Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (18): 114-121.

### End-to-End Encrypted Traffic Classification Based on Feature Fusion Convolutional Neural Network

XUE Wenlong, YU Jiong, GUO Zhiqi, LI Ziyang

1. 1.School of Software, Xinjiang University, Urumqi 830008, China
2.Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830008, China
3.School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
• Online:2021-09-15 Published:2021-09-13

### 基于特征融合卷积神经网络的端到端加密流量分类

1. 1.新疆大学 软件学院，乌鲁木齐 830008
2.新疆大学 软件工程技术重点实验室，乌鲁木齐 830008
3.新疆大学 信息科学与工程学院，乌鲁木齐 830046

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.