Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks

Anomaly detection is an important challenge for tasks such as fault diagnosis and intrusion detection in energy constrained wireless sensor networks. A key problem is how to minimise the communication overhead in the network while performing in-network computation when detecting anomalies. Our appro...

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Bibliographic Details
Published in2007 IEEE International Conference on Communications pp. 3864 - 3869
Main Authors Rajasegarar, S., Leckie, C., Palaniswami, M., Bezdek, J.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2007
Subjects
Bandwidth
Computer networks
Computer science
Energy consumption
Energy efficiency
Intrusion detection
Sensor phenomena and characterization
Support vector machine classification
Support vector machines
Wireless sensor networks
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Summary:Anomaly detection is an important challenge for tasks such as fault diagnosis and intrusion detection in energy constrained wireless sensor networks. A key problem is how to minimise the communication overhead in the network while performing in-network computation when detecting anomalies. Our approach to this problem is based on a formulation that uses distributed, one-class quarter-sphere support vector machines to identify anomalous measurements in the data. We demonstrate using sensor data from the Great Duck Island Project that our distributed approach is energy efficient in terms of communication overhead while achieving comparable accuracy to a centralised scheme.
ISBN:9781424403530
1424403537
ISSN:1550-3607
DOI:10.1109/ICC.2007.637