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.
Highlights
With the dramatic growth in network applications, the current network has become a large, dynamic and complex system
We compare the proposed model with the traditional support vector machine (SVM), and the representative supervised machine learning algorithm. It shows that traffic classification performance can be significantly improved by the proposed model by using very few training samples
We proposed an improved grid search algorithm, the algorithm gets the best combination of the key parameter of SVM classifier to improve the classification accuracy and prevent overfitting
Summary
With the dramatic growth in network applications, the current network has become a large, dynamic and complex system. The traffic classification method based on machine learning faces some challenges: (1) The optimal feature subset is difficult to obtain. We focus on reducing the dimension of the flow features and deriving the optimal working parameters based on the machine learning model. Traffic classification model, improve its classification and generalization ability, and improved the grid search parameter optimization algorithm is proposed. We compare the proposed model with the traditional SVM, and the representative supervised machine learning algorithm. It shows that traffic classification performance can be significantly improved by the proposed model by using very few training samples.
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