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Robust Network Traffic Classification | IEEE Journals & Magazine | IEEE Xplore

Robust Network Traffic Classification


Abstract:

As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robus...Show More

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.
Published in: IEEE/ACM Transactions on Networking ( Volume: 23, Issue: 4, August 2015)
Page(s): 1257 - 1270
Date of Publication: 08 May 2014

ISSN Information:


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