Guanglong Zhao
ABSTRACT
Deep learning techniques are frequently utilized and produce effective results in the classification of encrypted traffic. In the current encryption traffic classification process, the network traffic characteristics are not sufficiently extracted, which is a concern. An encrypted traffic classification model based on raw network traffic and its spatiotemporal characteristics is proposed in this paper. The raw network traffic is divided into sessions, and the packets inside each session are then split into 784-byte slices, and the traffic is then described using the slice data. The time feature vector and the spatial feature vector are then created by combining ResNet and GRU models to generate features from raw network data in parallel. The traffic is then classified using the combined features. According to experimental findings, the proposed model's recognition accuracy on the ISCX-NonVPN-VPN2016 dataset reached 99.36%, which is an improvement over other approaches currently in use.
- F. Pacheco, E. Exposito, M. Gineste, C. Baudoin and J. Aguilar, "Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey," in IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1988-2014, Secondquarter, 2019, doi: 10.1109/COMST.2018.2883147.Google Scholar
Cross Ref
- H. Yao, C. Liu, P. Zhang, S. Wu, C. Jiang and S. Yu, "Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory," in IEEE Transactions on Big Data, vol. 8, no. 1, pp. 241-252, 1 Feb. 2022, doi: 10.1109/TBDATA.2019.2940675.Google ScholarCross Ref
- Draper-Gil, G.; Lashkari, A.; Mamun, M. and A. Ghorbani, A. 2016. Characterization of Encrypted and VPN Traffic using Time-related Features. In Proceedings of the 2nd International Conference on Information Systems Security and Privacy - ICISSP, ISBN 978-989-758-167-0; ISSN 2184-4356, pages 407-414. DOI: 10.5220/0005740704070414.Google Scholar
- B. Yamansavascilar, M. A. Guvensan, A. G. Yavuz and M. E. Karsligil, "Application identification via network traffic classification," 2017 International Conference on Computing, Networking and Communications (ICNC), 2017, pp. 843-848, doi: 10.1109/ICCNC.2017.7876241.Google Scholar
- Habibi Lashkari, A.; Draper Gil, G.; Mamun, M. and Ghorbani, A. 2017. Characterization of Tor Traffic using Time based Features. In Proceedings of the 3rd International Conference on Information Systems Security and Privacy - ICISSP, ISBN 978-989-758-209-7; ISSN 2184-4356, pages 253-262. DOI: 10.5220/0006105602530262.Google Scholar
- E. Hodo, X. Bellekens, E. Iorkyase, A. Hamilton, C. Tachtatzis, and R. Atkinson, “Machine learning approach for detection of nontor traffic,” in the Proc. of the 12th International Conference on Availability, Reliability and Security, New York, NY, USA: ACM, 2017.Google ScholarDigital Library
- Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R. Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Comput 24, 1999–2012, 2020. https://doi.org/10.1007/s00500-019-04030-2.Google ScholarDigital Library
- W. Wang, M. Zhu, J. Wang, X. Zeng and Z. Yang, "End-to-end encrypted traffic classification with one-dimensional convolution neural networks," 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017, pp. 43-48, doi: 10.1109/ISI.2017.8004872.Google ScholarDigital Library
- Z. Zou, J. Ge, H. Zheng, Y. Wu, C. Han and Z. Yao, "Encrypted Traffic Classification with a Convolutional Long Short-Term Memory Neural Network," 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), 2018, pp. 329-334, doi: 10.1109/HPCC/SmartCity/DSS.2018.00074.Google Scholar
- M. Wang, K. Zheng, D. Luo, Y. Yang and X. Wang, "An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders," 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020, pp. 634-641, doi: 10.1109/ICCC51575.2020.9344978.Google Scholar
- A. Dainotti, A. Pescape and K. C. Claffy, "Issues and future directions in traffic classification," in IEEE Network, vol. 26, no. 1, pp. 35-40, January-February, 2012, doi: 10.1109/MNET.2012.6135854.Google ScholarDigital Library
- Wei W, Ming Z, Zeng X, Malware traffic classification using convolutional neural network for representation learning[C]// 2017 International Conference on Information Networking (ICOIN). IEEE, 2017.Google Scholar
Index Terms
- An encrypted traffic classification model based on the raw traffic and spatiotemporal characteristics
Recommendations
Encrypted Traffic Classification Using Graph Convolutional Networks
Advanced Data Mining and ApplicationsAbstractTraffic classification plays a vital role in the field of network management and network security. Because of the continuous evolution of new applications and services and the widespread use of encrypted communication technologies, it has become a ...
A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web
SIGMETRICS '21: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer SystemsTraffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging ...
Comments