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Optimal Resource Sharing Amongst Device-to-Device Communication Using Particle Swarm Algorithm Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Hybrid Method in Identifying the Fraud Detection in the Credit Card

Authors : Pooja Tiwari, Simran Mehta, Nishtha Sakhuja, Ishu Gupta, Ashutosh Kumar Singh

Published in: Evolutionary Computing and Mobile Sustainable Networks

Publisher: Springer Singapore

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Abstract

As the world is rapidly moving towards digitalization and money transactions are becoming cashless, the utilization of credit cards has rapidly heightened. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction and separate them from the non-fraudulent ones. The paper proffers the frame work to detect credit card frauds. A combination of the three techniques is used. These methodologies include Decision Trees, Neural Network and K-Nearest Neighbor. For each new incoming transaction, the new label is assigned by taking the majority of the labels from the output of these techniques. This model is believed to work fairly good for all sizes and kinds of dataset as it combines the advantages of the individual techniques. We conclude the paper with a comparison of our model with the existing ones.

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Literature
1.
2.
3.
Zheng L et al (2018) A new credit card fraud detecting method based on behavior certificate, IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, pp 1–6
4.
Delamaire L, Abdou HAH, Pointon J (2009) Credit card fraud and detection techniques: a review. Banks Bank Syst 4(2):57–68
5.
Gupta I, Singh AK (2019) Dynamic threshold based Information leaker identification scheme. Inform Process Lett 147:69–73MathSciNetCrossRef
6.
Bhattacharyya S, Jha S, Tharakunnel K, Westland J (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50:602–613CrossRef
7.
Suraj P, Varsha N, Kumar SP (2018) Predictive modelling for credit card fraud detection using data analytics. Procedia Comput Sci 132:385–395CrossRef
8.
Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225–245CrossRef
9.
Zareapoor M, Shamsolmoali P (2015) Application of credit card fraud detection: based on bagging ensemble classifier. Procedia Comput Sci 48:679–686CrossRef
10.
Sudha TRC (2017) Credit card fraud detection in internet using K nearest neighbour algorithm. IPASJ Int J Comput Sci 5(11)
11.
Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (2002) Credit card fraud detection using bayesian and neural networks
12.
Ho TK (1995) Random decision forests, in: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol 1, pp 278–282, IEEE, August-1995
13.
Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRef
14.
Zhang ML, Zhou ZH (2007) A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048CrossRef
15.
Mishra A, Ghorpade C (2018) Credit card fraud detection on the skewed data using various classification and ensemble techniques, in: 2018 IEEE International Students’ Conference on Electrical Electronics and Computer Science (SCEECS), pp 1–5
16.
Lakshmi SVSS, Kavilla SD, Machine learning for credit card fraud detection system
17.
Duman E, Özçelik M (2011) Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst Appl 38:13057–13063CrossRef
18.
Shin KS, Lee YJ (2002) A genetic algorithm application in bankruptcy prediction modeling. Expert Syst Appl 23(3):321–328CrossRef
19.
Malini N, Pushpa M Analysis on credit card fraud identification techniques based on KNN and outlier detection, in: 2017 Third International Conference on Advances in Electrical Electronics Information Communication and Bio-Informatics (AEEICB), pp 255–258
20.
Navamani C, Krishnan S Credit card nearest neighbor based outlier detection techniques
21.
Kazemi Z, Zarrabi H Using deep networks for fraud detection in the credit card transactions, in: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp 630–633
22.
Wang C, Wang Y, Ye Z, Yan L, Cai W, Pan S Credit card fraud detection based on whale algorithm optimized BP neural network, in: 2018 13th International Conference on Computer Science & Education (ICCSE), pp 1–4
23.
Gupta I, Singh AK (2019) An integrated approach for data leaker detection in cloud environment. J Inf Sci Eng
24.
Gupta I, Singh N, Singh AK (2019) Layer-based privacy and security architecture for cloud data sharing. J Commun Softw Syst (JCOMSS) 15(2)
25.
Wang Y, Adams S, Beling P, Greenspan S, Rajagopalan S, Velez-Rojas M, ankovski S, Boker S, Brown D (2018) Privacy preserving distributed deep learning and its application in credit card fraud detection, 1070–1078
26.
Reshma RS (2018) Deep learning enabled fraud detection in credit card transactions. Int J Res Sci Innov (IJRSI)
27.
Pandey Y (2017) Credit card fraud detection using deep learning. Int J Adv Res Comput Sci, May–June
28.
Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2018) Credit card fraud detection: a realistic modeling and a novel learning Strategy. IEEE Trans Neural Netw Learn Syst 29(8):3784–3797CrossRef
29.
Kazemi Z, Zarrabi H (2017) Using deep networks for fraud detection in the credit card transactions, in: IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, 2017, pp 0630–0633
30.
Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning, arXiv preprint arXiv:​1506.​00019
31.
Fine S, Singer Y, Tishby N (1998) The hierarchical hidden Markov model: analysis and applications. Mach Learn 32(1):41–62CrossRef
32.
Gupta I, Singh AK (2018) A probabilistic approach for guilty agent detection using bigraph after distribution of sample data. Procedia Comput Sci 125:662–668CrossRef
33.
Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15(3):142–147CrossRef
34.
De Sá A, Pereira A, Pappa G (2018) A customized classification algorithm for credit card fraud detection
35.
Panigrahi S, Kundu A, Sural S, Majumdar A (2009) Credit card fraud detection: a fusion approach using dempster–shafer theory and bayesian learning. Inform Fusion 10:354–363CrossRef
36.
Cheng J, Greiner R (2011) Learning bayesian belief network classifiers: algorithms and system, in: Conference of the Canadian Society for Computational Studies of Intelligence, June 2011, pp 141–151. Springer, Berlin, Heidelberg
37.
Kang F, Dawei C, Yi T, Liqing Z (2016) Credit card fraud detection using convolutional neural networks, 483–490
38.
Kumar J, Singh AK (2018) Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener Comput Syst 81:41–52CrossRef
39.
40.
Bayer JS (2015) München, Technische Universität München. Learning sequence representations, Diss
41.
Gupta I, Singh AK (2017) A probability based model for data leakage detection using bigraph, in: 7th International Conference on Communication and Network Security (ICCNS-2017), Tokyo, Japan, ACM, 2017
42.
Gupta I, Singh AK (2019) A confidentiality preserving data leaker detection model for secure sharing of cloud data using integrated techniques, in: Seventh International Conference on Smart Computing and Communication Systems (ICSCC), Sarawak, Malaysia, pp 1–5
43.
Kumar J, Singh AK (2019) Cloud resource demand prediction using differential evolution based learning scheme, in: 2019 Seventh International Conference on Smart Computing and Communication Systems (ICSCC), Curtin University, Miri
44.
Patidar R, Sharma L (2011) Credit card fraud detection using neural network. Int J Soft Comput Eng (IJSCE) 1:32–38
45.
Roy A, Sun J, Mahoney R, Alonzi L, Adams S, Beling P (2018) Deep learning detecting fraud in credit card transactions, in: Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, vol 2018, pp 129–134
46.
Yusuf S, Duman E (2011) Detecting credit card fraud by decision trees and support vector machines, in: IMECS 2011 – International Multiconference of Engineers and Computer Scientists 2011, vol 1, pp 442–447
47.
Sahin Y, Bulkan S, Duman E (2013) A cost-sensitive decision tree approach for fraud detection. Expert Syst Appl 40:5916–5923
48.
Behera TK, Panigrahi S (2015) Credit card fraud detection: a hybrid approach using fuzzy clustering, neural network, in: Second International Conference on Advances in Computing and Communication Engineering, Dehradun, pp 494–499
49.
Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 8:841–847CrossRef
50.
Sanaz M, Mehdi S (2018) Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors
51.
Srivastava A, Kundu A, Sural S, Majumdar A (2008) Credit card fraud detection using hidden markov model, in: IEEE Transactions on Dependable and Secure Computing, vol 5(1), pp 37–48
52.
Onukwugha C (2018) Data mining application in credit card fraud detection system
Metadata
Title
Hybrid Method in Identifying the Fraud Detection in the Credit Card
Authors
Pooja Tiwari
Simran Mehta
Nishtha Sakhuja
Ishu Gupta
Ashutosh Kumar Singh
Copyright Year
2021
Publisher
Springer Singapore
Book
Evolutionary Computing and Mobile Sustainable Networks

Print ISBN: 978-981-15-5257-1

Electronic ISBN: 978-981-15-5258-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-5258-8_3