TY - GEN
T1 - Clustering elliptical anomalies in sensor networks
AU - Bezdek, James C.
AU - Havens, Timothy C.
AU - Keller, James M.
AU - Leckie, Chris
AU - Park, Laurence
AU - Palaniswami, Marimuthu
AU - Rajasegarar, Sutharshan
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Elliptical similarity
KW - Visual assessment of clustering tendency
KW - Wireless sensor networks
UR - https://www.scopus.com/pages/publications/78549267848
U2 - 10.1109/FUZZY.2010.5584464
DO - 10.1109/FUZZY.2010.5584464
M3 - Conference contribution
AN - SCOPUS:78549267848
SN - 9781424469208
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
ER -