Traffic Identification in Semi-known Network Environment

Network traffic classification has attracted more and more attentions from both academia and industry. It has been widely adopted in network management and security, such as QoS measurements. Due to rapid emergence of new applications in current network environment, it is impractical for a classification system to obtain full knowledge of a network environment. A big challenge to the identification of interested traffic comes from semi-known network environment, in which some emerging applications are not recognized by the classification system yet. In this paper, we proposed a new framework of Traffic Identification with Unknown Discovery (TIUD) by innovatively combining supervised and unsupervised machine learning techniques to meet the challenge. The proposed TIUD framework has the capability to accurately identify the interested traffic in semi-known network environment. The proposed framework is fully evaluated on a large real-world traffic dataset, with a comparison with three state-of-the-art traffic classification methods. The experimental results yield a outstanding performance of the proposed framework.

[1]  Andrew W. Moore,et al.  Bayesian Neural Networks for Internet Traffic Classification , 2007, IEEE Transactions on Neural Networks.

[2]  Jun Zhang,et al.  A novel semi-supervised approach for network traffic clustering , 2011, 2011 5th International Conference on Network and System Security.

[3]  Philippe Owezarski,et al.  MINETRAC: Mining flows for unsupervised analysis & semi-supervised classification , 2011, 2011 23rd International Teletraffic Congress (ITC).

[4]  Sebastian Zander,et al.  Automated traffic classification and application identification using machine learning , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[5]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[6]  Maurizio Dusi,et al.  Traffic classification through simple statistical fingerprinting , 2007, CCRV.

[7]  Grenville J. Armitage,et al.  A survey of techniques for internet traffic classification using machine learning , 2008, IEEE Communications Surveys & Tutorials.

[8]  Michalis Faloutsos,et al.  Internet traffic classification demystified: myths, caveats, and the best practices , 2008, CoNEXT '08.

[9]  Carey L. Williamson,et al.  Offline/realtime traffic classification using semi-supervised learning , 2007, Perform. Evaluation.

[10]  T. Karagiannis,et al.  Challenges in Network Application Identification , 2012 .

[11]  Luca Salgarelli,et al.  Support Vector Machines for TCP traffic classification , 2009, Comput. Networks.

[12]  Zhi-Li Zhang,et al.  A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks , 2012, TKDD.

[13]  J. Erman,et al.  QRP05-4: Internet Traffic Identification using Machine Learning , 2006, IEEE Globecom 2006.

[14]  George Varghese,et al.  Graption: A graph-based P2P traffic classification framework for the internet backbone , 2011, Comput. Networks.

[15]  Renata Teixeira,et al.  Traffic classification on the fly , 2006, CCRV.

[16]  Anirban Mahanti,et al.  Traffic classification using clustering algorithms , 2006, MineNet '06.

[17]  Yu Wang,et al.  Semi-supervised Encrypted Traffic Classification Using Composite Features Set , 2012, J. Networks.

[18]  Jun Zhang,et al.  Network Traffic Classification Using Correlation Information , 2013, IEEE Transactions on Parallel and Distributed Systems.

[19]  Antonio Pescapè,et al.  Issues and future directions in traffic classification , 2012, IEEE Network.

[20]  Anthony McGregor,et al.  Flow Clustering Using Machine Learning Techniques , 2004, PAM.

[21]  Judith Kelner,et al.  Better network traffic identification through the independent combination of techniques , 2010, J. Netw. Comput. Appl..

[22]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[23]  Dario Rossi,et al.  Revealing skype traffic: when randomness plays with you , 2007, SIGCOMM '07.

[24]  Andrew W. Moore,et al.  Internet traffic classification using bayesian analysis techniques , 2005, SIGMETRICS '05.

[25]  Renata Teixeira,et al.  Early Recognition of Encrypted Applications , 2007, PAM.

[26]  Matthew Roughan,et al.  Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification , 2004, IMC '04.

[27]  Carey L. Williamson,et al.  Identifying and discriminating between web and peer-to-peer traffic in the network core , 2007, WWW '07.

[28]  Jeffrey Erman,et al.  Internet Traffic Identification using Machine Learning , 2006 .

[29]  Sebastian Zander,et al.  A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification , 2006, CCRV.

[30]  Xenofontas A. Dimitropoulos,et al.  Classifying internet one-way traffic , 2012, SIGMETRICS '12.

[31]  Michalis Faloutsos,et al.  SubFlow: Towards practical flow-level traffic classification , 2012, 2012 Proceedings IEEE INFOCOM.

[32]  Sebastian Zander,et al.  Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic , 2012, IEEE/ACM Transactions on Networking.