NDLI: Application traffic classification using statistic signature

Content Provider	IEEE Xplore Digital Library
Author	Hyun-Min An Myung-Sup Kim Jae-Hyun Ham
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Korea Univ., Seojong, South Korea (Hyun-Min An; Myung-Sup Kim; Jae-Hyun Ham)
Abstract	Networks today are becoming more complex and diverse because of the appearance of new applications and services. The importance, therefore, of application-level traffic classification is increasing daily. Application-level traffic classification has become a very popular area of study. Although many proposals have been presented, including port-based, payload-based and machine learning-based methods, the method that can manage all traffic has not yet been developed. More recently, methods based on statistical flow information have been studied. In this paper, we propose an application-level traffic classification methodology using the statistic signature. Our method creates a statistic signature using payload size, transmission order, and direction of the first N packets in the flow, and uses this to classify application traffic. Then, using a verification system, we prove the feasibility of our method and show its high accuracy.
Starting Page	1
Ending Page	6
File Size	742042
Page Count	6
File Format	PDF
ISBN	9784885522796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-25
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electronics, Information and Communication Engineers (IEICE)
Subject Keyword	Atmospheric measurements Application Traffic Particle measurements Traffic classification Reliability Statistic signature
Content Type	Text
Resource Type	Article

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