CAPTAR: Causal-Polytree-based Anomaly Reasoning for SCADA Networks

Klara Nahrstedt
Citation:

Ren, W., Yu, Y., Yardley, T., Nahrstedt, K. (2019) CAPTAR: Causal-Polytree-based Anomaly Reasoning for SCADA Networks. IEEE SmartGridComm 2019, Beijing, China, October 2019.

Abstract:

The Supervisory Control and Data Acquisition (SCADA) system is the most commonly used industrial control system but is subject to a wide range of serious threats. Intrusion detectionsystemsaredeployedtopromotethesecurityofSCADA systems, but they continuously generate tremendous number of alerts without further comprehending them. There is a need for an efficient system to correlate alerts and discover attack strategies to provide explainable situational awareness to SCADA operators. In this paper, we present a causal-polytree-based anomaly reasoning framework for SCADA networks, named CAPTAR. CAPTAR takes the meta-alerts from our previous anomaly detection framework EDMAND, correlates the them using a naive Bayes classifier, and matches them to predefined causal polytrees. Utilizing Bayesian inference on the causal polytrees,CAPTARcanproducesahigh-levelviewofthesecurity state of the protected SCADA network. Experiments on a prototype of CAPTAR proves its anomaly reasoning ability and its capabilities of satisfying the real-time reasoning requirement.

Publication Status:
Published
Publication Type:
Journal Article
Publication Date:
10/31/2019
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