Traffic Classification Method Based on Dynamic Balance Adaptive Transfer Learning

SHANG Fengjun; LI Saisai; WANG Ying; CUI Yunfan

doi:10.11999/JEIT210623

Volume 44 Issue 9

Sep. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3308-3319

SHANG Fengjun, LI Saisai, WANG Ying, CUI Yunfan. Traffic Classification Method Based on Dynamic Balance Adaptive Transfer Learning[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3308-3319. doi: 10.11999/JEIT210623

Citation:

SHANG Fengjun, LI Saisai, WANG Ying, CUI Yunfan. Traffic Classification Method Based on Dynamic Balance Adaptive Transfer Learning[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3308-3319. doi: 10.11999/JEIT210623

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Traffic Classification Method Based on Dynamic Balance Adaptive Transfer Learning

doi: 10.11999/JEIT210623

School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400000, China

Funds: The National Natural Science Foundation of China (61672004)

Received Date: 2021-06-22
Accepted Date: 2022-03-10
Rev Recd Date: 2022-01-28

Available Online: 2022-03-20

Publish Date: 2022-09-19

Abstract

Abstract

In this paper, an improved and adaptive transfer learning algorithm is proposed for mobile application traffic recognition filed, which maps the sample features of source domain and target domain into high-dimensional feature space to minimize the marginal distribution and conditional distribution distances of the domains. A probabilistic model is presented to judge and calculate the difference between marginal distribution and conditional distribution between the domains. It can determine the degree of classification category and calculate quantitively the balance factor $ \mu $, which solves effectively the problem that DDA only considers classification error rate and ignores the degree of confirmation. Besides, the cliff-type down strategy is introduced to determine dynamically the number of feature principal. Compared with the traditional machine learning method, the proposed algorithm improves the accuracy by about 7%. Moreover, a feature selection algorithm for application traffic recognition is proposed to solve the problem of high feature dimension, where the reverse feature self-deleting strategy combined with the Earth Mover’s Distance (EMD) and used the correlation coefficient of the bulldozer to weight the information gain is introduced. It solves the problems that increasing the training time of model due to invalid features and decreasing the model performance and accuracy caused by irrelevant features. Simulation result shows that when the training input data for transfer learning uses the feature set processed by the proposed algorithm, the time of the transfer algorithm can be shortened by about 80%.
- Transfer learning,
- Application recognition,
- Feature extraction,
- Cross-domain

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References(23)

References

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