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      Network Traffic Grouping Approach based on Statistic Signature and Support Vector Machine

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      https://www.riss.kr/link?id=A99715476

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      다국어 초록 (Multilingual Abstract)

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      As network traffic is dramatically increasing, classification of application traffic becomes important for the effective use of network resources. Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult because of many peer-to-peer (P2P) applications using dynamic port numbers, masquerading techniques, and encryption. An alternative approach is to classify traffic by exploiting the distinctive characteristics of applications. In this letter, we propose a classification method of application traffic using statistic signatures based on SVM (Support Vector Machine). The statistic signatures, defined as a directional sequence of packet size in a flow, are collected for each application, and applications are classified by SVM mechanism.
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      As network traffic is dramatically increasing, classification of application traffic becomes important for the effective use of network resources. Classification of network traffic using port-based or payload-based analysis is becoming increasingly di...

      As network traffic is dramatically increasing, classification of application traffic becomes important for the effective use of network resources. Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult because of many peer-to-peer (P2P) applications using dynamic port numbers, masquerading techniques, and encryption. An alternative approach is to classify traffic by exploiting the distinctive characteristics of applications. In this letter, we propose a classification method of application traffic using statistic signatures based on SVM (Support Vector Machine). The statistic signatures, defined as a directional sequence of packet size in a flow, are collected for each application, and applications are classified by SVM mechanism.

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      목차 (Table of Contents)

      • SUMMARY
      • Ⅰ. Introduction
      • Ⅱ. Related Works
      • Ⅲ. Our Classification Method
      • Ⅳ. Simulations
      • SUMMARY
      • Ⅰ. Introduction
      • Ⅱ. Related Works
      • Ⅲ. Our Classification Method
      • Ⅳ. Simulations
      • Ⅴ. Simulations
      • References
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