Clustering elliptical anomalies in sensor networks

被引：0

作者：

Bezdek, James C. ^{[1
]}

Havens, Timothy C. ^{[1
]}

Keller, James M. ^{[1
]}

Leckie, Chris ^{[2
]}

Park, Laurence ^{[2
]}

Palaniswami, Marimuthu ^{[2
]}

Rajasegarar, Sutharshan ^{[2
]}

机构：

[1] Univ Missouri, Columbia, MO 65211 USA

[2] Univ Melbourne, Melbourne, Vic 3010, Australia

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) | 2010年

关键词：

Anomaly detection; Visual assessment of clustering tendency; Elliptical similarity; wireless sensor networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We model anomalies in wireless sensor networks with ellipsoids that represent node measurements. Elliptical anomalies (EAs) are level sets of ellipsoids, and classify them as type 1, type 2 and higher order anomalies. Three measures of (dis) similarity between pairs of ellipsoids convert model ellipsoids into dissimilarity data. Clusters in the dissimilarity data may correspond to normal and anomalous measurements and nodes in the network. Assessment of (clustering) tendency is facilitated by visual inspection of (VAT/iVAT) images. Two examples illustrate the potential for anomaly detection.

引用

页数：8

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