Research on mobile traffic data augmentation methods based on SA-ACGAN-GN

• Published in: Mathematical biosciences and engineering : MBE Vol. 19; no. 11; pp. 11512 - 11532
• Main Authors: Gong, Xingyu, Jia, Ling, Li, Na
• Format: Journal Article
• Language: English
• Published: AIMS Press 01.01.2022
• Subjects: generative adversarial network; gradient normalization; long-tailed distribution; mobile traffic classification; self-attention mechanism
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2022536

With the rapid development and application of the mobile Internet, it is necessary to analyze and classify mobile traffic to meet the needs of users. Due to the difficulty in collecting some application data, the mobile traffic data presents a long-tailed distribution, resulting in a decrease in classification accuracy. In addition, the original GAN is difficult to train, and it is prone to "mode collapse". Therefore, this paper introduces the self-attention mechanism and gradient normalization into the auxiliary classifier generative adversarial network to form SA-ACGAN-GN model to solve the long-tailed distribution and training stability problems of mobile traffic data. This method firstly converts the traffic into images; secondly, to improve the quality of the generated images, the self-attention mechanism is introduced into the ACGAN model to obtain the global geometric features of the images; finally, the gradient normalization strategy is added to SA-ACGAN to further improve the data augmentation effect and improve the training stability. It can be seen from the cross-validation experimental data that, on the basis of using the same classifier, the SA-ACGAN-GN algorithm proposed in this paper, compared with other comparison algorithms, has the best precision reaching 93.8%; after adding gradient normalization, during the training process of the model, the classification loss decreases rapidly and the loss curve fluctuates less, indicating that the method proposed in this paper can not only effectively improve the long-tail problem of the dataset, but also enhance the stability of the model training.