SVM Based Event Detection and Identification: Exploiting Temporal Attribute Correlations Using SensGru

Abstract EN

In the context of anomaly detection in cyber physical systems (CPS), spatiotemporal correlations are crucial forhigh detection rate.

This work presents a new quarter sphere support vector machine (QS-SVM) formulation basedon the novel concept of attribute correlations.

Our event detection approach, SensGru, groups multiple sensors ona single node and thus eliminates communication between sensor nodes without compromising the advantages ofspatial correlation.

It makes use of temporal-attribute (TA) correlations and is thus a TA-QS-SVM formulation.

We show analytically that SensGru (or interchangeably TA-QS-SVM) results in a reduced node density and givesthe same event detection performance as more dense Spatiotemporal-Attribute Quarter-Sphere SVM (STA-QS-SVM)formulation which exploits both spatiotemporal and attribute correlations.

Moreover, this paper develops theoretical bounds on the internode distance, the optimal number of sensors, and the sensing range with SensGru so that the performance difference with SensGru and STA-QS-SVM is negligibly small.

Both schemes achieve event detectionrates as high as 100% and an extremely low false positive rate.