Intelligent Detection Approaches for Securing E-Banking Platforms against Phishing Websites.

E-banking has introduced fresh innovations in finances because of the new conveniences, efficiencies, and global access. Conversely, the fast spread of e-banking has increased phishing attacks, which are a cyber-attack on banking sites. This paper concentrates on the intelligent detection of phishing websites to protect e-banking systems, particularly the assessment and comparison of algorithms on intelligent detection. The UCI Phishing Dataset URL, content, and behavioral features were diverse and applied in training and testing different machine-learning models. Support Vector Machines, Random Forest, Neural Networks, XGBoost, and Hybrid performance models were evaluated in terms of accuracy, precision, recall, F1-score, and AUC. Although SVM showed a mediocre detection risk, the Random Forest and Neural Networks proved to be significantly more reliable, whereas XGBoost exhibited the highest performance due to its accuracy and scalability of performance. All tests yielded the most reliable results, and hybrid systems achieved the highest metrics and performance. This is revealed the most reliable detection and control systems of phishing threats in e-banking system since phishing frauds hugely make use of vulnerabilities in e-banking. In online banking systems, phishing frauds are best controlled by hybrid systems and intelligent detection systems. Some of the practical refinements provided by this study are improving fraud prevention, customer confidence, and regulatory compliance within financial organizations. The investigations of the future must focus on creating mechanisms of fraud detecting in real-time, using larger and more diverse data sets, and more adaptable and adaptable learning systems that can evolve with phishing attacks to sustain the digital banking protection mechanisms.

Phishing Detection, E-Banking Security

1. Aldakheel, E. A., Zakariah, M., Gashgari, G. A., Almarshad, F. A., & Alzahrani, A. I. A. (2023). A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators. Sensors, 23(9), 4403. https://doi.org/10.3390/s23094403 [Google Scholar] [Crossref]

2. Alshingiti, Z., Alaqel, R., Al-Muhtadi, J., Haq, Q. E. U., Saleem, K., & Faheem, M. H. (2023). A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics, 12(1), 232. https://doi.org/10.3390/electronics12010232 [Google Scholar] [Crossref]

3. Ibrahim, N., Rajalakshmi, N.R., Sivakumar, V. et al. (2025). An optimized hybrid ensemble machine learning model combining multiple classifiers for detecting advanced persistent threats in networks. J Big Data 12, 212. https://doi.org/10.1186/s40537-025-01272-w [Google Scholar] [Crossref]

4. Jabir, R., Le, J., & Nguyen, C. (2025). Phishing Attacks in the Age of Generative Artificial Intelligence: A Systematic Review of Human Factors. AI, 6(8), 174. https://doi.org/10.3390/ai6080174. [Google Scholar] [Crossref]

5. Kumari, A., & Nagarjan, C. (2022). The Impact of FinTech and Blockchain Technologies on Banking and Financial Services. Technology Innovation Management Review, 12, 753–767. https://doi.org/10.22215/timreview/1481. [Google Scholar] [Crossref]

6. Li, W., Manickam, S., Chong, Y.w., Leng, W., & Nanda, P. (2024). A State-of-the-Art Review on Phishing Website Detection Techniques. IEEE Access, 12(1), 1–21. https://doi.org/10.1109/ACCESS.2024.3514972. [Google Scholar] [Crossref]

7. Money, U., & Iyoha, A. (2025). ELECTRONIC BANKING CHANNELS AND FINANCIAL PERFORMANCE IN THE NIGERIAN BANKING INDUSTRY. Journal of Accounting, Finance and Risk Management, 8(1), 193–209. https://doi.org/10.61143/umyu-jafr.8(1)2025.013. [Google Scholar] [Crossref]

8. Nadeem, M., Zahra, S., Abbasi, M.N., Arshad, A., Riaz, S., & Ahmed, W. (2023). Phishing Attack, Its Detections and Prevention Techniques. International Journal of Wireless Information Networks, 12(1), 13–25. https://doi.org/10.37591/IJWSN. [Google Scholar] [Crossref]

9. Pinjarkar, L., Hete, P., Mattada, M., Nejakar, S., Agrawal, P., & Kaur, G. (2024). An Examination of Prevalent Online Scams: Phishing Attacks, Banking Frauds, and E-Commerce Deceptions. In Proceedings of the 6th International Conference on Advanced Information Technology (ICAIT), 1–6. https://doi.org/10.1109/ICAIT61638.2024.10690377. [Google Scholar] [Crossref]

10. Saias, J. (2025). Advances in NLP Techniques for Detection of Message-Based Threats in Digital Platforms: A Systematic Review. Electronics, 14(13), 2551. https://doi.org/10.3390/electronics14132551 [Google Scholar] [Crossref]

11. Shahbazi, Z., Jalali, R., & Molaeevand, M. (2025). AI-Based Phishing Detection and Student Cybersecurity Awareness in the Digital Age. Big Data and Cognitive Computing, 9(8), 210. https://doi.org/10.3390/bdcc9080210. [Google Scholar] [Crossref]

• A Study on Customer Awareness on Green Banking Intiatives
• International Financial Reporting Standards and Earnings Management: A Global Research Landscape Analysis
• Analysis of Financial Performance and Operational Efficiency of State Bank of India Using the CAMEL Framework
• Roles and Performance of India Post Payment Bank (IPPB) With Special References to Shivamogga District
• Enhancing Transactional Security in U.S. Banking Systems by Implementing OTP-Based Two-Factor Authentication to Mitigate Debit and Credit Card Fraud