Credit Card Fraud Detection Using Federated Learning Techniques

Authors

  • R. Suvarna  PG Scholar, Department of Computer Science and Engineering, Government College of Technology/Anna University, Coimbatore, Tamilnadu, India
  • Dr. A. Meena Kowshalya  M.E, Ph.D, Associate Professor, Department of Computer Science and Engineering, Government College of Technology/Anna University, Coimbatore, Tamilnadu, India

Keywords:

Credit Card Fraud, Auto Encoder, Restricted Boltzmann machine, Federated learning, decentralized model

Abstract

Frauds in Credit cards have become more usual in today’s generation and many cases have been reported in the past with the increase in cybercrimes. Though there exist numerous techniques to detect online credit card fraudulence, deep-learning and federated learning techniques can efficiently detect accurate fraudulence. This paper exploits two unsupervised learning algorithms namely Auto encoder and Restricted Boltzmann Machine (RBM) implemented over a federated learning framework to predict number of credit card fraudulent users. -time European credit card dataset with 284,807 transactions are used to find the number of fraudulent users. The decentralized federated learning framework is compared against centralized approach. The average accuracy using federated learning for Auto encoder and RBM is 88% and 94% respectively and 99% and 92% using centralized deep learning approach. Federated Learning ensured high differential privacy compromising accuracy.

References

  1. Suvarna. R, Dr. A.Meena Kowshalya, 2020, Credit Card Fraud Detection Using Deep Learning Techniques, Journal of Web Engineering and Technology, sISSN: 2455-1880, Vol. 7, Issue 1, April.
  2. Wensi Yang, Yuhang Zhang, Kejiang Ye, Li Li, Cheng-Zhong Xu, 2019, Ffd: A Federated Learning Based Method For Credit Card Fraud Detection, Springer Journal Big Data 2019,LNCS 11514, pp. 18–32.
  3. Meng Hao, Hongwei li, Guowen Xu, Sen Liu and Haomiao Yang, 1 June 2016, , Feature engineering strategies for credit card fraud detection.
  4. Albertio,chiung, International Conference on Science and Technology on Communication Security Laboratory, 978-I-5386-8088-9/19@IEEE, Towards Efficient and Privacy-preserving Federated Deep Learning.
  5. Wei Yang Bryan Lim, Nguyen cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, 26Sep 2019, International Conference on Science and Technology arXiv:1909.11875vl cs.NIFederated learning in Mobile Edge Networks: A Comprehensive Survey.
  6. Xu, Hongwei Li, Sen Liu, Kan Yang, Xiaodong, 2019, VerifyNet: Secure and verifiable Federated Learning, Guowen, China, Journal of Transaction pn Information Forensics and security, 1556-6013© IEEE.
  7. Takayuki Nishio, Ryo Yonetani, Japan, 2019, Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge, JST ACT-I Journal of Computer Science 978-1-5386-8088-9/19© IEEE.
  8. Yuze Zou, Shaohan Feng, Dusit Niyato, Yutao Jiao, Shimin Gong and Wenqing Cheng, 2019, Federated Learning: An Evolutionary Game Approach, International Conference on Internet of Things(iThings) and IEEE Green Computing and Communications(GreenCom) and IEEE Cyber 978-17281-2980-8, Physical and Social Computing(CPSCom) and IEEE Smart Data(SmartData), Mobile Device Training Strategies.
  9. Xin Yao, Tianchi Huang, Chenglei Wu, Ruixiao Zhang, Lifeng Sun, China, 2019, Towards Faster And Better Federated Learning: A feature Fusion Approach, International Conference on Compuer science and Engineering 978-1-5386-6249-6/19 © IEEE.
  10. Qibei Lu, Chunhua Ju, January 2011, Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine, Journal of Convergence Information Technology, volume 6, Number 1.
  11. Sanjeev Jha, Montserrat Guillen, J. Christopher Westland, 2012, Employing transaction aggregation strategy to detect credit card fraud, International Journal On Expert Systems with Applications 0957-4147.
  12. Apapan Pumsirirat, 2018, Credit Card Fraud Detection using Auto-Encoder and Restricted Boltzmann Machine, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 1.
  13. Detection N.Malini, Dr.M.Pushpa, 2017, Analysis on Credit Card Fraud Identification Techniques based on KNN and Outlier, 3rd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEEICB17) 978-1-5090-5434-3© IEEE.
  14. Ekrem Duman a, M. Hamdi Ozcelik Dogus , Istanbul, Turkey b Yapi Kredi Bankasi, IT Department, Istanbul, Turkey , Detecting credit card fraud by genetic algorithm and scatter search.
  15. Venkata Ratnam Ganji Gudivada A.P,India. Credit card fraud detection using anti-k nearest neighbor algorithm, International Journal on Computer Science and Engineering (IJCSE) .
  16. K.RamaKalyani, D.UmaDevi, Fraud Detection of Credit Card Payment System by Genetic Algorithm, International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012 1 ISSN 2229-5518 IJSER.
  17. Mohd Avesh Zubair Kha1 , Jabir Daud Pathan2 , Ali Haider Ekbal Ahmed, 2014, Credit Card Fraud Detection System Using Hidden Markov Model and K-Clustering, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 2.
  18. Y. Sahin and E. Duman ,2016, Detecting Credit Card Fraud by Decision Trees and Support Vector Machine, International Multiconference of Engineers and computer scientists 2011 vol1 IMECS 2011,March 16-18.
  19. Amlan kundu, suvasini panigrahi, shamik sural and arun k. majumdar, 2009, blast-ssaha hybridization for credit card fraud detection, ieee transactions on dependable and secure computing, vol. 6, no. 4.
  20. Suvasini Panigrahi a , Amlan Kundu a , Shamik Sural a, A.K. Majumdar , 2019, Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning
  21. Leila Seyedhossein, 2010, Mining Information from Credit Card Time Series for Timelier Fraud Detection, 5th International Symposium on Telecommunications (IST'2010) .
  22. N.Malini, Dr.M.Pushpa, credit card fraud detection techniques by data mining and big data approach, International journal of research in computer applications and robotics issn 2320-7345 analysis.
  23. Shamik Sural2, and A.K. Majumdar, 2019, A Game-Theoretic Approach to Credit Card Fraud Detection Vishal Vatsa1.
  24. Priya Ravindra Shimpi, Prof. Vijayalaxmi Kadroli, 2015, Survey on Credit Card Fraud Detection Techniques, International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 4 Issue 11 Nov 2015, Page No. 15010-15015.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-06-30

Issue

Volume 7, Issue 3, May-June-2020

Section

Research Articles

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How to Cite

[1]
R. Suvarna, Dr. A. Meena Kowshalya "Credit Card Fraud Detection Using Federated Learning Techniques" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 3, pp.356-367, May-June-2020.