Theory of evidence-based automated decision making in cyber-physical systems

Abstract

Abstract-The Smart Grid is a complex cyber-physical system that is evolving rapidly from a relatively isolated to an open and diverse environment. Within this context, enhancing the security of the future Smart Grid becomes a major priority. In this paper we introduce the use of data fusion for automated decision making in cyber-physical systems such as the Smart Grid. One of the most important applications of decision making is in the field of anomaly detection. This can enable the detection of attacks in cyber-physical systems without requiring a complete description of the physical process. The novelty of our approach is that it combines reports of various cyber and physical sensors, rather than focusing on either one single metric, or one singe realm, as was the case of similar techniques. Based on the proposed architecture we implement a new cyber-physical anomaly detection system. We show that data fusion is much more effective if it combines both cyber and physical realms, rather than focusing on the two realms separately.

Evidence Theory for Cyber-Physical Systems

“…Evidence theory has been applied in multi-sensor fusion problems such as diagnosis [1]. Siaterlis and Genge [10] have proposed an evidence theory framework for anomaly detection. In contrast, this paper proposes a hybrid knowledge model for evaluating the intersections among hypotheses.…”