[1] Li R P, Zhao Z F, Zheng J C, et al. The learning and prediction of application-level traffic data in cellular networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(6):3899-3912. [2] Anderson B, Mcgrew D. Machine learning for encrypted malware traffic classification:accounting for noisy labels and non-stationarity[C]//201923rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017:1723-1732. [3] Shi Y, Biswas S. A deep-learning enabled traffic analysis engine for video source identification[C]//201911th International Conference on Communication systems and networks (COMSNETS), 2019:15-21. [4] Wang W, Zhu M, Wang J L, et al. End-to-end encrypted traffic classification with onedimensional convolution neural networks[C]//2017 IEEE International Conference on Intelligence and Security Informatics, 2017:43-48. [5] Wang X Z, Mei X Y, Huang Q H, et al. Fine-grained learning performance prediction via adaptive sparse self-attention networks[J]. Information Sciences, 2021, 545(4):223-240. [6] Vu L, Thuy H V, Nguyen Q U, et al. Time series analysis for encrypted traffic classification:a deep learning approach[C]//201818th International Symposium on Communications and Information Technologies (ISCIT), 2018:121-126. [7] Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77:354-377. [8] Kim Y. Convolutional neural networks for sentence classification[C]//2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014:1746-1751. [9] Zhang X L, Han P, Xu L, et al. Research on bearing fault diagnosis of wind turbine gearbox based on 1DCNN-PSO-SVM[J]. IEEE Access, 2020, 8:192248-192258. [10] He J Y, Lee J, Song T T, et al. Recurrent neural network (RNN) for delay-tolerant repetitioncoded (RC) indoor optical wireless communication systems[J]. Optics Letters, 2019, 44(15):3745. [11] Velan P, Čermák M, Čeleda P, et al. A survey of methods for encrypted traffic classification and analysis[J]. International Journal of Network Management, 2015, 25(5):355-374. [12] 杨凌云, 董育宁, 王再见, 等. 基于M值概率分布的网络视频流分类[J]. 电子与信息学报, 2018, 40(5):1094-1100. Yang L Y, Dong Y N, Wang Z J, et al. Network video traffic classification based on probability distribution of M value[J]. Journal of Electronics & Information Technology, 2018, 40(5):1094-1100. (in Chinese) [13] Ma R L, Qin S J. Identification of unknown protocol traffic based on deep learning[C]//20173rd IEEE International Conference on Computer and Communications (ICCC) IEEE, 2017:1195-1198. [14] 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), 2017:712-717. [15] 孔俊. 基于双层特征融合的生物识别[J]. 北华大学学报(自然科学版), 2020, 21(1):110-117. Kong J. Biometric identification based on two-layer feature fusion[J]. Journal of Beihua University (Natural Science), 2020, 21(1):110-117. (in Chinese) [16] Dainotti A, Pescape A, Claffy K C. Issues and future directions in traffic classification[J]. IEEE Network, 2012, 26(1):35-40. |