One-dimensional CNN Model of Network Traffic Classification based on Transfer Learning

• Lingyun Yang,
• Yuning Dong,
• Zaijian Wang,
• Feifei Gao,

Vol. 18, No. 2, February 29, 2024
10.3837/tiis.2024.02.008, Download Paper (Free):

• Convolution Neural Network
• Traffic Classification
• Transfer Learning
• feature pre-processing

Abstract

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

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