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Fault Diagnosis in DSL Networks using Support Vector Machines

Marnerides, A and Malinowski, S and Morla, R and Kim, HS (2015) Fault Diagnosis in DSL Networks using Support Vector Machines. COMPUTER COMMUNICATIONS. ISSN 0140-3664

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Abstract

The adequate operation for a number of service distribution networks relies on the e�ective maintenance and fault management of their underlay DSL infrastructure. Thus, new tools are required in order to adequately monitor and further diagnose anomalies that other segments of the DSL network cannot identify due to the pragmatic issues raised by hardware or software misconfigurations. In this work we present a fundamentally new approach for classifying known DSL-level anomalies by exploiting the properties of novelty detection via the employment of one-class Support Vector Machines (SVMs). By virtue of the imbalance residing in the
training samples that consequently lead to problematic prediction outcomes when used within two-class formulations, we adopt the properties of one-class classification and construct models for independently identifying and classifying a single type of a DSL-level
anomaly. Given the fact that the greater number of the installed Digital Subscriber Line Access Multiplexers (DSLAMs) within the DSL network of a large European ISP were misconfigured, thus unable to accurately flag anomalous events, we utilize as inference solutions the models derived by the one-class SVM formulations built by the known labels as flagged by the much smaller number
of correctly configured DSLAMs in the same network in order to aid the classification aspect against the monitored unlabelled events. By reaching an average over 95% on a number of classification accuracy metrics such as precision, recall and F-score we show that one-class SVM classifiers overcome the biased classification outcomes achieved by the traditional two-class formulations and that they may constitute as viable and promising components within the design of future network fault management strategies. In addition, we demonstrate their superiority over commonly used two-class machine learning approaches such as Decision Trees and Bayesian Networks that has been used in the same context within past solutions.

Keywords: Network management, Support Vector Machines, supervised learning, one-class classifiers, DSL anomalies

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Computer Communications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Communications, January 2015 DOI:10.1016/j.comcom.2015.01.006
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: ELSEVIER SCIENCE BV
Date Deposited: 16 Jan 2015 14:33
Last Modified: 22 Jul 2016 23:50
URI: http://researchonline.ljmu.ac.uk/id/eprint/319

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