A Method of Improved CNN Traffic Classification
| Autor: | Yuming Liu, Yong Wang, Xiaochun Lei, Huiyi Zhou |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Normalization (statistics)
020203 distributed computing Contextual image classification Computer science business.industry Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network Statistical classification ComputingMethodologies_PATTERNRECOGNITION Traffic classification 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Feature learning |
| Zdroj: | CIS |
| Popis: | A traffic classification algorithm based on improved convolution neural network is proposed in this paper. It aims to improve the traditional traffic classification method. Firstly, the min-max normalization method is used to process the traffic data and map them into gray image, which will be used as the input data of convolution neural network to realize the independent feature learning. Then, an improved structure of the classical convolution neural network is proposed, both of the parameters of the feature map and the full connection layer are designed to select the optimal classification model to realize the traffic classification. Compared with the traditional classification method, the experimental results show that the proposed CNN traffic classification method can improve the accuracy and reduce the time of classification. |
| Databáze: | OpenAIRE |
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