High-Performance Internet Traffic Classification Using a Markov Model and Kullback-Leibler Divergence
Joint Authors
Kim, Jeankyung
Hwang, Jinsoo
Kim, Kichang
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-10
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Telecommunications Engineering
Abstract EN
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 n=100.
American Psychological Association (APA)
Kim, Jeankyung& Hwang, Jinsoo& Kim, Kichang. 2016. High-Performance Internet Traffic Classification Using a Markov Model and Kullback-Leibler Divergence. Mobile Information Systems،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1111560
Modern Language Association (MLA)
Kim, Jeankyung…[et al.]. High-Performance Internet Traffic Classification Using a Markov Model and Kullback-Leibler Divergence. Mobile Information Systems No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1111560
American Medical Association (AMA)
Kim, Jeankyung& Hwang, Jinsoo& Kim, Kichang. High-Performance Internet Traffic Classification Using a Markov Model and Kullback-Leibler Divergence. Mobile Information Systems. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1111560
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111560