Robust network Traffic Classification

Jun Zhang, Xiao Chen, Yang Xiang, Wanlei Zhou, Jie Wu

Research output: Contribution to journalArticleResearchpeer-review

199 Citations (Scopus)

Abstract

As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.

Original languageEnglish
Pages (from-to)1257-1270
Number of pages14
JournalIEEE/ACM Transactions on Networking
Volume23
Issue number4
DOIs
Publication statusPublished - 8 May 2014
Externally publishedYes

Keywords

  • Semi-supervised learning
  • traffic classification
  • zero-day applications

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