigation approach specifically designed for zero-trust cyber warfare scenarios. It aims to provide a transparent and robust defense mechanism against adversarial attacks, enabling effective protection and accountability for increased resilience against attacks. The simulation results show the balance of security and trust within the proposed parameter protection model achieving a high F1-score of 94%, a least test loss of 0.264, and an adequate detection time of 0.34s during the prediction of attack types.