iosr-JCE   Volume-10 ~ Issue-3

Abstract: Typically the traffic through the network is heterogeneous and it flows from multiple utilities and applications Considering todays threats in network there is yet not a single solution to solve all the issues because the traditional methods of port-based and payload-based with machine learning algorithm suffers from dynamic ports and encrypted application.Many international network equipment manufactures like cisco, juniper also working to reduce these issues in the hardware side.Here this paper presents a new approach considering the idea based on SOTC.This method adapts the current approaches with new idea based on service-oriented traffic classification(SOTC) and it can be used as an efficient alternate to existing methods to reduce the false positive and false negative traffic and to reduce computation and memory requirements.By evaluating the results on real traffic it confirm that this method is effective in improving the accuracy of traffic classification considerably,and promise to suits for a large number of applications.Finally, it is also possible to adopt a service database built offline, possibly provided by a third party and modeled after the signature database of antivirus programs,which in term reduce the work of training procedure and overfitting of parameters in case of parameteric classifier of supervised traffic classification.

Index Terms—Network operations, traffic classification, security.

[1] Cisco: a network device manufacturer. http://www.cisco.com

[2] J. Bellardo and S. Savage. 802.11 denial-of-service attacks: Real vulnerabilities and practical solutions. In Proceedings of the 11th USENIX Security Symposium, pages 15–28, Washington D.C, USA, 2003.

[3] A.Patwardhan, J.Parker, M.Iorga, A. Joshi, T.Karygiannis and Y.Yesha ―Threshold-based Intrusion Detection in Adhoc Networks and Secure AODV‖Ad Hoc Networks Journal (ADHOCNET), June 2008

[4] S. Sen, O. Spatscheck, D. Wang. Accurate, scalable in-network identification of p2p traffic using application signatures. Proceedings of World Wide Web Conference, pp. 512-521 NY, USA, May 2004.

[5] P. Haffner, S. Sen, O. Spatscheck, D. Wang, D. 2005. ACAS: automated construction of application signatures. In Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, pp. 197-202, Philadelphia, USA, August 2005.

[6] F. Risso, A. Baldini, M. Baldi, P. Monclus, O. Morandi. Lightweight, Session-Based Traffic Classification. Proceedings of the IEEE International Conference on Communications (ICC 2008) - Advances in Networks & Internet Symposium, Beijing, China, May 2008. [7] F. Risso, A. Baldini, F. Bonomi. Extending the NetPDL Language to Support Traffic Classification. In Proceedings of IEEE Globecom 2007, Washington, D.C, USA, November 2007.

[8] G.Varghese, J.A. Fingerhut, F. Bonomi. Detecting Evasion Attacks at High Speeds without Reassembly. Proceedings of ACM SIGCOMM 2006, Pisa, Italy, September 2006.

[9] J. Erman, A. Mahanti, M. Arlitt. Traffic Classification using Clustering Algoritms. Proceedings ACM SIGCOMM Workshop on Mining Network Data (MineNet 06), Pisa, Italy, September 2006.

[10] J. Erman, A. Mahanti, M. Arlitt, C. Williamson. Identifying and Discriminating Between Web and Peer-to-Peer traffic in the Network Core. Proceedings of the 16th International World Wide Web Conference (WWW), pp. 883-892, Banff, Canada, May 2007.

Abstract: Consumer's perception regarding shopping has been changed with the introduction of internet media. Retail industry has witnessed major revolution in the changing technology oriented business scenario of 21st century in India. Internet has shrunk the entire World. The rules of the game in retailing are fast changing with the introduction of Information Technology. The e-Retailing website is the front door of the online store that interacts between the e-retailer and consumers. The electronic retailing (e-Tailing, e-Retailing, internet retailing etc.) is the model of selling of retail goods using electronic media, in particular, the internet. E-Retailing is a subset of e-Commerce (Electronic Commerce). E-Retailing accounts for about 10% of the overall growth of e-Commerce market. The growth in the e-Retailing market is driven by the need to save time by urban India. It is estimated that 2.5 billion internet users, access to internet has played a significant role in growing the business markets. The Internet gives retailers an instrument for: broadening target markets, enhancing consumer relationships, extending product lines, improving cost efficiency, improving consumer communications, and delivering customized offers. Changing demographics (youthful India), changing lifestyles and exposure to the developed markets give a fillip to e-Retailing industry. One can buy anything from stereos to iPod's without stepping out through internet media. E-Retailers serve 24 hours x 7 days in a hassle free manner to consumers. Along with advantages of e-Retailing some major issues are associated with e-Retailing such as lack of personal touch; cyber crime; bargaining is not possible and e-illiteracy among rural India. But with all, we can say that Prospect of e-Retailing market is bright in India. Consumer's cognizance; internet literacy of consumer and wider use of internet with cyber security are some of the noteworthy factors which are vital for the sustainable development and growth of e-Retailing in India.

Key Words: Consumer Satisfaction, e-Retailing, e-Tailing, Information Technology, Online Retailing

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[3] Zeithaml, V.A. (2002): ―Service excellent in electronic channels‖, Managing Service Quality, Vol. XII, No.3, pp.135-138

[4] Wolfinbarger, M. & Gilly, M (2003) ―etailQ: Dimensionalzing, Measuring and Predicting etail Quality‖, Journal of Retailing, Vol.LXXIX, No.3, pp.183-198.

[5] Mohanty, A.K.& Panda, J. (2008), Retailing in India: Challenges and Opportunities, The Orissa Journal of Commerce, Vol. XXIX, No.2, Bhubaneswar, July, pp. 69-79.

[6] Goswami, Shubham & Mathur, Meera (2011), Retail goes Online- An Indian Perspective‖, IJMT, Volume XIX, Number 2, July - December 2011, pp. 1-11.

[7] Manish, Dwivedi; Kumawat, Mahesh & Verma Sanjeev (2012), ― Online Retailing in India : Opportunities and Challenges‖, International Journal of Engineering and Management Sciences, Vol.III, No.3, December, pp.336-338
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[9] [Online] E-RETAILING , www.thehindubusinessline.in/praxis/pr0301/03010440.pdf [Accessed on 1 February, 2013]
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Abstract: Micro array technology is one of the important biotechnological means that allows recording the expression levels of thousands of genes simultaneously within a number of different samples. An important application of micro array gene expression data is to classify samples according to their gene expression profiles. The gene expression dataset can be represented by an expression table, where each row corresponds to one particular gene, each column to a sample. The relevance of each attribute (attribute represents the gene expression conversion into numerical values) with respect to the class label and the redundancy between two attributes in terms of mutual information are calculated using supervised similarity measure. The proposed system uses supervised attribute clustering algorithm which determines the relevance of each attribute and growing the cluster around each relevant attribute by adding one attribute after the other. Min-hash algorithm is used to reduce the redundancy between the genes and also reduce the cluster size. The performance of the system can be improved by reducing the redundancy of genes.

Key words: Micro array, Gene expression, Mutual Information, Attribute Clustering, Supervised methods

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[4] Shuanhu Wu, Alan Wee-Chung Liew, Member, IEEE, Hong Yan, Senior Member, IEEE, and Mengsu Yang, "Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning" IEEE Trans on information technology in bio-medical, vol. 8, no. 1, march 2004.
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[7] W. Haiying, Z. Huiru, and A. Francisco, "Poisson-Based Self- Organizing Feature Maps and Hierarchical Clustering for Serial Analysis of Gene Expression Data," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 2,pp.163-175,Apr.-June 2007.
[8] J. Li, H. Su, H. Chen, and B.W. Futscher, "Optimal Search-Based Gene Subset Selection for Gene Array Cancer Classification," IEEE Trans. Information Technology in Biomedicine, vol. 11, no. 4, pp. 398-405, July 2007.
[9] P. Maji, "f-Information Measures for Efficient Selection of Discriminative Genes from Microarray Data," IEEE Trans. Biomedical Eng., vol. 56, no. 4
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