On-line SVM traffic classification.
ABSTRACT A wide range of traffic classification approaches has been proposed in the last few years by the scientific community. However, the development of complete classification architectures that work directly in real-time on high capacity links is limited. In this paper we present the implementation of a machine-learning technique (SVM), one of the most accurate but most computationally expensive mechanisms, on the CoMo project infrastructure. We show the computational time required to process different traffic traces and the optimization steps we adopted to improve the performance of the system and achieve real-time classification on high-speed links.
- SourceAvailable from: S. Vaton
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- "In order to enable a comparison between traces, we have grouped applications into eight different categories. Other traffic classification studies ,  use a similar number of classes. Unsupervised classifiers produce more classes, but this is not desired. "
ABSTRACT: Analyzing the composition of Internet traffic has many applications nowadays, like tracking bandwidth-consuming applications or QoS-based traffic engineering. Even though many classification methods, such as Support Vector Machines (SVMs) have demonstrated their accuracy, the ever-increasing data rates encountered in networks are higher than existing implementations can support. As SVM has been proven to provide a high level of accuracy, and is challenging to implement at high speeds, we consider in this paper the design of a real-time SVM traffic classifier at hundreds of Gb/s to allow online detection of categories of applications. We show the limits of software implementation and offer a solution based on the massive parallelism and low-level network interface access of FPGA boards. We also improve this solution by testing algorithmic changes that dramatically simplify hardware implementation. We then find theoretical supported bit rates up to 473 Gb/s for the most challenging trace on a Virtex 5 FPGA, and confirm them through experimental performance results on a Combov2 board with a 10 Gb/s interface.IEEE Transactions on Network and Service Management 09/2014; 11(3):278-291. DOI:10.1109/TNSM.2014.2346075
- "During the detection phase SVM simply classifies new points according to the subspace they belong to. SVM is often regarded as the best performing algorithm for traffic classification   and has been adopted by several authors   . The accuracy depends on the selection of the kernel functions where Radial Basis Function (RBF) kernels usually give good results. "
Conference Paper: Hardware Acceleration of SVM-Based Traffic Classification on FPGA[Show abstract] [Hide abstract]
ABSTRACT: 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.IWCMC TRAC 2012: 3rd International Workshop on TRaffic Analysis and Classification, Limassol, Cyprus; 08/2012
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ABSTRACT: Statistical traffic classification on high-speed, multi-Gb/s links has up to now been possible only with specialized, often proprietary, always quite costly hardware. In this paper we present MTCLASS, a new, multi-threaded, modular Internet statistical traffic analysis engine capable of running in real-time on commodity hardware processing multi-Gb/s traffic aggregates. Experimental results show that our engine, running on a low cost dual Xeon PC with a total of 12 cores at 2.6GHz can classify in real time using a Support Vector Machine (SVM) algorithm aggregates of up to 1.14 million packets per second, corresponding in the traces we used to a bit rate of 5.3 Gbps. We make MTCLASS' source code available to the community under an open source license.Communications (ICC), 2012 IEEE International Conference on; 01/2012