Open Access

ARTICLE

Applications Classification of VPN Encryption Tunnel Based on SAE-2dCNN Model

Jie Luo^*, Qingbing Ji, Lvlin Ni

Science and Technology on Communication Security Laboratory, The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu, Sichuan, China

* Corresponding Author: Jie Luo. Email:

Journal on Artificial Intelligence 2022, 4(3), 133-142. https://doi.org/10.32604/jai.2022.031800

Received 27 April 2022; Accepted 20 July 2022; Issue published 01 December 2022

Abstract

How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique. Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel. Various VPN protocols make the feature engineering of machine learning extremely difficult. Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification. This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model. SAE can effectively reduce the dimensionality of the data, which not only improves the training efficiency of 2dCNN, but also extracts more precise features and improves accuracy. This paper uses the most common VPN encryption data in the real network to train and test the model. The test results verify the effectiveness of the SAE-2dCNN model.

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