文章基本信息

标题：High-Performance Internet Traffic Classification Using a Markov Model and Kullback-Leibler Divergence
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作者：Jeankyung Kim ; Jinsoo Hwang ; Kichang Kim 等
期刊名称：Mobile Information Systems
印刷版ISSN：1574-017X
出版年度：2016
卷号：2016
DOI：10.1155/2016/6180527
出版社：Hindawi Publishing Corporation
摘要：As internet traffic rapidly increases, fast and accurate network classification is becoming essential for high quality of service control and early detection of network traffic abnormalities. Machine learning techniques based on statistical features of packet flows have recently become popular for network classification partly because of the limitations of traditional port- and payload-based methods. In this paper, we propose a Markov model-based network classification with a Kullback-Leibler divergence criterion. Our study is mainly focused on hard-to-classify (or overlapping) traffic patterns of network applications, which current techniques have difficulty dealing with. The results of simulations conducted using our proposed method indicate that the overall accuracy reaches around 90% with a reasonable group size of .