Now a days internet traffic classification is an emerging research field since 1990’s because of its use in a large number of network activities. Traditional techniques of internet traffic classification that relied on well known TCP/UDP port numbers or payload based are rarely used because of use of dynamic port numbers instead of fixed port numbers and due to various cryptographic techniques which inhibit inspection of packet payload. Recent trends are use of ML (machine learning) algorithms for internet traffic classification. In our research work we propose a technique to classify the internet traffic into two classes, one for educational websites and another for non-educational websites. In educational institutes for the optimum use of network resources and for the welfare of the students, the use of non-educational websites should be banned while only the educational websites should be allowed to open. To classify the internet traffic we propose a technique to capture data packets first, related with various educational and non-educational websites, using a packet capturing tool Wireshark. Then using feature selection algorithm, a reduced feature dataset will be developed. After that training and testing of various ML algorithms will have to be performed. Finally comparative analysis of the different classifiers from the obtained results is to be performed.

1. Arthur Callado, Carlos Kamienski, Géza Szabó, Balázs Péter Ger˝o, Judith Kelner,Stênio Fernandes ,and Djamel Sadok, “A Survey on Internet Traffic Identification,” IEEE Communications Survey & tutorials, vol. 11, no. 3, pp. 37-52, Third Quarter 2009.
2. Thuy T.T. Nguyen and Grenville Armitage, “A Survey of Techniques for Internet Traffic Classification using Machine Learning,” IEEE Communications Survey & tutorials, vol. 10, no. 4, pp. 56-76, Fourth Quarter 2008.
3. Runyuan Sun, Bo Yang, Lizhi Peng, Zhenxiang Chen, Lei Zhang, and Shan Jing, “Traffic Classification Using Probabilistic Neural Network,” in Sixth International Conference on Natural Computation (ICNC 2010), 2010, pp. 1914-1919.
4. http:/www.iana.org/assignments/port numbers.
5. Andrew W. Moore, Denis Zuev, Michael L. Crogan, “Discriminators for use in flow-based classification,” Queen Mary University of London, Department of Computer Science, RR-05-13, August 2005.
6. Hyunchul Kim, kc claffy, Marina Fomenkov, Dhiman Barman, Michalis Faloutsos, and KiYoung Lee, “Internet Traffic Classification Demystified: Myths, Caveats, and the Best Practices,” in ACM CoNEXT 2008, December 10-12, 2008, Madrid, SPAIN.
7. A. Madhukar and C. Williamson. “A Longitudinal Study of P2P Traffic Classification.” In MASCOTS'06, Monterey, USA, August 2006.
8. A.W.Moore and D.papagiannaki, “Toward the accurate Identification of network applications”, in poc. 6th passive active measurement. Workshop (PAM), mar 2005, Vol.3431, pp 41-54.
9. C. Dews, A. Wichmann, and A. Feldmann. “An Analysis of Internet Chat Systems” In IMC'03, Miami Beach, USA, October 2003.
10. S. Zander, T. Nguyen, and G. Armitage. “Automated Traffic Classification and Application Identification using Machine Learning”. In LCN'05, Sydney, Australia, November 2005.
11. S. Zander, T. Nguyen, and G. Armitage. “Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics”. In PAM'05, Boston, USA, March 2005.
12. Amina Lyhyaoui, “Support Vector Machine for Internet Traffic Identification,” in IEEE, 2007,pp. 351-354.
13. Murat Soysal, and Ece Guran Schmidt, “Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison,” Performance Evaluation Elsevier Journal, Vol. 67, 2010, pp. 451-467.
14. Indra Bhan Arya, and Rachna Mishra, “Internet Traffic Classification: An Enhancement in Performance using Classifiers Combination,” International Journal of Computer Science and Information Technologies, Vol. 2 (2), 2011, pp. 663-667.
15. Shijun Huang Kai Chen Chao Liu, Alei Liang, Haibing Guan, “A Statistical-Feature-Based Approach to Internet Traffic Classification Using Machine Learning” 9781-4244-3941-6/09/$25.00 ©2009 IEEE
16. Yongli Ma, Zongjue Qian, Guochu Shou, Yihong Hu“Study on Preliminary Performance of Algorithms for Network Traffic Identification” 978-0-7695-3336-0/08 $25.00 © 2008 IEEE DOI 10.1109/CSSE.2008.1277
17. Kuldeep Singh and Sunil Agrawal, Comparative Analysis of five Machine Learning Algorithms for IP Traffic Classification, Internation Conference on Emerging Trends in Networks and Computrt Communications (ENCTT-2011), Udaipur, Rajasthan, India, April 22-24, 2011.
18. Wireshark,Available:http://www.wireshark.org/
19. MATLAB,Available:www.mathworks.com
20. Wekawebsite, Available:http://www.cs.waikato.ac.nz/ml/Weka/

Machine Learning Features