Abstract

The level of automation, operational efficacy, and situational awareness in maritime domains has increased due to the development of smart port facilities along with the global deployment of Maritime Wireless Sensor Networks (MWSNs). Increased automation enables unparalleled connectivity, which in turn, has acute implications for cybersecurity risks. The broadened span of integration within systems automatically heightens the attack surface susceptible to numerous cyber threats. This paper elaborates on the design and architecture of a machine-learning Intrusion Detection Systems (IDS) for Mobile Wireless Sensor Networks (MWSNs) in smart ports. This system applies supervised-learning techniques to define attacks into multiple classes, such as denial of service (DoS), spoofing, and packet injection/alteration. Focused simulation tests aimed at evaluating realistic maritime communication scenarios; the analysis from the simulations confirms that the system provides high detection rates, maintains low false-positive outcomes, and enhances processing efficiency under real-time conditions. Tailored agile intelligent IDS designed for smart ports severely undermined the maritime systems cybersecurity.