关键词: 人工神经网络, Inception, 端对端, 加密流量分类, 特征融合

Abstract:

Aiming at the problem that the existing artificial neural network method has a complicated structure and a large amount of calculation in the application of network encryption traffic classification, a lightweight network model Inception-CNN based on feature fusion is proposed for the classification of end-to-end encrypted traffic, while significantly improving the accuracy of classification results, greatly reducing the complexity of network calculations. The 1×1 convolution of the Inception module is used to reduce the dimensions, reduce the calculation parameters, and reduce the computational complexity. Then the feature extraction is done at different levels from different receptor fields, and the features of many different size filter convolutions are fused, so that richer features are extracted from the raw data to automatically learn the nonlinear relationship between the raw input and the expected output. The feature of the pooling operation without parameters is used to prevent overfitting. The international publicly available ISCX VPN-nonVPN dataset is selected as experimental data, and softmax is used as a classifier to achieve accurate classification of encrypted traffic. The experimental results show that the classification accuracy rate of the model reaches 97.3%, the precision reaches 97.2%, the recall rate reaches 97.7%, and the F1-score reaches 97.5%, and the recognition effect of different types of encrypted traffic is also more balanced.

Key words: artificial neural network, Inception, end-to-end, encrypted traffic classification, feature fusion