Protecting E-Commerce Systems From Online Fraud

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{PhaniAlekhya:2013:IJCTT,
  author =       "P. PhaniAlekhya and Sk. Mahaboob Basha",
  title =        "Protecting {E}-Commerce Systems From Online Fraud",
  journal =      "International Journal of Computer Trends and
                 Technology",
  year =         "2013",
  volume =       "4",
  number =       "10",
  pages =        "3542--3554",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, credit card
                 fraud detection, fraud detection techniques,
                 e-commerce",
  ISSN =         "2231-2803",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:5319e55b28528c16d3d99a2dd88a4610",
  publisher =    "Seventh Sense Research Group",
  URL =          "http://ijcttjournal.org/archives/ijctt-v4i10p132",
  broken =       "http://www.ijcttjournal.org/volume-4/issue-10/IJCTT-V4I10P132.pdf",
  size =         "6 pages",
  abstract =     "Due to the advent of Internet technologies, E-commerce
                 widely adapted mode of business in modern times. With
                 the growth of E-commence domain credit card usage has
                 become a common phenomenon. This has given chance to
                 adversaries to commit fraud. In the real world, there
                 were plenty of instances of fraud cases. It has its
                 impact on financial outfits that issue credit cards,
                 the E-commerce business entities and also the customers
                 of the E-commerce applications. To overcome this
                 problem and to build confidence in the stakeholders of
                 the E-commerce many techniques came into existence. As
                 simple pattern matching methods are inadequate to solve
                 the problem many modern techniques came into existence.
                 They are based on Genetic programming, Sequence
                 Alignment, Machine learning, Fuzzy logic, Data mining
                 and Artificial intelligence. These techniques are
                 capable of detecting fraudulent transactions. In this
                 paper we explore various techniques being used. We also
                 build a prototype application which demonstrates the
                 efficiency of Genetic Programming to detect credit card
                 fraud. The empirical results revealed that the proposed
                 solution is effective.",
}

Genetic Programming entries for P PhaniAlekhya Sk Mahaboob Basha

Citations