Support Vector Machine for Internet Traffic Identification

In this paper a non linear system identification problem is addressed. A Support Vector Regressor is used to solve the Internet traffic identification problem. We give a basic idea underlying Support Vector (SV) machine for regression, which is a novel type of learning machine based on statistical learning theory. Furthermore, we describe how SV regressor can be applied for non linear system identification. In our simulations results we present two type of kernel functions, the Radial Basis Function (RBF), and the hyperbolic tangent, which are compared with the classical two-layer MLP (Multi-Layer-Perceptron) Neural Networks, trained to minimize a quadratic error objective with the Back-Propagation (BP) algorithm. The SV regressor outperforms the MLP and demonstrates its effectiveness for solving non linear system identification problems.