Network traffic classification method based on improved deep convolutional neural network

Abstract

The prerequisite of traffic classification based on machine learning models is that traffic is independent and has identical distributions. However, traffic changes in the wild increase the memory cost of these models and reduces their accuracy. To tackle these problems, this work proposes a new classification model. The model combines a principal component analysis algorithm and an improved deep convolutional neural network. The former performs dimensionality reduction so that the key features affecting detection accuracy are found. The latter adopts the autonomous feature learning method to improve the classification accuracy. Experiments show that the memory overhead is reduced by 3.2% and that the detection accuracy is improved by 8% relative to other similar works.