An Improved Network Traffic Classification Model Based on a Support Vector Machine

Abstract

Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

“…This model achieved 98% and 94% of recall for application identification and traffic categorization, respectively. Cao et al [27] proposed a network traffic classification technique based on SVM. To prevent overfitting, a wrapper-based feature selection algorithm is used to select important features.…”

Explainable Internet Traffic Classification

“…One of the first significant work on the application of SVM to traffic classification is [19], where the authors apply one of the approaches to solving multi-class problems with SVMs and describe a simple optimization algorithm that allows the classifier to perform correctly with as little training as a few hundred samples. Since then, many other works have proposed SVM-based methods [4,[20][21][22][23] and, as a result, SVM is nowadays considered as a de facto standard in the field. Nonetheless, as already discussed, all of these works propose methods based on black-box models that do not provide any information about the classification criteria.…”

“…As a consequence, in the last decade, research efforts have moved towards classification tools based on Machine Learning (ML) and Artificial Intelligence (AI) algorithms, which rely on statistical features [3]. Among these, Support Vector Machine (SVM) [4] and deep learning techniques [5] have emerged as powerful tools for traffic classification and other network application, such as intrusion detection [6] and other cyber security application [7,8]. Indeed, these techniques, and especially SVM, represent an almost de facto standard in the field.…”

Distributed Architecture Proposal for Efficient Energy Management of Road Lighting in Urban Environments

“…Consequently it is necessary to have an intelligent control algorithm that will be responsible for obtaining the desired efficiency. There are several algorithms based on neuronal networks [9], support vector machine [2] and machine learning [7]. The most used solution is to use distributed algorithms in which the nodes collaborate with each other.…”