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Critical infrastructures like water treatment facilities and power plantsdepend on industrial control systems (ICS) for monitoring and control, makingthem vulnerable to cyber attacks and system malfunctions. Traditional ICSanomaly detection methods lack transparency and interpretability, which make itdifficult for practitioners to understand and trust the results. This paperproposes a two-phase dual Copula-based Outlier Detection (COPOD) method thataddresses these challenges. The first phase removes unwanted outliers using anempirical cumulative distribution algorithm, and the second phase develops twoparallel COPOD models based on the output data of phase 1. The method is basedon empirical distribution functions, parameter-free, and providesinterpretability by quantifying each feature's contribution to an anomaly. Themethod is also computationally and memory-efficient, suitable for low- andhigh-dimensional datasets. Experimental results demonstrate superiorperformance in terms of F1-score and recall on three open-source ICS datasets,enabling real-time ICS anomaly detection.

Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System