Enhanced E-Banking Security System for Phishing Detection Using Machine Learning and Privacy Preservation

Orokor Favour Wenebunwo

doi:10.56201/ijcsmt.v11.no2.2025.pg73.84

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Enhanced E-Banking Security System for Phishing Detection Using Machine Learning and Privacy Preservation

Orokor Favour Wenebunwo

Abstract

E-banking systems have revolutionized financial transactions, but they are increasingly vulnerable to phishing attacks that compromise sensitive user data. Phishing detection in e-banking presents several challenges, including the ability to accurately identify deceptive URLs, the presence of imbalanced datasets where phishing attempts are significantly fewer than legitimate activities, and the need for privacy-preserving mechanisms that protect user data during the detection process. Addressing these issues requires the development of advanced machine learning algorithms that can detect phishing attacks with high accuracy while preserving user privacy. This dissertation presents the development of a phishing detection system for e-banking, addressing key challenges such as detecting sophisticated phishing attempts while ensuring data privacy. The system was built using Object-Oriented Design (OOD) methodology, with Python as the programming language. Utilizing the Random Forest Classifier (RFC) and privacy preservation techniques, the model enhances e-banking security. Exploratory Data Analysis (EDA) revealed an imbalanced dataset, which was mitigated through Random OverSampling. Key features were identified using feature importance ranking and correlation matrices, leading to a highly accurate RFC model. To protect sensitive data, differential privacy was incorporated by adding Laplace noise during both training and deployment. The model achieved an accuracy of 98.98%, outperforming other systems, and was deployed via a Flask web application with a user-friendly interface for phishing URL detection. Comparisons with existing models showed the system’s superior performance across key metrics.

Keywords

E-banking Security; Phishing Detection; Machine Learning; Privacy Preservation.

References

AlHamad, A. Q. M. (2020). Acceptance of E-learning among university students in UAE: A practical study. International Journal of Electrical and Computer Engineering, 10(4), 3660. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 395(10223), 497-506. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and trends® in machine learning, 14(1–2), 1-210. Karabatak, M., & Mustafa, T. (2018, March). Performance comparison of classifiers on reduced phishing website dataset. In 2018 6th international symposium on digital forensic and security (ISDFS) (pp. 1-5). IEEE. Mansouri, Z., Asgari, H. M., Battaleb-Looie, S., Heidari, M., & Azhdari, A. (2024). Source Identification of Urban dust Heavy metals using in situ and Satellite Data in three mega cities of Iran. Bulletin of Environmental Contamination and Toxicology, 113(1), 11. Parikh, S. B., & Atrey, P. K. (2018, April). Media-rich fake news detection: A survey. In 2018 IEEE conference on multimedia information processing and retrieval (MIPR) (pp. 436-441). IEEE. Peng, P., Barnes, M., Wang, C., Wang, W., Li, S., Swanson, H. L., ... & Tao, S. (2018). A meta-analysis on the relation between reading and working memory. Psychological bulletin, 144(1), 48. Sarjiyus, O., Oye, N. D., & Baha, B. Y. (2019). Improved online security framework for e-banking services in Nigeria: A real world perspective. management, 6, 7. Shima, K., Tasker, E. J., Federrath, C., & Habe, A. (2018). The effect of photoionizing feedback on star formation in isolated and colliding clouds. Publications of the Astronomical Society of Japan, 70(SP2), S54. Sonmez, A. I., Camsari, D. D., Nandakumar, A. L., Voort, J. L. V., Kung, S., Lewis, C. P., & Croarkin, P. E. (2019). Accelerated TMS for depression: a systematic review and meta-analysis. Psychiatry research, 273, 770-781. Tyagi, I., Shad, J., Sharma, S., Gaur, S., & Kaur, G. (2018, February). A novel machine learning approach to detect phishing websites. In 2018 5th International conference on signal processing and integrated networks (SPIN) (pp. 425-430). IEEE. Vazhayil, A., Vinayakumar, R., & Soman, K. P. (2018, July). Comparative study of the detection of malicious URLs using shallow and deep networks. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.

Enhanced E-Banking Security System for Phishing Detection Using Machine Learning and Privacy Preservation

Abstract

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