Abstract
Network traffic classification aims to associate the network traffic with a class of traffic characterization (e.g., Streaming) or applications (e.g., Facebook). This ability plays an important role in advanced network management. The tasks of traffic characterization and application identification are usually implemented by individual models. However, when multiple models are deployed in the online environment, this causes a dramatic increase in the complexity, resource demand and maintenance costs. In this paper, an efficient multi-task learning method named multi-task transformer (MTT) is proposed. It simultaneously classifies the traffic characterization and application identification tasks. The proposed model considers the input packet as a sequence of bytes and applies a multi-head attention mechanism to extract features. Experiments are conducted on the ISCX VPN-nonVPN dataset to demonstrate the effectiveness of MTT. \(F_1\) scores of 98.75% and 99.35% have been achieved for application identification and traffic characterization, respectively. To the best of our knowledge, the results are better than the state-of-the-art results. The MTT model outputs the two results simultaneously in \(\sim\) 0.1 milliseconds (per packet), which satisfies the requirement of online traffic classification. Compared with the 1D-CNN and 2D-CNN models, the proposed MTT model is more stable, presents higher classification performance and requires less storage space. Finally, the selection strategies of input length for different neural networks are suggested and the related principles are investigated.
Similar content being viewed by others
References
Aceto G, Ciuonzo D, Montieri A, Pescapé A (2019) Mobile encrypted traffic using deep learning: Experimental evaluation, lessons learned, and challenges. IEEE Transactions on Network and Service Management 16(2):445–458
Yao H, Liu C, Zhang P, Wu S, Jiang C, Yu S (2019) Identification of encrypted traffic through attention mechanism based long short term memory. IEEE Trans Big Data
Lotfollahi M, Siavoshani MJ, Zade RSH, Saberian M (2020) Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Computing 24(3):1999–2012
Wang M, Zheng K, Luo D, Yang Y, Wang X (2020a) An encrypted traffic classification framework based on convolutional neural networks and stacked autoencoders. In: 2020 IEEE 6th international conference on computer and communications (ICCC). IEEE, pp 634–641
Shapira T, Shavitt Y (2019) Flowpic: Encrypted internet traffic classification is as easy as image recognition. In: IEEE INFOCOM 2019-IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, pp 680–687
Hu C, Wang Y, Gu J (2020) Cross-domain intelligent fault classification of bearings based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks. Knowledge-Based Systems 209:106214
Cui S, Jiang B, Cai Z, Lu Z, Liu S, Liu J (2019) A session-packets-based encrypted traffic classification using capsule neural networks. In: 2019 IEEE 21st international conference on high performance computing and communications; IEEE 17th international conference on smart city; IEEE 5th international conference on data science and systems (HPCC/SmartCity/DSS). IEEE, pp 429–436
Bu Z, Zhou B, Cheng P, Zhang K, Ling ZH (2020) Encrypted network traffic classification using deep and parallel network-in-network models. IEEE Access 8:132950–132959
Ma Q, Huang W, Jin Y, Mao J (2021) Encrypted traffic classification based on traffic reconstruction. In: 2021 4th International conference on artificial intelligence and big data (ICAIBD). IEEE, pp 572–576
Sun B, Yang W, Yan M, Wu D, Zhu Y, Bai Z (2020) An encrypted traffic classification method combining graph convolutional network and autoencoder. In: 2020 IEEE 39th international performance computing and communications conference (IPCCC). IEEE, pp 1–8
Vu L, Thuy HV, Nguyen QU, Ngoc TN, Nguyen DN, Hoang DT, Dutkiewicz E (2018) Time series analysis for encrypted traffic classification: A deep learning approach. In: 2018 18th international symposium on communications and information technologies (ISCIT). IEEE, pp 121–126
Lu CN, Huang CY, Lin YD, Lai YC (2012) Session level flow classification by packet size distribution and session grouping. Computer Networks 56(1):260–272
Crotti M, Dusi M, Gringoli F, Salgarelli L (2007) Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Computer Communication Review 37(1):5–16
Shim KS, Ham JH, Sija BD, Kim MS (2017) Application traffic classification using payload size sequence signature. International Journal of Network Management 27(5):1981
Lin T, Wang Y, Liu X, Qiu X (2021) A survey of transformers. arXiv:21060 4554. Accessed 8 June 2021
Zou Z, Ge J, Zheng H, Wu Y, Han C, Yao Z (2018) Encrypted traffic classification with a convolutional long short-term memory neural network. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th International conference on smart city; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, pp 329–334
Rezaei S, Liu X (2020) Multitask learning for network traffic classification. In: 2020 29th International conference on computer communications and networks (ICCCN). IEEE, pp 1–9
Rago A, Piro G, Boggia G, Dini P (2020) Multi-task learning at the mobile edge: An effective way to combine traffic classification and prediction. IEEE Transactions on Vehicular Technology 69(9):10362–10374
Cheng J, He R, Yuepeng E, Wu Y, You J, Li T (2020) Real-time encrypted traffic classification via lightweight neural networks. In: GLOBECOM 2020-2020 IEEE global communications conference. IEEE, pp 1–6
Ren X, Gu H, Wei W (2021) Tree-rnn: Tree structural recurrent neural network for network traffic classification. Expert Systems with Applications 167:114363
Xie G, Li Q, Jiang Y (2021) Self-attentive deep learning method for online traffic classification and its interpretability. Computer Networks 196:108267
Guo L, Wu Q, Liu S, Duan M, Li H, Sun J (2020) Deep learning-based real-time vpn encrypted traffic identification methods. Journal of Real-Time Image Processing 17(1):103–114
Dong C, Zhang C, Lu Z, Liu B, Jiang B (2020) Cetanalytics: Comprehensive effective traffic information analytics for encrypted traffic classification. Computer Networks 176:107258
Zhang J, Xiang Y, Wang Y, Zhou W, Xiang Y, Guan Y (2012) Network traffic classification using correlation information. IEEE Transactions on Parallel and Distributed systems 24(1):104–117
Johnson JM, Khoshgoftaar TM (2019) Survey on deep learning with class imbalance. Journal of Big Data 6(1):1–54
Wang P, Li S, Ye F, Wang Z, Zhang M (2020b) Packetcgan: Exploratory study of class imbalance for encrypted traffic classification using cgan. In: ICC 2020-2020 IEEE international conference on communications (ICC). IEEE, pp 1–7
Soleymanpour S, Sadr H, Soleimandarabi MN (2021) Cscnn: Cost-sensitive convolutional neural network for encrypted traffic classification. Neural Process Lett :1–27
Draper-Gil G, Lashkari AH, Mamun MSI, Ghorbani AA (2016) Characterization of encrypted and vpn traffic using time-related. In: Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), pp 407–414
Caruana R (1997) Multitask learning. Machine Learning 28(1):41–75
Vu L, Bui CT, Nguyen QU (2017) A deep learning based method for handling imbalanced problem in network traffic classification. In: Proceedings of the eighth international symposium on information and communication technology, pp 333–339
Gómez SE, Hernández-Callejo L, Martínez BC, Sánchez-Esguevillas AJ (2019) Exploratory study on class imbalance and solutions for network traffic classification. Neurocomputing 343:100–119
Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv:16070 6450. Accessed 21 July 2016
Guo M, Haque A, Huang DA, Yeung S, Fei-Fei L (2018) Dynamic task prioritization for multitask learning. In: Proceedings of the european conference on computer vision (ECCV), pp 270–287
Liu S, Johns E, Davison AJ (2019) End-to-end multi-task learning with attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1871–1880
Hu C, He S, Wang Y (2021) A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Applied Intelligence 51(4):2609–2621
Acknowledgements
This work was partially supported by the National Key Research and Development Program under Grant 2019YFB1804003.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zheng, W., Zhong, J., Zhang, Q. et al. MTT: an efficient model for encrypted network traffic classification using multi-task transformer. Appl Intell 52, 10741–10756 (2022). https://doi.org/10.1007/s10489-021-03032-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-021-03032-8