CAPTAR: Causal-Polytree-based Anomaly Reasoning for SCADA Networks
Klara Nahrstedt
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.
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