Abstract
Today, online services have evolved at a large scale which has made our life very easy, but there are many problems and challenges to make these services more secure for users. For instance, every day, many transactions are made by customers, and much private information is posted and shared on E-commerce and social media websites which makes privacy, safety and reliability a trough challenge to defy. Credit card fraud detection is one of these problems because fraudsters try to make every transaction legitimate by stealing the information related to the credit card. Hence, easy methods and other less complex techniques are not going to detect this type of fraud. Having an effective fraud detection technique has become a requirement for all banks to minimize chaos and maintain some order in place. In this paper, we use machine learning to detect fraudulent transactions by applying a genetic algorithm (GA) to optimize the hyperparameter and compare it with grid search (GS) methods. The used algorithms are random forest (RF), AdaBoost (AB), logistic regression (LR), decision tree (DT), and support vector machine (SVM) classifier. As the credit card fraud dataset is highly skewed (imbalanced data set) and the performance of fraud detection is greatly affected by the sampling approach, so we use undersampling to handle this issue. The obtained results in terms of accuracy, precision, recall, and F1_score have shown that the genetic algorithm can generate better performances in a short-time in comparison with the GS algorithm.
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References
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. IEEE (2017)
Sadgali, I., Sael, N., Benabbou, F.: Detection of credit card fraud: state of art. Int. J. Comput. Sci. Netw. Secur. 18(11), 76–83 (2018)
Wu, J., Chen, X.Y., Zhang, H., Xiong, L.D., Lei, H., Deng, S.H.: Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electron. Sci. Technol. 17(1), 26–40 (2019)
Rtayli, N., Enneya, N.: Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. J. Inf. Secur. Appl. 55, 102596 (2020)
Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Exp. Syst. Appl. 38(10), 13057–13063 (2011)
Zeager, M. F., Sridhar, A., Fogal, N., Adams, S., Brown, D.E., Beling, P.A.: Adversarial learning in credit card fraud detection. In: 2017 Systems and Information Engineering Design Symposium (SIEDS), pp. 112–116. IEEE (2017)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.S., Zeineddine, H.: An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access 7, 93010–93022 (2019)
Agrawal, A., Kumar, S., Mishra, A. K.: Credit card fraud detection: a case study. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 5–7. IEEE (2015)
Hormozi, H., Akbari, M. K., Hormozi, E., Javan, M. S.: Credit cards fraud detection by negative selection algorithm on hadoop (To reduce the training time). In: The 5th Conference on Information and Knowledge Technology, pp. 40–43. IEEE (2013)
Taha, A.A., Malebary, S.J.: An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access 8, 25579–25587 (2020)
Kewei, X., Peng, B., Jiang, Y., Lu, T.: A hybrid deep learning model for online fraud detection. In: 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 431–434. IEEE (2021)
Vermeulen, A.F.: Unsupervised learning: deep learning. In: Industrial Machine Learning, pp. 225–241. Apress, Berkeley (2020)
Syarif, I., Prugel-Bennett, A., Wills, G.: SVM parameter optimization using grid search and genetic algorithm to improve classification performance. Telkomnika 14(4), 1502 (2016)
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Tayebi, M., El Kafhali, S. (2021). Hyperparameter Optimization Using Genetic Algorithms to Detect Frauds Transactions. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_27
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DOI: https://doi.org/10.1007/978-3-030-76346-6_27
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