Distributed anomaly detection using minimum volume elliptical principal component analysis

O'Reilly, C., Gluhak, A. and Imran, M. A. (2016) Distributed anomaly detection using minimum volume elliptical principal component analysis. IEEE Transactions on Knowledge and Data Engineering, 28(9), pp. 2320-2333. (doi:10.1109/TKDE.2016.2555804)

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Abstract

Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models; however, in this environment, links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighboring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: O'Reilly, C., Gluhak, A., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering
Journal Name:IEEE Transactions on Knowledge and Data Engineering
Publisher:IEEE
ISSN:1041-4347
Published Online:21 April 2016
Copyright Holders:Copyright © 2016 IEEE
First Published:First published in IEEE Transactions on Knowledge and Data Engineering 28(9): 2320-2333
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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