CFP last date
20 May 2024
Reseach Article

Avoiding Cybercrime Pandemic in Cashless Society using HMM

by Abdulrahman Abdulganiyu, Aliyu Y. Badeggi, Usman M. Gana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 7
Year of Publication: 2012
Authors: Abdulrahman Abdulganiyu, Aliyu Y. Badeggi, Usman M. Gana
10.5120/9706-4157

Abdulrahman Abdulganiyu, Aliyu Y. Badeggi, Usman M. Gana . Avoiding Cybercrime Pandemic in Cashless Society using HMM. International Journal of Computer Applications. 60, 7 ( December 2012), 35-43. DOI=10.5120/9706-4157

@article{ 10.5120/9706-4157,
author = { Abdulrahman Abdulganiyu, Aliyu Y. Badeggi, Usman M. Gana },
title = { Avoiding Cybercrime Pandemic in Cashless Society using HMM },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 7 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number7/9706-4157/ },
doi = { 10.5120/9706-4157 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:56.944555+05:30
%A Abdulrahman Abdulganiyu
%A Aliyu Y. Badeggi
%A Usman M. Gana
%T Avoiding Cybercrime Pandemic in Cashless Society using HMM
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 7
%P 35-43
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Internet fraudulent activities are increasing dramatically in the availability of technology resources like telecommunication networks, mobile communications, and E-commerce. Fraud is a major problem in electronic payment systems. With this increased availability has come a new form of criminal activity that takes advantage of electronic payment system, namely cybercrime, mobile-crime, SIM-crime and computer fraud. Currently, these new forms of crime are burgeoning and pose a new and challenges to researchers,merchant, customers and the law enforcement agencies. In this paper we discus types of electronic payment, we propose an effective method of detecting and preventing unauthorized cybercriminals from gaining access to several devices and technologies used in electronics payment by using Hidden Markov Model, also we take care not to prevent genuine transaction not to be rejected.

References
  1. A. J. Graaff A. P. Engelbrecht "The Artificial Immune System for Fraud Detection in the Telecommunications Environment"; (2011). (1-4)
  2. AbhinavSrivastava, AmlanKundu, ShamikSural, Arun K. Majumdar. "Credit Card Fraud Detection using Hidden Markov Model". IEEE Transactions on dependable and secure computing,Volume 5; (2008) (37-48).
  3. AihuaShen, Rencheng Tong, Yaochen Deng "Application of Classification Models on Credit Card Fraud Detection". (2007).
  4. Anshul Singh, Devesh Narayan "A Survey on Hidden Markov Model for Credit Card Fraud Detection". International Journal of Engineering and Advanced Technology (IJEAT), (2012). Volume-1, Issue-3; (49-52).
  5. B. SanjayaGandhi ,R. LaluNaik, S. Gopi Krishna, K. lakshminadh "Markova Scheme for Credit Card Fraud Detection". International Conference on Advanced Computing, Communication and Networks; (2011). (144-147).
  6. Osama Dandash,Phu Dung Le and BalaSrinivasan "Security Analysis for Internet Banking Models". Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing IEEE DOI 10. 1109/SNPD. 2007.
  7. Qinghua Zhang "Study on Fraud Risk Prevention of Online Banks". 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.
  8. Bidgoli, B. M. , Kashy, D. , Kortemeyer, G. & Punch, W. F "Predicting student performance: An Application of data mining methods with the educational web-based system LON-CAPA". In Proceedings of ASEE/IEEE frontiers in education conference. . (2003).
  9. Bolton, R. J. , Hand, D. J (2002). "Statistical fraud detection: A review". Statistical Science (1994). 28(3); (235—255).
  10. Clifton Phua, Vincent Lee, Kate Smith, and Ross Gayler "A comprehensive survey of data mining-based fraud detection research". In Artificial Intelligence Review. (2005).
  11. Cortes, C. &Vapnik, V "Support vector networks, Machine Learning". . (1995). Vol. 20; (273–297).
  12. De Castro, L. , &Timmis, J "Artificial immune systems: a new computational approach". London, UK: Springer-Verlag. . (2002).
  13. Dipti D. Patil, V. M. Wadhai, J. A. Gokhale "Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy". International Journal of Computer Applications, (2010). Volume 11– No. 2; (23-30).
  14. MasoumehZareapoor,Seeja. K. R,M. Afshar. Alam"Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria". International Journal of Computer Applications, August 2012. Volume 52– No. 3 (0975 – 8887).
  15. Sunil S Mhamane, L. M. R. J Lobo" Use of Hidden Markov Model as Internet Banking Fraud Detection". International Journal of Computer Applications May 2012. Volume 45– No. 21, (0975 – 8887).
  16. SandeepPratap Singh, Shiv Shankar P. Shukla,NitinRakesh and VipinTyagi"PROBLEM REDUCTION IN ONLINE PAYMENT SYSTEM USING HYBRID MODEL". International Journal of Managing Information Technology (IJMIT) Vol. 3, No. 3, August 2011.
  17. Adnan M. Al-Khatib"Electronic Payment Fraud Detection Techniques". World of Computer Science and Information Technology Journal (WCSIT) 2012, ISSN: 2221-0741 Vol. 2, No. 4, 137-141.
  18. DEJAN SIMIC"REDUCING FRAUD IN ELECTRONIC PAYMENT SYSTEMS". The 7th Balkan Conference on Operational Research, May 2005, Romania
Index Terms

Computer Science
Information Sciences

Keywords

SIM-crime E-payment E-cash Cyber criminals Enabling technologies Cashless society