참고문헌
- "How the Virus Transformed the Way Americans Spend Their Money - The New York Times." https://www.nytimes.com/interactive/2020/04/11/business/economy/coronavirus-us-economy-spending.html (accessed Jun. 08, 2021).
- "Amount of Bank Data for Sale on Dark Web up 135%: Report I Credit Union Times." https://www.cutimes.com/2018/07/13/amount-of-bankdata-for-sale-on-dark-web-up-135-re/ (accessed Jun. 08, 2021).
- "Payment Card Fraud Losses Reach $27.85 Billion." https://www.prnewswire.com/news-releases/paymentcard-fraud-losses-reach-27-85-billion-300963232.html (accessed Jun. 08, 2021).
- H. Harwani, J. Jain, C. Jadhav, and M. Hodavdekar, "Credit Card Fraud Detection Technique using Hybrid Approach: An Amalgamation of Self Organizing Maps and Neural Networks," International Research Journal of Engineering and Technology, 2020
- A. RB and S. K. KR, "Credit card fraud detection using artificial neural network," Global Transitions Proceedings, vol. 2, no. 1, pp. 35-41, Jun. 2021, doi: 10.1016/j.gltp.2021.01.006.
- V. N. Dornadula and S. Geetha, "Credit Card Fraud Detection using Machine Learning Algorithms," in Procedia Computer Science, 2019, vol. 165, pp. 631-641. doi: 10.1016/j.procs.2020.01.057.
- A. Roy, J. Sun, R. Mahoney, L. Alonzi, S. Adams, and P. Beling, "Deep learning detecting fraud in credit card transactions," in 2018 Systems and Information Engineering Design Symposium, SIEDS 2018, Jun. 2018, pp. 129-134. doi: 10.1109/SIEDS.2018.8374722.
- I. Benchaji, S. Douzi, and B. el Ouahidi, "Using genetic algorithm to improve classification of imbalanced datasets for credit card fraud detection," in Lecture Notes in Networks and Systems, vol. 66, Springer, 2019, pp. 220-229. doi: 10.1007/978-3-030-11914-0_24.
- T. K. Behera and S. Panigrahi, "Credit Card Fraud Detection: A Hybrid Approach Using Fuzzy Clustering & Neural Network," in Proceedings - 2015 2nd IEEE International Conference on Advances in Computing and Communication Engineering, ICACCE 2015, Oct. 2015, pp. 494-499. doi: 10.1109/ICACCE.2015.33.
- H. Harwani, J. Jain, C. Jadhav, and M. Hodavdekar, "Credit Card Fraud Detection Technique using Hybrid Approach: An Amalgamation of Self Organizing Maps and Neural Networks," no. July, pp. 5071-5075, 2020.
- V. N. Dornadula and S. Geetha, "Credit Card Fraud Detection using Machine Learning Algorithms," in Procedia Computer Science, 2019, vol. 165, pp. 631-641. doi: 10.1016/j.procs.2020.01.057.
- A. Saputra and Suharjito, "Fraud detection using machine learning in e-commerce," International Journal of Advanced Computer Science and Applications, vol. 10, no. 9, pp. 332-339, 2019, doi: 10.14569/ijacsa.2019.0100943.
- S. Samarasinghe, Neural Networks for Applied Sciences and Engineering. Auerbach Publications, 2006. doi: 10.1201/9781420013061.
- H. H. Tan and K. H. Lim, "Review of second-order optimization techniques in artificial neural networks backpropagation," in IOP Conference Series: Materials Science and Engineering, 2019, vol. 495, no. 1. doi: 10.1088/1757-899X/495/1/012003.
- J. R. Quinlan, "Learning first-order definitions of functions," Journal of Artificial Intelligence Research, vol. 5, pp. 139-161, 1996, doi: 10.1613/jair.308.
- J. C. Meza, "Steepest descent," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 6, pp. 719-722, Nov. 2010, doi: 10.1002/wics.117.
- S. Samarasinghe, Neural Networks for Applied Sciences and Engineering. Auerbach Publications, 2006. doi: 10.1201/9781420013061.
- M. Puig-Arnavat and J. C. Bruno, "Artificial Neural Networks for Thermochemical Conversion of Biomass," in Recent Advances in Thermochemical Conversion of Biomass, Elsevier Inc., 2015, pp. 133-156. doi: 10.1016/B978-0-444-63289-0.00005-3.
- T. Huang, Z. Zeng, C. Li, and C. S. Leung, Eds., Neural Information Processing, vol. 7667. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. doi: 10.1007/978-3-642-34500-5.
- J. Duchi JDUCHI and Y. Singer, "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization * Elad Hazan," Journal of Machine Learning Research, vol. 12, pp. 2121-2159, 2011.
- S. Daliri, "Using Harmony Search Algorithm in Neural Networks to Improve Fraud Detection in Banking System," Computational Intelligence and Neuroscience, vol. 2020, 2020, doi: 10.1155/2020/6503459.
- X. Niu, L. Wang, and X. Yang, "A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised." Accessed: Jun. 08, 2021. [Online]. Available: www.aaai.org
- Y. Kumar, S. Saini, and R. Payal, "Comparative Analysis for Fraud Detection Using Logistic Regression, Random Forest and Support Vector Machine," SSRN Electronic Journal, Mar. 2021, doi: 10.2139/ssrn.3751339.