AI Defenders: Machine learning driven anomaly detection in critical infrastructures

Abstract

Previous studies have evaluated the suitability of different machine learning (ML) models for anomaly detection in critical infrastructures, which are pivotal due to the potential consequences of disruptions that can lead to safety risks, operational downtime, and financial losses. Ensuring robust anomaly detection for these systems within a company is vital to mitigate risks and maintain continuous operation. In this paper, we utilize a time-series labeled dataset obtained from a hydraulic model simulator (ELVEES simulator) to conduct a comprehensive and comparative analysis of various ML models. The study aims to demonstrate how different models effectively identify and respond to anomalies, underscoring the potential artificial intelligence (AI) driven systems to mitigate attacks. With the chosen approach, we expect to achieve the best performance in detecting two types of anomalies: point anomaly and contextual anomaly.