Biblio

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 detection systems are deployed to promote the security of SCADA 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, CAPTAR can produces a high-level view of the security 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.

The Smart Grid control systems need to be protected from internal attacks within the perimeter. In Smart Grid, the Intelligent Electronic Devices (IEDs) are resource-constrained devices that do not have the ability to provide security analysis and protection by themselves. And the commonly used industrial control system protocols offer little security guarantee. To guarantee security inside the system, analysis and inspection of both internal network traffic and device status need to be placed close to IEDs to provide timely information to power grid operators. For that, we have designed a unique, extensible and efficient operation-level traffic analyzer framework. The timing evaluation of the analyzer overhead confirms efficiency under Smart Grid operational traffic.

The Smart Grid control systems need to be protected from internal attacks within the perimeter. In Smart Grid, the Intelligent Electronic Devices (IEDs) are resource-constrained devices that do not have the ability to provide security analysis and protection by themselves. And the commonly used industrial control system protocols offer little security guarantee. To guarantee security inside the system, analysis and inspection of both internal network traffic and device status need to be placed close to IEDs to provide timely information to power grid operators. For that, we have designed a unique, extensible and efficient operation-level traffic analyzer framework. The timing evaluation of the analyzer overhead confirms efficiency under Smart Grid operational traffic.