Fine-grained Traffic Classification Based on Improved Residual Convolutional Network in Software Defined Networks
Keywords:
Deep Learning (DL), Deep Learning (DL), Fine-grained Traffic Classification, Long Short-Term Memory (LSTM), Residual Convolutional Networks, Software Defined Networks (SDN)., Fine-grained Traffic ClassificationAbstract
As Internet traffic becomes increasingly complex, a growing number of applications are carrying different services. To provides service-aware traffic classification, allowing network operators to better analyze network composition as well as manage and schedule efficiently network resources to facilitate sustainable development network, fine-grained traffic classification has become a crucial and challenging problem. Software defined networks has become the most promising new network architecture in the future with two major advantages of centralized control and programmability. Deep learning has also become a mainstream technology by its excellent feature extraction ability for large datasets. Thus the combination of software defined networks and deep learning can effectively solve the challenge of data set collection and feature extraction in the field of traffic classification. Inspired by research in computer vision, in this paper we propose an improved residual convolutional network approach to network traffic classification, which addresses the network degradation problem that occurs with increasing network depth in traditional deep learning methods for fine-grained network traffic identification, and can effectively learn deeper network features to achieve fine-grained network traffic classification. Experimental results show that the overall accuracy of the proposed method can reach 99.93%, which is about 4%, 13%, 7% and 2% higher than other state-of-the-art models such as classical residual networks ResNet-18, One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) and combined model CNN-LSTM respectively, thus verify the effectiveness of our method.
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