Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

November Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the November 2021 Edition of the journal. The last date of research paper submission is October 15, 2021.

A Comprehensive Survey of Fraud Detection Techniques

Lutfun Nahar Lata, Israt Amir Koushika, Syeda Shabnam Hasan. Published in Security

International Journal of Applied Information Systems
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Lutfun Nahar Lata, Israt Amir Koushika, Syeda Shabnam Hasan
10.5120/ijais2015451471
Download full text
  1. Lutfun Nahar Lata, Israt Amir Koushika and Syeda Shabnam Hasan. Article: A Comprehensive Survey of Fraud Detection Techniques. International Journal of Applied Information Systems 10(2):26-32, December 2015. BibTeX

    @article{key:article,
    	author = "Lutfun Nahar Lata and Israt Amir Koushika and Syeda Shabnam Hasan",
    	title = "Article: A Comprehensive Survey of Fraud Detection Techniques",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 10,
    	number = 2,
    	pages = "26-32",
    	month = "December",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"
    }
    

Abstract

To overcome the financial loss and threat, fraud detection is a must. New theories and many techniques have been introduced to overcome the fraud. Fraud detection techniques monitor the behavior of the user and inform the user if any harmful event occurs. These modern techniques help to lessen the fraud and unwanted behavior. Some techniques have lacking in some cases, so there are many studies and experiments to improve new methods to detect fraud detection. This paper is about a wide-ranging survey about different kinds of modern techniques used for computer intrusion, credit card fraud, telecommunication fraud and insurance fraud. The main goal of this paper is to assess most common and useful techniques used for different kinds of fraud detection now-a-days.

Reference

  1. Anomaly-based network intrusion detection: Techniques, systems and challenges; P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez, E. Vazquez. P. 1-5.
  2. Learning Program Behavior Proles for Intrusion Detection; Anup K. Ghosh, Aaron Schwartzbard & Michael Schatz.
  3. Intrusion Detection Framework for Cyber Crimes using Bayesian Network Chaminda Alocious, Nasser Abouzakhar, Hannan Xiao, Bruce Christianson University of Hertfordshire, Hatfield, UK. P. 4.
  4. Survey of Fraud Detection Techniques; Yufeng Kou, Chang-Tien Lu, Sirirat Sirwongwattana Dept. of Computer Science; Yo-Ping Huang Dept. of Computer Science and Engineering. P. 1-5.
  5. Mahoney M., Chan P.K.; An analysis of the 1999 DARPA/Lincoln laboratory evolution data for network anomaly detection. Florida tech. report CS-2003-02; 2003.
  6. Application of Bayesian Methods in Detection of Healthcare Fraud Tahir Ekina, Francesca Leva*,b, Fabrizio Ruggeri c, Refik Soyer d . P. 1,3.
  7. Cooper C (2003) Turning information into action. Computer Associates: The Software That Manages e-Business, Report, available at http://www.ca.com
  8. A survey on statistical methods for health care fraud detection; Jing Li & Kuei-Ying Huang & Jionghua Jin & Jianjun Shi. P. 7-9.
  9. Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. SIGKEE Explorations 6 (1):50–59
  10. Yang WS (2003) A Process Pattern Mining Framework for the Detection of Health Care Fraud and Abuse, Ph.D. thesis, National Sun Yat-Sen University, Taiwan
  11. Credit Card Fraud Detection Using Neural Network, Raghavendra Patidar, Lokesh Sharma
  12. Association of certified fraud examiners (ACEF); http://www.acfe.com/article.aspx?id=4294976280
  13. Survey of Insurance Fraud Detection Using Data Mining Techniques; H.Lookman Sithic, T.Balasubramanian. P. 1-2.
  14. Data Mining Techniques in Fraud Detection; Rekha Bhowmik University of Texas at Dallas. P. 4-5.
  15. Ray-I Chang, Liang-Bin Lai, WenDe Su, Jen-Chieh Wang, Jen-Shiang Kouh “Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query”.Research India Publications; (2006). (6-10).
  16. Raghavendra Patidar, Lokesh Sharma “Credit Card Fraud Detection Using Neural Network”. International Journal of Soft Computing and Engineering (IJSCE), (2011). Volume-1, Issue; (32-38).
  17. Raghavendra Patidar, Lokesh Sharma, “Credit Card Fraud Detection Using Neural Network” 2011.
  18. Tao Guo, Gui-Yang Li “Neural Data Mining For Credit Card Fraud Detection”. IEEE, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics; (2008). (3630-3634).
  19. Review Paper on Credit Card Fraud Detection, Suman Research Scholar, GJUS&T Hisar HCE Sonepat, Nutan Mtech. CSE, HCE Sonepat.
  20. Survey of Fraud Detection Techniques, YufengKou,Chang-TienLu,Sirirat Sirwongwattana,Yo-Ping Huang.
  21. S.Rosset, U. Murad, E. Neumann, Y. Idan, and G. Pinkas. Discovery of fraud rules for telecommunications challenges and solutions. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pages409-413.ACMPress,1999.
  22. M.Taniguchi, M.Haft, J.Hollmen, and V.Tresp. Fraud detection in communication networks using neural and probabilistic methods. In Proceedings of the 1998 IEEE International Conference in Acoustics, Speech and Signal Processing,volume2,pages1241-1244,1998.
  23. S. Benson Edwin Raj, A. Annie Portia “Analysis on Credit Card Fraud Detection Methods” 2011. S. Benson Edwin Raj, A. Annie Portia “Analysis on Credit Card Fraud Detection Methods” 2011.

Keywords

Fraud detection, Intrusion, credit card, telecommunication, healthcare, insurance, data mining etc