ABSTRACT
Traffic classification (TC) is very important as it can provide useful information which can be used in the flexible management of the network. However, TC has become more and more complicated because of the emergence of various network applications and techniques. In this paper, we apply deep learning based method to the classification of four different kinds of media traffic, i.e., audio, picture, text and video. We collect traffic data from the real network environment. Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) based traffic classification methods are designed to accurately classify the target traffic into different categories. We found that MLP has very good performance in most scenarios. Moreover, specific architecture can reduce the training time of the neural network in the classification.
- R. Aggarwal and Nanhay Singh. 2017. A New Hybrid Approach for Network Traffic Classification Using Svm and Naïve Bayes Algorithm.Google Scholar
- Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey. 2015. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology 33, 8 (2015), 831.Google Scholar
- Mina Tahmasbi Arashloo, Yaron Koral, Michael Greenberg, Jennifer Rexford, and David Walker. 2016. SNAP: Stateful network-wide abstractions for packet processing. In Proceedings of the 2016 ACM SIGCOMM Conference. ACM, 29--43. Google ScholarDigital Library
- Tom Auld, Andrew W Moore, and Stephen F Gull. 2007. Bayesian neural networks for internet traffic classification. IEEE Transactions on neural networks 18, 1 (2007), 223--239. Google ScholarDigital Library
- Alberto Dainotti, Antonio Pescape, and Kimberly C Claffy. 2012. Issues and future directions in traffic classification. IEEE network 26, 1 (2012). Google ScholarDigital Library
- Pieter Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein. 2005. A tutorial on the cross-entropy method. Annals of operations research 134, 1 (2005), 19--67.Google ScholarCross Ref
- Cicero dos Santos and Maira Gatti. 2014. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 69--78.Google Scholar
- Alessandro Finamore, Marco Mellia, Michela Meo, and Dario Rossi. 2010. Kiss: Stochastic packet inspection classifier for udp traffic. IEEE/ACM Transactions on Networking (TON) 18, 5 (2010), 1505--1515. Google ScholarDigital Library
- Michael Finsterbusch, Chris Richter, Eduardo Rocha, Jean-Alexander Muller, and Klaus Hanssgen. 2014. A survey of payload-based traffic classification approaches. IEEE Communications Surveys & Tutorials 16, 2 (2014), 1135--1156.Google ScholarCross Ref
- Open Networking Fundation. 2012. Software-defined networking: The new norm for networks. ONF White Paper 2 (2012), 2--6.Google Scholar
- Bo Han, Vijay Gopalakrishnan, Lusheng Ji, and Seungjoon Lee. 2015. Network function virtualization: Challenges and opportunities for innovations. IEEE Communications Magazine 53, 2 (2015), 90--97.Google ScholarDigital Library
- Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feed-forward networks are universal approximators. Neural networks 2, 5 (1989), 359--366. Google ScholarDigital Library
- Nan Hua, Haoyu Song, and TV Lakshman. 2009. Variable-stride multi-pattern matching for scalable deep packet inspection. In INFOCOM 2009, IEEE. Citeseer, 415--423.Google ScholarCross Ref
- IANA. {n. d.}. https://www.iana.org/. ({n. d.}).Google Scholar
- T Jayalakshmi and A Santhakumaran. 2011. Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering 3, 1 (2011), 1793--8201.Google Scholar
- Chengjun Jia, Zhe Fu, Xiaohe Hu, Shui Cao, Liang Wang, and Jun Li. 2018. Multi-core HTB for bandwidth sharing. In Proceedings of the 2018 Symposium on Architectures for Networking and Communications Systems. ACM, 169--171. Google ScholarDigital Library
- Thomas Karagiannis, Andre Broido, Nevil Brownlee, Kimberly Claffy, and Michalis Faloutsos. 2003. File-sharing in the Internet: A characterization of P2P traffic in the backbone. University of California, Riverside, USA, Tech. Rep (2003).Google Scholar
- Hyang-Ah Kim and Brad Karp. 2004. Autograph: Toward Automated, Distributed Worm Signature Detection. In USENIX security symposium, Vol. 286. San Diego, CA. Google ScholarDigital Library
- Alok Kumar, Sushant Jain, Uday Naik, Anand Raghuraman, Nikhil Kasinad-huni, Enrique Cauich Zermeno, C Stephen Gunn, Jing Ai, Björn Carlin, Mihai Amarandei-Stavila, et al. 2015. BwE: Flexible, hierarchical bandwidth allocation for WAN distributed computing. In ACM SIGCOMM Computer Communication Review, Vol. 45. ACM, 1--14. Google ScholarDigital Library
- Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng. 2009. Convo-lutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th annual international conference on machine learning. ACM, 609--616. Google ScholarDigital Library
- Zhi Liu, Shijie Sun, Ju Xing, Zhe Fu, Xiaohe Hu, Jianwen Pi, Xiaofeng Yang, Yunsong Lu, and Jun Li. 2018. MN-SLA: a modular networking SLA framework for cloud management system. Tsinghua Science and Technology 23, 6 (2018), 635--644.Google ScholarCross Ref
- Zhi Liu, Xiang Wang, Weishen Pan, Baohua Yang, Xiaohe Hu, and Jun Li. 2015. To-wards efficient load distribution in big data cloud. In 2015 International Conference on Computing, Networking and Communications (ICNC). IEEE, 117--122.Google ScholarCross Ref
- Mohammad Lotfollahi, Ramin Shirali, Mahdi Jafari Siavoshani, and Mohammd-sadegh Saberian. 2017. Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning. arXiv preprint arXiv:1709.02656 (2017).Google Scholar
- Alok Madhukar and Carey Williamson. 2006. A longitudinal study of P2P traffic classification. In Modeling, Analysis, and Simulation of Computer and Telecom-munication Systems, 2006. MASCOTS 2006. 14th IEEE International Symposium on. IEEE, 179--188. Google ScholarDigital Library
- Alok Madhukar and Carey Williamson. 2006. A longitudinal study of P2P traffic classification. In Modeling, Analysis, and Simulation of Computer and Telecom-munication Systems, 2006. MASCOTS 2006. 14th IEEE International Symposium on. IEEE, 179--188. Google ScholarDigital Library
- Nick McKeown, Tom Anderson, Hari Balakrishnan, Guru Parulkar, Larry Pe-terson, Jennifer Rexford, Scott Shenker, and Jonathan Turner. 2008. OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communi-cation Review 38, 2 (2008), 69--74. Google ScholarDigital Library
- Andrew W Moore and Konstantina Papagiannaki. 2005. Toward the accurate identification of network applications. In International Workshop on Passive and Active Network Measurement. Springer, 41--54. Google ScholarDigital Library
- Andrew W Moore and Denis Zuev. 2005. Internet traffic classification using bayesian analysis techniques. In ACM SIGMETRICS Performance Evaluation Re-view, Vol. 33. ACM, 50--60. Google ScholarDigital Library
- T Nguyen and Grenville Armitage. 2006. Synthetic sub-flow pairs for timely and stable IP traffic identification. In Proc. Australian Telecommunication Networks and Application Conference.Google Scholar
- Salima Omar, Asri Ngadi, and Hamid H Jebur. 2013. Machine learning tech-niques for anomaly detection: an overview. International Journal of Computer Applications 79, 2 (2013).Google ScholarCross Ref
- Mohammad Reza Parsaei, Mohammad Javad Sobouti, Seyed Raouf Khayami, and Reza Javidan. 2017. Network traffic classification using machine learning techniques over software defined networks. International Journal of Advanced Computer Science and Applications 8, 7 (2017), 220--225.Google Scholar
- RK Rahul, T Anjali, Vijay Krishna Menon, and KP Soman. 2017. Deep learning for network flow analysis and malware classification. In International Symposium on Security in Computing and Communication. Springer, 226--235.Google ScholarCross Ref
- David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986. Learning representations by back-propagating errors. nature 323, 6088 (1986), 533.Google Scholar
- Muhammad Shafiq and Xiangzhan Yu. 2017. Effective packet number for 5G IM WeChat application at early stage traffic classification. Mobile Information Systems 2017 (2017).Google Scholar
- Yiyang Shao, Yibo Xue, and Jun Li. 2014. PPP: Towards parallel protocol parsing. China Communications 11, 10 (2014), 106--116.Google ScholarCross Ref
- Yiyang Shao, Baohua Yang, Jingjie Jiang, Yibo Xue, and Jun Li. 2014. Emilie: Enhance the power of traffic identification. In 2014 International Conference on Computing, Networking and Communications (ICNC). IEEE, 31--35.Google ScholarCross Ref
- Yiyang Shao, Luoshi Zhang, Xiaoxian Chen, and Yibo Xue. 2014. Towards time-varying classification based on traffic pattern. In 2014 IEEE Conference on Communications and Network Security. IEEE, 512--513.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Pu Wang, Shih-Chun Lin, and Min Luo. 2016. A framework for QoS-aware traffic classification using semi-supervised machine learning in SDNs. In 2016 IEEE International Conference on Services Computing (SCC). IEEE, 760--765.Google ScholarCross Ref
- Pan Wang, Feng Ye, Xuejiao Chen, and Yi Qian. 2018. Datanet: Deep learning based encrypted network traffic classification in sdn home gateway. IEEE Access 6 (2018), 55380--55391.Google ScholarCross Ref
- Zhanyi Wang. 2015. The applications of deep learning on traffic identification. BlackHat USA (2015).Google Scholar
- Baohua Yang, Guangdong Hou, Lingyun Ruan, Yibo Xue, and Jun Li. 2011. Smiler: Towards practical online traffic classification. In 2011 ACM/IEEE Seventh Sympo-sium on Architectures for Networking and Communications Systems. IEEE, 178--188. Google ScholarDigital Library
- Baohua Yang, Guodong Li, Yaxuan Qi, Yibo Xue, and Jun Li. 2010. DFC: Towards Effective Feedback Flow Management for Datacenters. In 2010 Ninth International Conference on Grid and Cloud Computing. IEEE, 98--103. Google ScholarDigital Library
- Changhe Yu, Julong Lan, JiChao Xie, and Yuxiang Hu. 2018. QoS-aware Traffic Classification Architecture Using Machine Learning and Deep Packet Inspection in SDNs. Procedia computer science 131 (2018), 1209--1216. Google ScholarDigital Library
- Zhenlong Yuan, Yibo Xue, and Yingfei Dong. 2013. Harvesting unique characteristics in packet sequences for effective application classification. In Communications and Network Security (CNS), 2013 IEEE Conference on. IEEE, 341--349.Google ScholarCross Ref
Index Terms
- Effective Media Traffic Classification Using Deep Learning
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