Application of Artificial Intelligence Technology in Vulnerability Analysis of Intelligent Ship Network

Abstract

AbstractThe improvement in transportation efficiency, security, safety, and environmental effects may be possible due to the impending advent of autonomous ships. Automatic situational awareness, risk detection, and intelligent decision-making are the key features of the intelligent ship network, differentiating it from conventional ships. There is an immediate need to implement a system for marine information management and network security due to the growing importance of this field, which poses a risk to national and societal stability due to factors, such as the diversity and complexity of marine information types, the challenges associated with data collection, and other similar factors. By recognizing different vulnerabilities and through research cases of the ship systems and Artificial Intelligence (AI) technologies, this paper presents Adaptive Fuzzy Logic-assisted Vulnerability Analysis of Intelligent Ship Networks (AFL-VA-ISN) in various cyberattack scenarios for autonomous ship intrusion detection and information management. Fuzzy logic has been combined with AI, providing a framework for handling uncertainty and imprecision in intelligent ship networks and effective decision-making. This work presents a method for detecting anomalies in risk data based on the collaborative control structure of the Ship Information System. Maintaining the network security of intelligent ships is the primary focus of this research, which mainly employed multi-sensor nodes to evaluate data containing information about malicious attacks and placed self-execution protection organize generating nodes into place to intercept and protect against attacks. The experimental outcomes demonstrate that the suggested AFL-VA-ISN model increases the data transmission rate by 99.2%, attack detection rate by 98.5%, risk assessment rate by 97.5%, and access control rate of 96.3%, and reduces the network latency rate of 11.4% compared to other existing models.