article

Credit Card Fraud Detection Using Deep Learning Approach (LSTM) Under IoT Environment

Authors:

A. V. Senthil Kumar,

Abdelmalek Amine,

Hamou Mohamed RedaAuthors Info & Claims

International Journal of Organizational and Collective Intelligence (IJOCI), Volume 12, Issue 1

Pages 1 - 20

https://doi.org/10.4018/IJOCI.305207

Published: 21 July 2022 Publication History

Abstract

Online payment has mainly relied since 2008 on having a rechargeable cash card that remains the most popular form of payment for e-commerce transactions. Using this card is not without risks because of its sensitivity and buying online is practical and fast yet it can be a risk when we do not know the sellers or their websites well and one of these risks is fraud, which causes great financial losses where the card holder's information is stolen and used illegally without the physical presence of the card by fraudsters who impersonate trusted institutions to steal personal or financial information via a tainted link. Most of the fraud detection systems have also shown their inability to fully detect fraudulent cards every time and repeat false alarms. In this paper one of the complex techniques known as deep learning has been used especially LSTM variant of the RNN to classify credit cards. Unlike traditional classifiers this technique is distinguished by its ability to learn. Concerning the classification by LSTM a set of information about the card is taken including time and amount.

References

[1]

Abhinav, S., Amlan, K., Shamik, S., & Arun, M. (2008). Credit card fraud detection using hidden Markov model. IEEE Transactions on Dependable and Secure Computing, 5(1).

[2]

Amine, A. (2012). Text Mining Course. Department of Computer Science, Dr. Tahar Moulay University of Saida.

[3]

Amir, F., Hoda, M., & Hamid, H. (2017). A comparative performance analysis of different activation functions in LSTM networks for classification. Springer Neural Computing & Application.

[4]

Bengio, Y. (2009). ‘deep Learning deep architectures for AI’. Foundations and Trends in Machine Learning, 2(1), 1–127.

Digital Library

[5]

Chuan-Jun, S., & Yi, L. (2019). Recurrent neural network based real-time failure detection of storage devices . Microsystem Technologies.

[6]

Colah. (2015). Understanding lstm networks. Available from: http://colah.github.io/posts/2015-08-Understanding-LSTMs/

[7]

Fang, J. & Boli, Y. (2021). Machine Learning Applications in Complex Economics and Financial Networks. Academic Press.

[8]

Farhad, A., & Sanjay, P. (2017). Comparative Study of J48, Naive Bayes and One-R Classification Technique for Credit Card Fraud Detection using WEKA. Advances in Computational Sciences and Technology, 10(6), 1731-1743.

[9]

Fraud. (2019). UK finance organization website. Available from: www.ukfinance.org.uk/system/files/Fraud

[10]

Fraud the Facts. (2019). The definitive overview of payment industry fraud. UK Finance 2019.

[11]

Gauri, J., Manisha, S.& Basant, A. (2019). A Optimizing semantic LSTM for spam detection. International Journal of Information Technology.

[12]

Hasim, S., Andrew, S., & Françoise, B. (2014). Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling. Google.

[13]

HibaE.DarwishE.HassanienA.AbrahamA. (2010). Principle Components Analysis and Support Vector Machine based Intrusion Detection System. In Proc. 10th International conference on Intelligent Systems Design and Applications (ISDA). IEEE Press. 10.1109/ISDA.2010.5687239

[14]

Hochreiter, S., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies. IEEE Press.

[15]

Hua, Y., & Xi, Z. (2017). Research on the application of IoT in E-commerce. IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[16]

Ian, G., Yoshua, B. & Aaron, C. (2016). Deep learning book. diacriTech.

[17]

Ikram, K. (2018). Learning sequences and extracting rules from recurrent networks: application to schema tracing techniques. Academic Press.

[18]

Jain, G., Sharma, M., & Agarwal, B. (2018). A Optimizing semantic LSTM for spam detection . International Journal of Information Technology, 11(2), 239–250.

[19]

JohnA.AdebayoA.SamuelO. (2017). Credit card fraud detection using Machine Learning Techniques: A Comparative Analysis. In International Conference on Computing Networking and Informatics (ICCNI). IEEE.

[20]

Junhu, R., & Yan, S. (2016). Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches . Information Sciences, 373, 557–570.

Digital Library

[21]

Jürgen, S. (2015). ‘Deep Learning in Neural Networks’: An Overview. Neural Networks, 61, 85–117. 25462637.

Digital Library

[22]

Jurgovsky, J., Granitzer, M., Ziegler, K. & Calabretto, S. (2018). Sequence Classification for Credit-Card Fraud Detection. Expert Systems With Applications.

[23]

Kaggel. (2018). Credit Card Fraud Detection. Available from: www.kaggle.com/mlg-ulb/creditcardfraud

[24]

Karim, R. (2018). Animated RNN, LSTM and GRU. Available from: https://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45

[25]

Keras. (2019). Deep learning. Available from: https://keras.io/examples/conv_lstm/

[26]

KolariP.FininT.OatesT.JoshiA. (2006). Detecting spam blogs: a machine learning approach. Proceedings of the 21st national conference on artificial intelligence (AAAI).

[27]

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Exploring Deep Reinforcement Learning with Multi Q-Learning. Nature, 521(7553), 436–444. 26017442.

[28]

Mohamed, A., & Karar, M. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN (Vol. 31). Springer Neural Computing and Applications.

[29]

Mohamed, A., Victor, C., Hossam, H., Ripon, K., & Michael, R. (2021). Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment . IEEE Transactions on Industrial Informatics, 17(11), 7704–7715.

[30]

Ning, L., Dan, L., Wenbo, S., Pandi, V., Francesco, P., & Victor, C. (2021). An efficient combined deep neural network based malware detection framework in 5G environment . Computer Networks, 189.

[31]

Parthasarathya, G., Ramanathanb, L., JustinDhasc, Y., Saravanakumard, J., & Darwine, J. (2019). Comparative Case Study of Machine Learning Classification Techniques Using Imbalanced Credit Card Fraud Datasets. International Conference on Sustainable Computing in Science, Technology & Management (SUSCOM-2019). 10.2139/ssrn.3351584

[32]

Payment Card Fraud. (2020). UK finance organization website. Available from: https://www.paymentscardsandmobile.com/uk-fraud-report-2019/

[33]

Pozzolo, A., Boracchi, G., Caelen, O., & Alippi, C. (2018). Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8).

[34]

Ritu, G., & Manik, S. (2020). Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis. Springer Science+Business Media, LLC.

[35]

Saad, M. D. (2019). An intelligent credit card fraud detection approach based on semantic fusion of two classifiers. Springer-Verlag GmbH Germany.

[36]

Seunghye, L., Jingwan, H., Mehriniso, Z., Hyeonjoon, M., & Jaehong, L. (2017). Background Information of Deep Learning for Structural Engineering. CIMNE.

[37]

Shen, A., Tong, R., & Deng, Y. (2007). Application of Classification Models on Credit Card Fraud Detection. Graduate University of the Chinese Academy of Sciences.

[38]

Shukur, H., & Kurnaz, S. (2019). Credit Card Fraud Detection using Machine Learning Methodology . IJCSMC, 8(3), 257–260.

[39]

Siddhartha, B., Sanjeev, J., Kurian, T., & Christopher, J. (2011). Data mining for credit card fraud: A comparative study . Decision Support Systems, (50), 602–613.

[40]

Srivastava, H. (2017). What is k-fold cross validation. Available from: https://magoosh.com/data-science/k-fold-cross-validation/

[41]

Turban, E., King, D., McKay, J., Marshall, P., Lee, J., & Viehland, D. (2008). Electronic Commerce 2008: A Managerial Perspective. Pearson Education.

[42]

Victor, C., Le Minh Thao, D., Alessandro, D., & Zhili, S. (2022). Digital payment fraud detection methods in digital ages and Industry 4.0 . Computers & Electrical Engineering, 100, 107734.

Digital Library

[43]

Victor, C., Lewis, G., Paolo, M., Qianwen, A., Le Minh Thao, D., Karl, H., Sreeja, B., & Anna, K. (2022). A Survey on Intrusion Detection Systems for Fog and Cloud Computing . Future Internet, 14(3), 89.

[44]

Victor, C., & Muthu, R. (2015). Towards achieving Data Security with the Cloud Computing Adoption Framework . IEEE Transactions on Services Computing, 9(1), 138–151.

[45]

Vries, B., & Principe, J. (1991). A theory for neural networks with time delays . Advances in Neural Information Processing Systems, 3, 162–168.

[46]

Wollmer, M., Metallinou, A., Eyben, F., Schuller, B., & Narayanan, S. (2010). Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional lstm modeling. Proceedings on INTERSPEECH 2010, 2362–2365.

[47]

Xiao-qiang, W., Lei, Z., Song-ling, T. & Lan, W. (2019). Scenario based e-commerce recommendation algorithm based on customer interest in Internet of things environment. Springer Science+Business Media, LLC.

[48]

Zareapoor, M., & Shamsolmoalia, P. (2015). Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier . Procedia Computer Science, 48, 679–686.

[49]

Zouhair, C., Noreddine, A., Khalid, M., & Amina, E. (2018). A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Computers & Security, 75.

Cited By

Marwa MHajlaoui JSonia YOmri MRachid C(2023)Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in Fog-Cloud environmentComputing10.1007/s00607-022-01148-4105:7(1361-1393)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s00607-022-01148-4

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Credit Card Fraud Detection Using Deep Learning Approach (LSTM) Under IoT Environment

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cover image International Journal of Organizational and Collective Intelligence

International Journal of Organizational and Collective Intelligence Volume 12, Issue 1

Nov 2021

617 pages

ISSN:1947-9344

EISSN:1947-9352

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Copyright © 2022.

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IGI Global

United States

Publication History

Published: 21 July 2022

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Cited By

Marwa MHajlaoui JSonia YOmri MRachid C(2023)Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in Fog-Cloud environmentComputing10.1007/s00607-022-01148-4105:7(1361-1393)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s00607-022-01148-4

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