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On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems using Multivariate Statistical Process Control

M. Iturbe; José Camacho; I. Garitano; U. Zurutuza; R. Uribeetxeberria
Abstract:
Process Control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In this paper we present an anomaly detection and diagnostic system based on Multivariate Statistical Process Control (MSPC), that aims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process level and controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approach can be used to distinguish disturbances from intrusions to a certain extent and we conclude that the proposed approach can be extended with other sources of data for improving results.
Research areas:
Year:
2016
Type of Publication:
In Proceedings
Keywords:
Process control systems, Multivariate Statistical Process Control, Tennessee-Eastman
Book title:
3rd International Workshop on Reliability and Security Aspects for Critical Infrastructure Protection (resa4ci 2016)
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