Impact of Self C Parameter on SVM-based Classification of Encrypted Multimedia Peer-to-Peer Traffic

Abstract

Home users are increasingly acquiring, at lower prices, electronic devices such as video cameras, portable audio players, smartphones, and video game devices, which are all interconnected through the Internet. This increase in digital equipment ownership induces a massive production and sharing of multimedia content between these users. The supervised learning machine method Support Vector Machine (SVM) is vastly used in classification. It is capable of recognizing patterns of samples of predefined classes and supports multi-class classification. The purpose of this article is to explore the classification of multimedia P2P traffic using SVMs. To obtain relevant results, it is necessary to properly adjust the so-called Self C parameter. Our results show that SVM with linear kernel leads to the best classification results of P2P video with an F-Measure of 99% for C parameter ranging from 10 to 70 and to the best classification results of P2P file-sharing with an F-Measure of 98% for C parameter ranging from 30 to 70. We also compare these results with the ones obtained with Kolmogorov-Smirnov (KS) tests and Chi-square tests. It is shown that SVM with linear kernel leads to a better classification performance than KS and chi-square tests, which reached an F-Measure of 67% and 70% for P2P filesharing and P2P video, respectively, for KS test, and reached an F-Measure of 85% for both P2P file-sharing and P2P video for chi-square test. Therefore, SVM with linear kernel and suitable values for the Self C parameter can be a good choice for identifying encrypted multimedia P2P traffic on the Internet.

Modelado espacial y temporal de contaminantes atmosféricos en la Zona Metropolitana de la Ciudad de México

“…Por su parte, Kriging Universal requiere encontrar un variograma óptimo para el conjunto de datos y así evitar extrapolaciones, además de que es sensible a un número bajo de puntos o una variación alta entre ellos (Shekaramiz et al, 2019). Finalmente, con el modelo Supported Vector Machine se deben elegir los parámetros Kernel y C de manera adecuada, ya que estos afectan la complejidad del modelo (Cunha et al, 2022;Liu & Xu, 2014). Pese a estas limitantes, Supported Vector Machine presentó el mejor ajuste para todos los contaminantes y, por tanto, se utilizó para representar su distribución espacial.…”

“…Kriging Universal requires an optimal variogram for the dataset to avoid extrapolations, and it is sensitive to a low number of points or a high variation between them (Shekaramiz et al, 2019). Finally, with Supported Vector Machine, Kernel and C parameters should be chosen appropriately, as they affect the complexity of the model (Cunha et al, 2022;Liu & Xu, 2014). Despite these limitations, Supported Vector Machine had the best fit for all pollutants and was therefore used to represent their spatial distribution.…”