Volume 3 Number 5 (Oct. 2013)
Home > Archive > 2013 > Volume 3 Number 5 (Oct. 2013) >
IJEEEE 2013 Vol.3(5): 365-369 ISSN: 2010-3654
DOI: 10.7763/IJEEEE.2013.V3.260

Mining Online Shopping Frauds—A Non-Data Mining Approach

Kenichi Yoshida, Kazuhiko Tsuda, Setsuya Kurahashi, and Hiroki Azuma
Abstract— With the growth of e-commerce, various schemes have emerged to defraud suppliers who offer services and sell goods over the Internet. The deferred payment system, which is a traditional Japanese business practice whereby customers do not pay until goods are received, facilitates online fraud. After receiving goods, fraudulent clients simply disappear and the supplier does not receive the payment. However, since the traditional deferred payment system is expected by honest customers, online shopping sites cannot eliminate this payment system, and consequently are vulnerable to this type of fraud. The conventional approach to detect online shopping fraud is the use of various data mining methods based on statistical analysis. However, outbreaks of new fraudulent clients create new samples that change the distribution of data and decrease the performance of data-mining-based fraud detection. In this study, we propose a new approach that does not rely primarily on data mining. The main characteristic of the proposed approach is the use of the nature of economic crimes. In addition, specific implementations to detect online shopping fraud are proposed. The application of the proposed approach in other areas, such as spam filtering and Internet virus detection, is also discussed.

Index Terms— Online shopping, fraud detection, forged clients, data mining, economic crime.

Kenichi Yoshida, Kazuhiko Tsuda, and Setsuya Kurahashi are with Graduate School of Business Science, University of Tsukuba, Otsuka 3-29-1, Bunkyo, Tokyo 112-0012, Japan (e-mail: yoshida@gssm.otsuka.tsukuba.ac.jp, tsuda@gssm.otsuka.tsukuba.ac.jp, kurahashi.setsuya.gf@u.tsukuba.ac.jp).
Hiroki Azuma is with HAZS Corporation. Kojimachi6-2-6, Chiyoda-ku, Tokyo 102-0083, Japan (hirokiazuma2002@hazs.biz).

Cite: Kenichi Yoshida, Kazuhiko Tsuda, Setsuya Kurahashi, and Hiroki Azuma, " Mining Online Shopping Frauds—A Non-Data Mining Approach," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 3, no. 5, pp. 365-369, 2013.

General Information

ISSN: 2010-3654 (Online)
Abbreviated Title: Int. J. e-Educ. e-Bus. e-Manag. e-Learn.
Frequency: Quarterly
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, EI (INSPEC, IET)
E-mail: ijeeee@iap.org
  • May 14, 2019 News!

    Vol.7, No.4-Vol.8, No.2 have been indexed by EI (Inspec).   [Click]

  • Jul 01, 2020 News!

     IJEEEE Vol 10, No 3 is available online!   [Click]

  • Apr 03, 2020 News!

    IJEEEE Vol 10, No 2 is available online!    [Click]

  • Apr 01, 2020 News!

    The papers published in Vol 10, No 1 have all received dois from Crossref

  • Jan 09, 2020 News!

    IJEEEE Vol 10, No 1 is available online!    [Click]

  • Read more>>