Artificial Intelligence in Maritime Cybersecurity: A Systematic Review of AI-Driven Threat Detection and Risk Mitigation Strategies
Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, S. Sokołowska, Polina Kozlovska, Rafał Zwolak
Abstract
The maritime industry is undergoing a digital transformation, integrating automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and safety. However, this technological evolution has also increased cybersecurity vulnerabilities, exposing vessels, ports, and maritime communication networks to sophisticated cyber threats. This systematic review, conducted following the PRISMA guidelines, examines the current landscape of AI-driven cybersecurity solutions in maritime environments. By analyzing peer-reviewed studies and industry reports, this review identifies key AI methodologies, including machine-learning-based intrusion detection systems, anomaly detection mechanisms, predictive threat modeling, and AI-enhanced zero-trust architectures. This study assesses the effectiveness of these techniques in mitigating cyber risks, explores their implementation challenges, and highlights existing research gaps. The findings indicate that AI-powered solutions significantly enhance real-time threat detection and response capabilities in maritime networks, yet issues such as data scarcity, regulatory constraints, and adversarial attacks on AI models remain unresolved. Future research directions should focus on integrating AI with blockchain, federated learning, and quantum cryptographic techniques to strengthen maritime cybersecurity frameworks.