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Distributed Anomaly Detection using Minimum Volume Elliptical Principal Component Analysis

O'Reilly, CE, Gluhak, A and Imran, A (2016) Distributed Anomaly Detection using Minimum Volume Elliptical Principal Component Analysis IEEE Transactions on Knowledge and Data Engineering, 28 (9). pp. 2320-2333.

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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 neighbouring 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: Article
Subjects : subj_Electronic_Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
AuthorsEmailORCID
O'Reilly, CEUNSPECIFIEDUNSPECIFIED
Gluhak, AUNSPECIFIEDUNSPECIFIED
Imran, AUNSPECIFIEDUNSPECIFIED
Date : 21 April 2016
Identification Number : 10.1109/TKDE.2016.2555804
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 15 Apr 2016 15:41
Last Modified : 16 Nov 2016 15:54
URI: http://epubs.surrey.ac.uk/id/eprint/810452

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