[1] ƺ л.ڸĽEfficientNetV2෽о[J].ڿƼ,2023,25(12):1-5.
Research on Network Traffic Classification Method Based on Improved EfficientNetV2[J].Popular Science & Technology,2023,25(12):1-5.





Research on Network Traffic Classification Method Based on Improved EfficientNetV2
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network traffic classification improved EfficientNetV2 transfer learning hyperparametric optimization
Դͳ෽Чʵ¡ݼ󡢼ȺķѼԴ⣬һֻڸĽEfficientNetV2෽ǨѧϰķڴݼImageNetѵԤѵģEfficientNetV2Ǩݼʵ飬ݵص㣬ԭֱʽкţѵʱͬʱ徫ȷȣжγŻʵѡAdamAdaptive Moment Estim afionΪŻCosineAnnealing-Warm-upԡʵĽEfficientNetV2ResNet50ģ͡ԭEfficientNetV2ȣ׼ȷʷֱ1.19%1.21%ģѵʱֱ11 min5.5 minѵʱͬʱʵľ׼ࡣ
In view of the low efficiency of traditional network traffic classification methods, excessive dependence on data sets and extreme consumption of computing resources, this paper proposes a network traffic classification method based on improved EfficientNetV2. Using the method of transfer learning, the pre-training model EfficientNetV2 which reaches the standard of ImageNet training in large data sets is transferred to network traffic data sets for experiments, and according to the characteristics of network traffic data. The input resolution of the original network is scaled reasonably to shorten the data training time and improve the overall accuracy at the same time. After several hyperparametric optimization experiments, Adam (Adaptive Moment Estim afion) is selected as the optimizer and CosineAnnealing-Warm-up strategy is added. The experimental results show that compared with the ResNet50 model and the native EfficientNetV2, the accuracy of the improved EfficientNetV2 increases by 1.19% and 1.21% respectively, and the overall training time of the model is reduced by 11 min and 5.5 min, respectively. While shortening the data training time, the accurate classification of network traffic is realized.


[1] йϢģCNNICھ 48 Ρй緢չ״ͳƱ桷[EB/OL]. (2021-08-27) [2022-12-27]. http://www.cnnic.cn/gywm/xwzx/rdxw/ 20172017_7084/202109/t20210923_71551. html.[2] Protocol registries[EB/OL]. (2017-08-02)[2022-12-27]. https://www.iana.org/protocols. [3] MOORE A W, PAPAGIANNAKI K. Toward the accurate identification of network applications[C]// International Workshop on Passive and Active Network Measurement, Berlin Heidelberg: Springer, 2005: 41-54.[4] SANVITO D, MORO D, CAPONE A . Towards traffic classification offloading to stateful SDN data planes[C]// 2017 IEEE Conference on Network Softwarization. Bologna, Italy. IEEE, 2017: 1-4.[5] WANG P, YE F, CHEN X J, et al. Datanet: Deep learning based encrypted network traffic classification in SDN home gateway[J]. IEEE Access, 2018, 6: 55380-55391.[6] ܸգ毣Ըգ. оչչ[J]. ɼ2012(1): 32-42.[7] ؿľ⼪飬. ʵʱо[J]. ѧ2013(9): 8-15.[8] ΰ. һֻڸĽK-means㷨෽[J]. ӼӦã2017(11): 86-94.[9] ABBASI M, SHAHRAKI A, TAHERKORDI A. Deep learning for network traffic monitoring and analysis (NTMA): A survey[J]. Computer Communications, 2021, 170: 19-41.[10] WANG W, ZHU M, ZENG X W, et al. Malware traffic classification using convolutional neural network for representation learning[C]// 2017 International Conference on Information Networking (ICOIN). Da Nang, Vietnam. IEEE, 2017: 712-719.[11] WANG W, ZHU M, WANG J L, et al. End-to-end encrypted traffic classification with one-dimensional convolution neural networks[C]// 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). Beijing, China. IEEE, 2017: 43-48.[12] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. IEEE, 2016: 770-778.[13] TAN M, LE Q V. EfficientNetV2: Smaller models and faster training[EB/OL]. (2021-06-23)[2023-01-15]. https://arxiv.org/ abs/2104.00298.[14] TOUVRON H, VEDALDI A, DOUZE M, et al. Fixing the train-test resolution discrepancy: FixEfficientNet[EB/OL]. (2020-11-18)[2023-01-15]. https://arxiv.org/abs/2003.08237v4.


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