Advertisement

Efficient Module for OHM (Online Hybrid Model)

  • Akash Agarwal
  • Nitin RakeshEmail author
  • Nitin Agarwal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)

Abstract

The Internet-based businesses are increasing day by day and even the new concept of digital India is developing, through which every government and private companies are switching to Internet and cloud services. Internet users are increasing day by day at State Bank of India and about 69% of daily transactions happen through alternative channels, including Internet, ATM, and mobile banking. This figure is rising every year and more young generation is using online services. But on the other hand, there are many security concerns as well; presently we have many secured transaction channels. In this paper, we have proposed the new algorithm to prevent frauds and track the transactions location if any fraud occurs in the Internet. Using Online Hybrid Model algorithm, we will generate unique Internet id of the user. This algorithm also supports in the prevention of fraudulent activities, for example, if terrorist do any transaction online, we can track easily.

Keywords

Online phantom transactions Online fraud detection OHM E-Commerce 

References

  1. 1.
    U.S. Commerce Department, Forrester Research, Internet Retailer, ComScore., www.statisticbrain.com/total-online-sales/.
  2. 2.
    Prasad, B.: Intelligent techniques for E-Commerce. J. Electron. Commer. Res. 4(2), 65–71(2003).Google Scholar
  3. 3.
    Kristin M. Finklea, “Identity Theft: Trends and Issues,” Congressional Research Service, A CRS research report for congress, 2012.Google Scholar
  4. 4.
    National Crime Prevention Council, http://www.ncpc.org/cmsupload/ncpc/File/aucfraud.pdf.
  5. 5.
  6. 6.
    Fei Donga, Sol M. Shatza and HaipingXub, “Combating Online In-Auction Fraud: Clues, Techniques and Challenges,” Computer Science Review, 3 (4) 245–258, 2009.Google Scholar
  7. 7.
    W.L. Wang, Z. Hidvègi, and A. B. Whinston, “Shill Bidding in English Auctions,” Technical report, Emory University, 2001, http://oz.stern.nyu.edu/seminar/fa01/1108.pdf.
  8. 8.
    National White Collar crime center: Report on Internet fraud, www.nw3c.org/docs/whitepapers/internet_fraud.pdf?sfvrsn=7, June 2008.
  9. 9.
    J. Trevathan and W. Read, “Detecting Collusive Shill Bidding,” Proc. of International Conference on Information Technology: New Generations, 799–808, 2007.Google Scholar
  10. 10.
    Porter, R., Shoham, Y., On cheating in sealed-bid auctions. J. Decis. Support Syst. Special issue of the fourth ACM Conference on Electronic Commerce, 39(1), 41–54 (2005).Google Scholar
  11. 11.
    U.S. Commerce Department: Forrester Research, Internet Retailer, ComScore., http://www.statisticbrain.com/total-online-sales/.
  12. 12.
    B. Prasad: “Intelligent Techniques for E-Commerce,” Journal of Electronic Commerce Research, 4 (2) 65–71, 2003.Google Scholar
  13. 13.
    W. L. Wang, Z. Hidvègi, and A.B. Whinston, “Shill Bidding in Multi-Round Online Auctions,” Proc. of the 35th Annual Hawaii International Conference on System Sciences, January 2002.Google Scholar
  14. 14.
    Fault tolerance based routing approach using WMN http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7361345.
  15. 15.
    AnkitMundra, Nitin Rakesh, (2013) “Online Hybrid Model for Online Fraud Prevention and Detection,” International Conference on Advance Computing, Networking, and Informatics–ICACNI-2013, Springer, pp. 805–815.Google Scholar
  16. 16.
    Wang, W.L., Hidvègi, Z., Whinston, A.B.: Shill Bidding in Multi-Round Online Auctions. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences, Jan 2002.Google Scholar
  17. 17.
    Trevathan, J., Read, W.: Detecting Collusive Shill Bidding. In: Proceedings of InternationalConference on Information Technology: New Generations, pp. 799–808 (2007).Google Scholar
  18. 18.
    Liang Zhang Jie Yang Belle Tseng, “Online Modeling of Proactive Moderation System for Auction Fraud Detection,” World Wide Web Conference (WWW), 669–678, 2012.Google Scholar
  19. 19.
    Srivastava, A., Kundu, A., Sural, S., Majumdar, A.K.: Credit card Fraud Detection using Hidden Markov model. IEEE Trans. Dependable Secure Computer5(1), 1062–1066 (2008).Google Scholar
  20. 20.
    SandeepPratap Singh, Shiv Shankar P. Shukla, Nitin Rakesh and VipinTyagi, “Problem reduction in online payment system using hybrid model,” International Journal of Managing Information Technology, 3(3) 62–71, August 2011.Google Scholar
  21. 21.
    Chui, K., Xwick, R.: Auction on the Internet: A Preliminary Study, http://repository.ust.hk/dspace/handle/1783.1/1035, July 2008.
  22. 22.
    S.O. Falki, B.K. Alese, O.S. Adewale, J.O. Ayeni, G.A. Aderounmu and W.O. Islamia, “Probablistic Credi Card Fraud Detection System in Online Transactions,” Interantional Journal of Software Engineering and Its Applications, Vol.6, No.4, 69–78, 2012.Google Scholar
  23. 23.
    S. P. Singh, S. S. P. Shukla, Nitin Rakesh,VipinTyagi., Problem reduction in online payment system using hybrid model. Int. J. Manag. Inf. Technol. 3(3), 62–71 (2011).Google Scholar
  24. 24.
    AbhinavSrivastava, AmlanKundu, S. Sural, A.K. Majumdar, “Credit Card Fraud Detection Using Hidden Markov Model,” IEEE Transactions on Dependable And Secure Computing, 5 (1) 1062–1066,2008.Google Scholar
  25. 25.
    Stephan Kovach, Wilson Vicente Ruggiero, “Online Banking Fraud Detection Based on Local and Global Behavior,” Proc. Of ICDS: The Fifth International Conference on Digital Society, 166–171, 2011.Google Scholar
  26. 26.
    Yungchang Ku, Yuchi Chen, Chaochang Chiu, “A Proposed Data Mining Approach for Internet Auction Fraud Detection,” Intelligence and Security Informatics Lecture Notes in Computer Science Volume 4430, 238–243, 2007.Google Scholar
  27. 27.
    Wang, W.L., Hidvègi, Z., Whinston, A.B.: Shill Bidding in English Auctions, Technical report, Emory University, http://oz.stern.nyu.edu/seminar/fa01/1108.pdf (2001).
  28. 28.
    Internet Crime Complain Center: Internet Crime Report, 2004–2011, http://www.ic3.gov/media/annualreports.aspx.
  29. 29.
    National White Collar crime center, Report on Internet fraud, June, 2008, www.nw3c.org/docs/whitepapers/internet_fraud.pdf?sfvrsn=7.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Amity School of Engineering and TechnologyNoidaIndia
  2. 2.Amity University Uttar PradeshNoidaIndia
  3. 3.KPMGBengaluruIndia

Personalised recommendations