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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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The Journal of Machine Learning Research
Support Vector Data Description
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Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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Communications of the ACM - Wireless sensor networks
Convex Optimization
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Neural Computation
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VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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Expert Systems with Applications: An International Journal
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The VLDB Journal — The International Journal on Very Large Data Bases
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SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
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ACM Transactions on Sensor Networks (TOSN)
Some properties of the Gaussian kernel for one class learning
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Anomaly detection in wireless sensor networks
IEEE Wireless Communications
Structured One-Class Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Distributed support vector machines
IEEE Transactions on Neural Networks
Proceedings of the 2011 International Conference on Communication, Computing & Security
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Anomaly detection in wireless sensor networks is an important challenge for tasks such as intrusion detection and monitoring applications. This paper proposes two approaches to detecting anomalies from measurements from sensor networks. The first approach is a linear programming-based hyperellipsoidal formulation, which is called a centered hyperellipsoidal support vector machine (CESVM). While this CESVM approach has advantages in terms of its flexibility in the selection of parameters and the computational complexity, it has limited scope for distributed implementation in sensor networks. In our second approach, we propose a distributed anomaly detection algorithm for sensor networks using a one-class quarter-sphere support vector machine (QSSVM). Here a hypersphere is found that captures normal data vectors in a higher dimensional space for each sensor node. Then summary information about the hyperspheres is communicated among the nodes to arrive at a global hypersphere, which is used by the sensors to identify any anomalies in their measurements. We show that the CESVM and QSSVM formulations can both achieve high detection accuracies on a variety of real and synthetic data sets. Our evaluation of the distributed algorithm using QSSVM reveals that it detects anomalies with comparable accuracy and less communication overhead than a centralized approach.