A Data Extraction Method for Anomaly Detection in Naval Systems

With the growth of Cyber-Physical Systems, new security challenges have emerged. Over the past few years, Naval systems have seen an increase of the deployment of Intrusion Detection Systems (IDS) to ensure the security of Programmable Logic Controller and Industrial Control System due to a plenty of vulnerabilities. In this context, several methods have been developed to effectively detect anomalies and intrusions such as cyber and physical alerts. Those methods need to be managed powerfully in order to increase anomaly detection in the cybernetic flows within naval systems. In this paper, we present a new strategy to generate meta data of naval cybernetics flows to illustrate vulnerabilities of naval systems. An anomaly detection method based on Teager-Kaiser operator is developed to show such vulnerabilities by analysing the collected time series. Simulations of three scenarios are provided to validate the new approach in Naval systems. The obtained results show the interest of the proposed naval anomaly detection strategy.