DMCA
A Hybrid Classifier Using Reduced Signatures for Automated Soft-Failure Diagnosis in Network End-User Devices
BibTeX
@MISC{Widanapathirana_ahybrid,
author = {C. Widanapathirana and X. Ang and J. C. Li and M. V. Ivanovich and P. G. Fitzpatrick and Y. A. Şekercioğlu},
title = {A Hybrid Classifier Using Reduced Signatures for Automated Soft-Failure Diagnosis in Network End-User Devices},
year = {}
}
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Abstract
Abstract—We present an automated system for the diagnosis of both known and unknown soft-failures in end-user de-vices (UDs). Known faults that cause network performance degradation are used to train the classifier-based system in a supervised manner while unknown faults are automatically detected and clustered to identify the existence of new categories of soft-failures. The supervised classifier used in the system can be retrained by including the newly detected faults to enhance its performance. The system uses 460 features to construct Normalized Sta-tistical Signatures (NSSs) for fault characterization. Due to the high dimensionality of NSSs, EigenNSS was proposed to reduce the complexity without losing important information. Because of the natural network inconsistencies that exist in communication links, we propose FisherNSS, a reduced signature that provides improved linear separability between classes to further enhance classification performance. The system is evaluated over a live campus network using 17 emulated UD faults. The results show that the best overall classification accuracy of up to 97 % was achieved by using FisherNSS with a dimensionality reduction of 96.74%. In comparison, both EigenNSS and FisherNSS have faster training and diagnosis time compared to NSS, which makes them suitable for on-demand as well as real time diagnostic applications. Furthermore, FisherNSS compared to EigenNSS has a higher diagnostic accuracy and quicker diagnosis time (order of microseconds). I.
Keyphrases
network end-user device automated soft-failure diagnosis hybrid classifier using reduced signature diagnosis time new category high dimensionality dimensionality reduction fault characterization unknown soft-failures supervised classifier communication link enhance classification performance live campus network improved linear separability diagnostic accuracy natural network inconsistency automated system classifier-based system cause network performance degradation supervised manner reduced signature overall classification accuracy end-user de-vices unknown fault important information emulated ud fault real time diagnostic application normalized sta-tistical signature
