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oai:ebiltegia.mondragon.edu:20.500.11984/11842024-03-06T08:38:58Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Iturbe, Mikel author Garitano, Iñaki author Zurutuza, Urko author Uribeetxeberria, Roberto author 2016 Process Control Systems (PCSs) are the operat-ing core of Critical Infrastructures (CIs). As such, anomalydetection has been an active research field to ensure CInormal operation. Previous approaches have leveraged networklevel data for anomaly detection, or have disregarded theexistence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In thispaper we present an anomaly detection and diagnostic systembased on Multivariate Statistical Process Control (MSPC), thataims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process leveland controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approachcan be used to distinguish disturbances from intrusions to acertain extent and we conclude that the proposed approach canbe extended with other sources of data for improving results. 978-1-5090-3688-2 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=123680 https://hdl.handle.net/20.500.11984/1184 process control systems Multivariate Statistical Process Control Tennessee-Eastman On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems using Multivariate Statistical Process Control