Abstract

International audience; Understanding the composition of the Internet traffic has many applications nowadays, mainly tracking bandwidth consuming applications, QoS-based traffic engineering and lawful interception of illegal traffic. Although many classification methods such as Support Vector Machines (SVM) have demonstrated their accuracy, not enough attention has been paid to the practical implementation of lightweight classifiers. In this paper, we consider the design of a real-time SVM classifier at many Gbps to allow online detection of categories of applications. Our solution is based on the design of a hardware accelerated SVM classifier on a FPGA board.

Abstract

The ability to accurately classify and identify the network traffic associated with different applications is a central issue for many network operation and research topics including Quality of Service enforcement, traffic engineering, security, monitoring and intrusion-detection. However, traditional classification approaches for traffic to higher-level application mapping, such as those based on port or payload analysis, are highly inaccurate for many emerging applications and hence useless in actual networks. This paper presents a recurrence plot-based traffic classification approach based on the analysis of non-stationary "hidden" transition patterns of IP traffic flows. Such nonlinear properties cannot be affected by payload encryption or dynamic port change and hence cannot be easily masqueraded. In performing a quantitative assessment of the above transition patterns, we used recurrence quantification analysis, a nonlinear technique widely used in many fields of science to discover the time correlations and the hidden dynamics of statistical time series. Our model proved to be effective for providing a deterministic interpretation of recurrence patterns derived by complex protocol dynamics in end-to-end traffic flows, and hence for developing qualitative and quantitative observations that can be reliably used in traffic classification. © 2008 Elsevier B.V. All rights reserved.