Cyber-Attack Detection on Maritime Autonomous Surface Ships: Python-based Predictive Analysis
TL;DR
A hybrid model that combines LSTM and random forests to improve reliability, expandability, and predictive accuracy is proposed, which aims to enhance the detection accuracy with techniques such as deep learning and machine learning.
Abstract
Maritime Autonomous Surface Ships that are used for navigation, cargo handling and communication on a digital system. It improves operational efficiency and maximises cyber risk threats. This project is based on a framework that incorporates cybersecurity methodologies and examines dynamic data, such as geo-location coordinates, timestamps, navigation map status, performance, efficiency, cargo type, data source, and destination port, grounded in the operating unit. By leveraging Python, machine learning, and deep learning techniques, the system can easily analyse both real-time and Automatic Identification System datasets to detect anomalies linked to potential cyber threats. Anomalies are detected early, and the model supports risk mitigation and enhances autonomous maritime operations. Informed decision-making can support proactive maritime monitoring, with Automatic Identification System data features that improve accuracy and reliability. This paper proposes a hybrid model that combines LSTM and random forests to improve reliability, expandability, and predictive accuracy. The model aims to enhance the detection accuracy with techniques such as deep learning and machine learning.