ENHANCING E-COMMERCE SECURITY: A NOVEL APPROACH TO CREDIT CARD FRAUD DETECTION
Received: 30th December 2024, Revised: 13th March 2025, 23rd May 2025, Accepted: 24th June 2025, Date of Publication: 11th December 2025
Keywords:
Credit Card Fraud Detection, K-Nearest Neighbors (KNN), Naive Bayes Classifier (NB), E-Commerce Security, Machine Learning, Fraud Detection SystemAbstract
Credit card fraud presents a risk to businesses and their clients. To combat security breaches, organizations implement different administrative and technical security controls to protect their data. With the increasing use of online transactions, organizations implement fraud detection systems. In this paper, we propose a novel credit card fraud detection system that integrates K-Nearest Neighbors and Naive Bayes machine learning algorithms. It educates employees on adherence to company guidelines and enhances their
ability to handle cyber threats. The proposed system examines online transactions and notifies administrators of suspicious transactions to act. The system is trained and tested on a dataset of 188 transactions. It achieved 94.3% accuracy, 94.4% sensitivity, and 94.1% specificity. The research findings demonstrate effectiveness of the proposed system in improving e-commerce security and safeguarding businesses and customers from this risk.
References
Bhasin, M. L. (2023). Credit Card Fraud Detection Using Machine Learning Algorithms: A Review.
International Journal of Advanced Computer Science and Applications, 14(1).
Nilson Report (2024). The Nilson Report, Issue 1229. Website: https://nilsonreport.com/newsletters/1229/
de Moura, J. M., Santos, A. T., & Lastres, O. (2023). Credit card fraud detection: a comprehensive survey of techniques and challenges. Artificial Intelligence Review, 1-53.
Bhatia, M. S., & Goyal, P. (2021). Credit card fraud detection: A systematic review of machine learning techniques. Journal of Emerging Technologies and Innovative Research, 8(1), 627-634.
Shaghayegh Hajian, Mohsen Rabbani, Shahaboddin Shamshirband. Credit Card Fraud Detection Based on Synthetic Minority Oversampling Technique and Deep Learning. Neural Computing and Applications. 2023.
Arun, A. K., Varatharajan, R., & Surendran, M. (2023). Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach. Electronics, 12(11), 2409.
Marín, D., Ramírez, S., & Sotoca, J. M. (2022). Explainable AI for Credit Card Fraud Detection: A Comparative Study. Applied Sciences, 12(11), 5545.
Zhang, X., Wang, Y., Zhou, Y., Liu, Y., & Zhang, S. (2021). Credit Card Fraud Detection Based on Generative Adversarial Networks. IEEE Access, 9, 149299-149309.
Fernando, A. D. S. L., & Arachchige, K. K. W. (2021). Real-time Credit Card Fraud Detection Using Streaming Analytics and Machine Learning. International Journal of Advanced Computer Science and Applications, 12(11).
Auwal, M. A. H. A., & Yau, A. S. (2022). Credit Card Fraud Detection Using Transaction Amount and Location Information. Journal of Theoretical and Applied Information Technology, 100(12), 3391- 3401.
Alzahrani, A. A. A., Aljuaid, M., & Alshammari, A. K. (2022). Credit Card Fraud Detection Based on User Behavior: A Systematic Literature Review. IEEE Access, 10, 49143-49163.
Liu, J., Chen, M., Li, J., & Huang, H. (2023). Federated Learning for Credit Card Fraud Detection: A Collaborative Approach. IEEE Transactions on Network Science and Engineering, 10(3), 1820-1832.
Omar, A. A., Alharbi, A., & Alghanmi, W. M. (2023). Blockchain-Based Secure Credit Card Transaction System for Fraud Detection. Sustainability, 15(11), 8866.
Kumar, S., Sharma, S., & Singh, S. K. (2022). A Hybrid Model for Credit Card Fraud Detection Using Machine Learning and Deep Learning Techniques. International Journal of Information Technology, 14(3), 1091-1100.
Bhattacharyya, R., Ghosh, S., & Das, A. K. (2021). Detecting Application Fraud in Credit Card Applications Using Machine Learning. Expert Systems with Applications, 166, 114077.
