000203930 001__ 203930
000203930 005__ 20181203023715.0
000203930 0247_ $$2doi$$a10.1155/2014/259508
000203930 022__ $$a1024-123X
000203930 02470 $$2ISI$$a000345041100001
000203930 037__ $$aARTICLE
000203930 245__ $$aSVM Based Event Detection and Identification: Exploiting Temporal Attribute Correlations Using SensGru
000203930 260__ $$bHindawi Publishing Corporation$$c2014$$aNew York
000203930 269__ $$a2014
000203930 300__ $$a12
000203930 336__ $$aJournal Articles
000203930 520__ $$aIn 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.
000203930 700__ $$0248142$$g232886$$uEcole Polytech Fed Lausanne, Doctoral Sch Informat & Commun Sci EDIC, CH-1015 Lausanne, Switzerland$$aShahid, Nauman
000203930 700__ $$uDHA, LUMS SBA Sch Sci & Engn, Dept Elect Engn, Lahore 54792, Pakistan$$aNaqvi, Ijaz Haider
000203930 700__ $$aBin Qaisar, Saad
000203930 773__ $$tMathematical Problems In Engineering
000203930 909C0 $$xU10318$$0252445$$pIEL
000203930 909CO $$pSTI$$particle$$ooai:infoscience.tind.io:203930
000203930 937__ $$aEPFL-ARTICLE-203930
000203930 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000203930 980__ $$aARTICLE