Timestamp and IP Address based Fraud Detection in Credit Cards using Hidden Markov Model

Authors

  • Deepti Rai  Department of Computer Science & Engineering, New Horizon College of Engineering, Bangalore, Karnataka, India.

Keywords:

E-Transactions, Fraud Detection, Hidden Markov Model, Credit Card

Abstract

Online activities mainly involve purchasing products, electronic devices and other similar things in a regular basis. There are many online transaction methods particularly made for such activities, which ensures the security by authorizing the transfer of funds. The online transactions are achieved by different bank cards that, makes the process simple. Although they are having notable advantages, they confront some of their drawbacks regarding the security. The credit card frauds can happen for many reasons, mainly to get access to non-accredited funds from the account. It is a responsibility for the bank to screen and protect the card details of the user while doing online transactions. Our approach is based on the Hidden Markov Model. HMM detects the fraud in the transactions and blocks it. It also stores the details about the timestamp and IP address of the fraudster’ s machine. Whenever a new transaction is made, the system will make a note of it by recording the transaction. The spending profile of the card holder is created based on his previous transaction history using HMM. Now if any intruder tries to make transactions with any registered credit card, the system notices the difference in the spending pattern of the card holder and thus the intruder gets easily trapped.

References

  1. Yonghui Xiao, Li Xiong, Si Zhang, Yang Cao, "Loclok: Location cloaking with diferential privacy via hidden markov model", Proceeding in VLDB Endowment 10(12) (ACM), pp.1901-1904, August 2017.
  2. Lutao Zheng, Guanjun Liu, Chungang Yan, and Changjun Jiang, "Transaction Fraud Detection Based on Total Order Relation and Behavior Diversity", IEEE Transaction May 2018.
  3. Ishan Sohony, Rameshwar Pratap, Ullas Nambiar, "Ensemble Learning for Credit Card Fraud Detection", ACM ISBN 978-1-4503-6341-9/18/01'(ACM), January 2018.
  4. Phuong Hanh Tran, Kim Phuc Tran, Truong Thu Huong, Cedric Heuchenne, Phuong HienTran, Thi Minh Huong Le, "Real Time Data-Driven Approaches for Credit Card Fraud Detection", ACM ISBN 978-1- 4503-6368-6/18/02, Febraury 2018.
  5. Leite R. A., Gschwandtner T.,Miksch S., Kriglstein S., Pohl M., Gstrein E.,Kuntner J., "Eva: Visual analytics to identify fraudulent events", IEEE Transactions on visualization and computer graphics 24, 1 (2018), 330:339, March 2017.
  6. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, G. Bontempi, " Credit card fraud detection: "A Realistic Modeling And A Novel Learning Strategy ", IEEETransactions on Neural Networks and Learning Systems D.O.I. 10.1109/TNNLS.2017.2736643, June 2017.
  7. Alexander Artikis, Nikos Katzouris, Ivo Correia, Chris Baber, Natan Morar, Inna Skarbovsky, Fabiana Fournier, Georgios Paliouras, "Industry Paper: A Prototype For Credit Card Fraud Management ", ISBN:978-1-4503-5065-5/17/06, Proceedings of the 7th ACM 2017.
  8. Ziegeldorf JH, Metzke J, Ruth J, Henze M, Wehrle K., "Privacy-Preserving HMM Forward Computation ", in proceedings of the 7th ACM Conference on Data and Application Security and Privacy. New York, ISBN 978-1-4503-4523-1/17/03, ACM 2017.
  9. Z. G. Wu, P. Shi, Z. Shu, H. Su, and R. Lu, "Passivity- based asynchronous control for Markov jump systems", IEEE Transaction Autom. Control, to be published, DOI:10.1109/TAC.2016.2593742, May 2017.
  10. F. Kang, Dawei Cheng, Yi Tu, and Liqing Zhang, "Credit Card Fraud Detection Using Convolutional Neural Networks", in Proceeding of the International Conference on Neural Information Processing, Springer Inter-national Publishing, 2016.
  11. N. Mahmoudi and E. Duman, "Detecting credit card fraud by modified Fisher discriminant analysis", Expert Syst. Appl. 42 (April (5))(2015) 2510 : 2516, Elsevier 2014.
  12. Ivo Correia, Fabiana Fournier, and Inna Skarbovsky, "The uncertain case of credit card fraud detection", in ACM DEBS. 181 : 192, ACM 978-1-4503-3286-6/15/06, ACM 2014.
  13. Santiago, G. P., Pereira, A. C. M. Hirata, R. (2015), "A modeling approach for credit card fraud detection in electronic payment services", 30th Annual ACM Symposium on Applied Computing, 978-1-4503-3196-8/15/04 ACM 2015.
  14. Ashphak Khan, Tejpal Singh and Amit Sinhal, "Observation Probability in Hidden Markov Model for Credit Card Fraudulent Detection System", Springer 2016.
  15. Ankit Mundra and Nitin Rakesh, "Online Hybrid Model for Online Fraud Prevention and Detection", Intelligent Computing, Networking, and Informatics, Springer India 2014.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-12-30

Issue

Volume 4, Issue 9, November-December-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Deepti Rai, " Timestamp and IP Address based Fraud Detection in Credit Cards using Hidden Markov Model" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 9, pp.390-396, November-December-2019.