An encrypted traffic classification method based on autoencoders and convolutional neural networks

Abstract

To solve the problems of existing encrypted traffic classification methods, such as the need for large-scale training data, high computational costs, and poor generalization ability, an encrypted traffic classification method based on autoencoders and convolutional neural networks was proposed. This method first utilizes an autoencoder to recon-struct the dataset, enabling it to work with smaller-scale datasets. The autoencoder allows shorter traffic flows to learn abstract feature representations from longer traffic flows of the same type, replacing zeros and mitigating the negative effects of zero-padding on traffic classification when using uniform flow lengths. After reconstruction, a convolutional neural network is used to classify the traffic. Due to its characteristics of parameter sharing and local connectivity, the CNN exhibits strong generalization ability when handling tasks, allowing it to better adapt to samples outside of the training data. Experimental results show that, compared to existing advanced methods, this method can achieve a classi-fication accuracy improvement of 2.86% to 18.13%, while also demonstrating greater robustness compared to other advanced methods.