期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：4
页码：6551
DOI：10.15680/IJIRCCE.2017.0504001
出版社：S&S Publications
摘要：Ubiquitous high-speed communication networks play a crucial role in the modern life, demanding thehighest level of reliability and availability. Due to the rapid growth of computer networks in terms of size, complexityand heterogeneity, the probability of network faults increases. Manual network administration is hopelessly outdated;complex automated fault diagnosis and management are essential to ensure the provision and maintenance of highquality service in computer networks. Guaranteed Service with higher levels of reliability and availability for real-timeapplications can be achieved with a systematic approach for real-time classification of network faults, which helps inwell-informed (often-automated) decision making. In this paper we discuss three different data mining algorithms aspart of the proposed solution for network fault classification: K-Means, Fuzzy C Means, and ExpectationMaximization. The proposed approach can help capture abnormal behavior in communication networks, thus pavingthe way for real-time fault classification and management. We used datasets obtained from a network with heavy andlight traffic scenarios in the router and server and built a prototype to demonstrate the network traffic faultclassification under given scenarios. Our empirical results reveal that the FCM is more accurate while causingcomputational overhead. The other two algorithms attain almost the same performance.
关键词：Network fault diagnosis; network fault classification; clustering algorithms; Fuzzy Clustering;Means; Fuzzy C Means
