Machine Learning Approaches for Anomaly Detection in Cyber-Physical Systems: A Case Study in Critical Infrastructure Protection
Abstract
This paper presents machine learning approaches for anomaly detection in cyber-physical systems (CPS), with a focus on critical infrastructure protection. It investigates the application of supervised, unsupervised, and semi-supervised learning techniques in identifying abnormal behaviors and potential security threats in CPS environments. The study includes a case study analysis of anomaly detection methods applied to energy, transportation, and healthcare systems, highlighting their effectiveness in detecting and mitigating cyber-physical attacks. The paper discusses challenges such as data heterogeneity, scalability, and interpretability, and proposes strategies for improving anomaly detection performance in CPS.
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