A network traffic identification method based on AutoEncoder - a feature selection algorithm

Abstract

Traffic identification methods consider a large number of traffic features, resulting in low identification efficiency. To address the efficiency problem of traffic recognition, this paper proposes an efficient network traffic recognition method, AutoEncoder-based traffic recognition (AE-NTI). The method first preprocesses the original dataset and converts it into a two-dimensional grayscale image. Then, feature selection is performed by an improved feature selection algorithm based on AutoEncoder. The algorithm consists of a feature scorer, which globally scores all features, and a feature selector, which selects the highest scoring features to reconstruct the original data. Finally, the convolutional neural network structure is adjusted so that the network traffic can be identified. The experimental results show that the method has significantly improved in recognition accuracy and model fitting speed.