 SVM Based Event Detection and Identification: Exploiting Temporal Attribute Correlations Using SensGruNauman Shahid, Ijaz Haider Naqvi and Saad Bin Qaisar Mathematical Problems in Engineering, 2014, vol. 2014, 1-12 Abstract: In the context of anomaly detection in cyber physical systems (CPS), spatiotemporal correlations are crucial for high detection rate. This work presents a new quarter sphere support vector machine (QS-SVM) formulation based on the novel concept of attribute correlations . Our event detection approach, SensGru, groups multiple sensors on a single node and thus eliminates communication between sensor nodes without compromising the advantages of spatial 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 gives the 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 detection rates as high as 100% and an extremely low false positive rate. Date: 2014 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:259508 DOI: 10.1155/2014/259508 Access Statistics for this article More articles in Mathematical Problems in Engineering from Hindawi |
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