文章基本信息

标题：A Hybrid Classifier Using Reduced Signatures for Automated Soft-Failure Diagnosis in Network End-User Devices
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作者：Widanapathirana, C. ; Ang, X. ; Li, J. C. 等
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2014
卷号：9
期号：12
页码：3275-3289
DOI：10.4304/jnw.9.12.3275-3289
语种：English
出版社：Academy Publisher
摘要：We present an automated system for the diagnosis of both known and unknown soft-failures in end-user devices (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 Statistical 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).
关键词：Hybrid Classifier Using Reduced Signatures;Automated Soft-Failure Diagnosis;Network End-User Devices