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SCIENTIA SINICA Informationis, Volume 51 , Issue 1 : 56(2021) https://doi.org/10.1360/SSI-2019-0213

Network traffic classification method based on improved deep convolutional neural network

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  • ReceivedSep 27, 2019
  • AcceptedMar 7, 2020
  • PublishedDec 25, 2020

Abstract


Funded by

国家重点研发计划(2018YFB1800600)


References

[1] Hao F, Kodialam M, Lakshman T V. Fast Dynamic Multiple-Set Membership Testing Using Combinatorial Bloom Filters. IEEE/ACM Trans Networking, 2012, 20: 295-304 CrossRef Google Scholar

[2] Sen S, Spatscheck O, Wang D M. Accurate, scalable in-network identification of P2P traffic using application signatures. In: Proceedings of the 13th International Conference World Wide Web Conference, Florham Park, 2004. 512--521. Google Scholar

[3] Moore A W, Papagiannaki K. Toward the accurate identification of network applications. In: Proceedings of the 6th International Workshop on Passive and Active Network Measurement, Cambridge, 2005. 41--54. Google Scholar

[4] Ertam F, Avc? E. A new approach for internet traffic classification: GA-WK-ELM. Measurement, 2017, 95: 135-142 CrossRef Google Scholar

[5] Naoum R S, Abid N A, Al-Sultani Z N. An enhanced resilient backpropagation artificial neural network for intrusion detection system. Int J Comput Sci Netw Secur, 2012, 12: 11--16. Google Scholar

[6] Naoum R S, Al-Sultani Z N. Learning vector quantization (LVQ) and knearest neighbor for intrusion classification. World Comput Sci Inf Technol J, 2012, 2: 105--109. Google Scholar

[7] Aburomman A A, Ibne Reaz M B. A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems. Inf Sci, 2017, 414: 225-246 CrossRef Google Scholar

[8] Eid H F, Darwish A, Ella H A, et al. Principle components analysis and support vector machine based intrusion detection system. In: Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, Cairo, 2010. 363--367. Google Scholar

[9] Kuang F, Xu W, Zhang S. A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Computing, 2014, 18: 178-184 CrossRef Google Scholar

[10] Rastegari S, Hingston P, Lam C P. Evolving statistical rulesets for network intrusion detection. Appl Soft Computing, 2015, 33: 348-359 CrossRef Google Scholar

[11] He J, Zhao L. Research on P2P traffic classification based on PCA-Probabilistic neural network. Comput Dev Appl, 2011, 7: 1--3. Google Scholar

[12] Valenti S, Rossi D, Dainotti A, et al. Reviewing Traffic Classification. Berlin: Springer, 2013. Google Scholar

[13] Pan W B, Cheng G, Guo X J, et al. Review and perspective on encrypted traffic identification research. J Commun, 2016, 37: 1--14. Google Scholar

[14] Yang Y, Kang C C, Gou G P, et al. TLS/SSL encrypted traffic classification with autoencoder and convolutional neural network. In: Proceedings of the 20th HPCC/16th SMARTCITY/4th DSS, Beijing, 2018. 362--369. Google Scholar

[15] Xiong g, Zhao Y, Cao Z G. Real-time classification for encrypted P2P traffic based on host behavior association. Chinese High Technol Lett, 2013, 23: 1008--1015. Google Scholar

[16] Karagiannis T, Broido A, Brownlee N, et al. Is P2P dying or just hiding? In: Proceedings of IEEE Global Telecommunications Conference, San Diego, 2004. 1532--1538. Google Scholar

[17] Aceto G, Dainotti A, De Donato W, et al. PortLoad: taking the best of two worlds in traffic classification. In: Proceedings of INFOCOM IEEE Conference on Computer Communications Workshops, Naples, 2010. Google Scholar

[18] Khakpour A R, Liu A X. High-speed flow nature identification. In: Proceedings of IEEE International Conference on Distributed Computing Systems, Montreal, 2009. 510--517. Google Scholar

[19] Shi H, Li H, Zhang D. Efficient and robust feature extraction and selection for traffic classification. Comput Networks, 2017, 119: 1-16 CrossRef Google Scholar

[20] de la Hoz E, de la Hoz E, Ortiz A, et al. PCA filtering and probabilistic SOM for network intrusion detection. In: Proceedings of the 12th International Work-Conference on Artificial Neural Networks (IWANN), Puerto de la Cruz, 2015. 71--81. Google Scholar

[21] Agrawal S, Sohi B S. Off-line analysis of internet traffic for accurate identification of P2P applications using neural networks. In: Proceedings of the 1st International Conference on Recent Advances in Information Technology (RAIT), Chandigarh, 2012. 431--435. Google Scholar

[22] Dong S, Li R. Traffic identification method based on multiple probabilistic neural network model. Neural Comput Applic, 2019, 31: 473-487 CrossRef Google Scholar

[23] Ertam F, Galip A. Data classification with deep learning using tensorflow. In: Proceedings of the 2nd International Conference on Computer Science and Engineering, Elazig, 2017. 755--758. Google Scholar

[24] Peng L, Yang B, Chen Y. Effective packet number for early stage internet traffic identification. Neurocomputing, 2015, 156: 252-267 CrossRef Google Scholar

[25] Wang Y, Zhou H Y, Feng H, et al. Network traffic classification method based on improved capsule neural network. J Commun, 2018, 1: 14--23. Google Scholar

[26] Jain A V. Network traffic identification with convolutional neural networks. In: Proceedings of the 16th DASC/16th PICom/ 4th DataCom/ 3rd CyberSciTec, Rochester, 2018. 1001--1007. Google Scholar

[27] Lopez-Martin M, Carro B, Sanchez-Esguevillas A. Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things. IEEE Access, 2017, 5: 18042-18050 CrossRef Google Scholar

[28] Hoque N, Bhattacharyya D K, Kalita J K. An alert analysis approach to DDoS attack detection. In: Proceedings of International Conference on Accessibility to Digital World, Assam, 2016. 33--38. Google Scholar

[29] Sze V, Chen Y H, Yang T J, et al. Efficient processing of deep neural networks: a tutorial and survey. In: Proceedings of the IEEE, Cambridge, 2017. 2295--2329. Google Scholar

[30] Sun G, Liang L, Chen T. Network traffic classification based on transfer learning. Comput Electrical Eng, 2018, 69: 920-927 CrossRef Google Scholar

[31] Deng X G, Tian X M, Chen S, et al. Deep learning based nonlinear principal component analysis for industrial process fault detection. In: Proceedings of International Joint Conference on Neural Networks, Qingdao, 2017. 1237--1243. Google Scholar

[32] Dias K L, Pongelupe M A, Caminhas W M. An innovative approach for real-time network traffic classification. Comput Networks, 2019, 158: 143-157 CrossRef Google Scholar

[33] Radford B J, Richardson B D, Davis S E. Sequence aggregation rules for anomaly detection in computer network traffic. Comput Sci, 2018, 8: 1--5. Google Scholar

[34] Aurélien G. Hands-on Machine Learning with Scikit-learn $\&$ Tensorflow. Beijing: China Machine Press, 2018. Google Scholar

[35] Aceto G, Ciuonzo D, Montieri A. Multi-classification approaches for classifying mobile app traffic. J Network Comput Appl, 2018, 103: 131-145 CrossRef Google Scholar

[36] Kornycky J, Abdul-Hameed O, Kondoz A. Radio Frequency Traffic Classification Over WLAN. IEEE/ACM Trans Networking, 2017, 25: 56-68 CrossRef Google Scholar

[37] Zhou F Y, Jin L P, Dong J. Review of convolution neural network. Chinese J Comput, 2017, 6: 1229--1251. Google Scholar

[38] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60: 84-90 CrossRef Google Scholar

[39] Gao Y C, Liu N H, Zhang S. Relative indexed compressed sparse filter encoding format for hardware-oriented acceleration of deep convolutional neural networks. In: Proceedings of the 7th IEEE International Symposium on next-generation Electronics (ISNE), Taipei, 2018. 323--326. Google Scholar