期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2015
卷号：19
期号：1
页码：37-40
DOI：10.14445/22312803/IJCTT-V19P108
出版社：Seventh Sense Research Group
摘要：In modern network security and management architecture, Classification of Traffic plays a major role in recent years. In particular, the process of intrusion detection and QOS control is considered as a essential components in traffic classification. Recent method uses statistical feature related classification approach with machine learning techniques for Traffic classification. Earlier method used several machine learning classifiers for classification purpose. Due to the lack of classifier performance in each aspect, the overall classification of traffic affected while least size of training data are used. To deal with this process, this paper proposes an efficient classifier called adaptive nearest cluster based classifier (ANCCclassifier). The proposed classifier classifies the traffic by collecting the statistical feature based correlated information. Such information is obtained by analysing the normal and abnormal flow of network. The present system is analysed in theoretical and experiential perspectives. Experimental result provides improved performance when compared with the other state of art methods.
