Phishing is perpetrated by using electronic devices to deceive users of internet in order to extract valuable information from such users using fake website which resembles the original website they clone. Clicking on the fake website redirects the users to the fake website and then valid information is extracted from the users. Phishing has made many to lose their financial credentials to criminals and within few seconds, their hard-earned income disappeared into thin air. The breach of privacy alone is a threat to internet users and it is a criminal offense. This study developed self-organizing map model that is capable of preventing real-time phishing of -financial data. The methodology adopted in this study is a rule-based approach which ensures that features relevant to the study are preprocessed for learning of the model in preventing real-time phishing. The rule-based approach classified the URL to phishing and legitimate/benign in which the phishing URLs is blocked and prevented from opening. SOM is a clustering algorithm; result showed that it is highly effective in detecting and preventing phishing. Validating the SOM model showed Openphish dataset have accuracy of 95.00% and UCI dataset has accuracy of 86.00% respectively. The SOM model from the Openphish outperformed the model from UCI dataset. Using machine learning, especially artificial neural network is a proactive defense mechanism that marks a paradigm shift in cybersecurity. Harnessing the power of advanced analytics and pattern recognition makes SOM a more intelligent, adaptive, and efficient defense technique. It was suggested that users of internet should be educated on regular basis on how to avoid being phished on the internet.

Keywords: Phishing, Self-Organizing Map, Fastapi, Man-in-the-Middle, Neural, Network, Cybersecurity.

[1] Basnet, R.B., Shash, R., Johnson, C., Walgren, L., & Doleck, T. (2019). Towards detecting and classifying network intrusion traffic using deep learning frameworks. Journal of Internet Services and Information Security, 9(4): 1–17. https://doi.org/10.22667/jisis.2019.11.30.001.

[2] Alkhalil, Z., Hewage, C., Nawaf, L., & Khan, I. (2021). Phishing attacks: A recent comprehensive study and a new anatomy. Frontiers in Computer Science, 3: 563060. https://doi.org/10.3389/fcomp.2021.563060.

[3] Omari, K. (2023). Comparative study of machine learning algorithms for phishing website detection. International Journal of Advanced Computer Science and Applications, 14(9): 417–425. https://doi.org/10.14 569/ijacsa.2023.0140945.

[4] Simion, C.A., Gavrilut, D.T., & Luchian, H. (2019). An adversarial machine learning approach to evaluate the robustness of a security solution. In Proceedings of the 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2019).

[5] Kamal, O. (2023). Comparative study of machine learning algorithms for phishing website detection. International Journal of Advanced Computer Science and Applications, 14(9): 417–423. 

[6] Rao, R.S., & Ali, S.T. (2015). PhishShield: A desktop application to detect phishing webpages through heuristic approach. Procedia Computer Science, 54: 147–156. https://doi.org/10.1016/j.procs.2015.06.017.

[7] Pei, L., Ghimire, M., Byanjankar, N., Joshi, M., Mushtaq, N., Ji, M., & Joshi, T.P. (2022). Impacts of land use on surface water quality using self-organizing map in middle region of the Yellow River Basin, China. Water, 14(17): 2736. https://doi.org/10.3390/w14172736.

[8] Ahmed, A.A., & Abdullah, N.A. (2016). Real time detection of phishing websites. In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON): 1–6, IEEE. 

[9] Sarker, I.H. (2020). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3): 160. https://doi.org/10.1007/s42979-021-00592-x.

[10] Kaur, A., & Mian, S.M. (2023). A review on phishing technique: Classification, lifecycle and detection approaches. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Pages 336–339, IEEE. https://doi.org/10.1109/icacite57410.2023.10182964.

[11] Kowsher, M.D., Tahabilder, A., Sanjid, Z.I., Prottasha, N.J., Uddin, S., Hossain, A., & Jilani, F.A.K. (2021). LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy. Procedia Computer Science, 193: 131–140. https://doi.org/10.1016/j.procs.2021.10.014.

[12] Ahmad, S.A., Mohammed, B.K., Fqi, K.O., Karim, F.R., & Rafiq, S.K. (2024). Soft computing modeling including artificial neural network, non-linear, and linear regression models to predict the compressive strength of sustainable mortar modified with palm oil fuel ash. Construction, 4(1): 52–67. 

[13] Gugnani, G., Rawat, A., Shastri, M., & Kumar, P. (2015). Anomaly recognition in online social networks. International Journal of Security and Its Applications, 9(7): 109–118. 

[14] Back, S., & Guerette, R.T. (2021). Cyber place management and crime prevention: The effectiveness of cybersecurity awareness training against phishing attacks. Journal of Contemporary Criminal Justice, 37(3): 427–451. https://doi.org/10.1177/10439862211001628.

[15] Ansari, M.F., Sharma, P.K., & Dash, B. (2022). Prevention of phishing attacks using AI-based cyber security awareness training. International Journal of Smart Sensor and Adhoc Network, 3(3): 61–72. 

[16] Shahrivari, V., Darabi, M.M., & Izadi, M. (2020). Phishing detection using machine learning techniques. ArXiv. https://doi.org/10.48550/arxiv.2009.11116.

[17] Alshantti, A., & Rasheed, A. (2021). Self-organising map-based framework for investigating accounts suspected of money laundering. Frontiers in Artificial Intelligence, 4: 761925. 

[18] Breskuvienė, D., & Dzemyda, G. (2024). Enhancing credit card fraud detection: Highly imbalanced data case. Journal of Big Data, 11: 182. https://doi.org/10.1186/s40537-024-01059-5.

[19] Anjana, U., & Divyashri, C. (2026). Artificial intelligence-enhanced weighted self-organizing map for accurate weather forecasting and crop prediction in agriculture. International Journal of Engineering Research & Technology, 15(1).

[20] Meyer, S.G., Reading, A.M., & Bassom, A.P. (2022). The use of weighted self-organizing maps to interrogate large seismic data sets. Geophysical Journal International, 231(3): 2156–2172.

[21] Deokar, S., Khandare, P., & Dixit, A. (2020). Improvement of traditional protection system in the existing hybrid microgrid with advanced intelligent method. International Journal of Data Science, 1(2): 72–81. https://doi. org/10.53188/ijds.2020.12.72.

[22] Asker, N.S., & Essa, E.I. (2024). A review of social engineering attack detection based on machine learning techniques. Nanotechnology Perceptions, 20(s2): 636–641.

[23] Eyassu, S., & Gambäck, B. (2005). Classifying Amharic news text using self-organizing maps. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages: 71–78, Association for Computational Linguistics. https://doi.org/10.3115/1621787.1621801.

[24] Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6): 391–407. 

[25] Ličen, S., Di-Gilio, A., Palmisani, J., Petraccone, S., de-Gennaro, G., & Barbieri, P. (2020). Pattern recognition and anomaly detection by self-organizing maps in a multi month e-nose survey at an industrial site. Sensors, 20(7): 1887. https://doi.org/10.3390/s20071887.

[26] Saarikoski, J., Järvelin, K., Laurikkala, J., & Juhola, M. (2009). On document classification with self-organising maps. In International Conference on Adaptive and Natural Computing Algorithms, Pages 140–149, Springer. https://doi.org/10.1007/978-3-642-04921-7_15.

[27] Saarikoski, J., Laurikkala, J., Järvelin, K., & Juhola, M. (2011). Self-organising maps in document classification: A comparison with six machine learning methods. In International Conference on Adaptive and Natural Computing Algorithms: 260–269, Springer. https://doi.org/10.1007/978-3-642-20282-7_27.

[28] Khacef, L., Rodriguez, L., & Miramond, B. (2020). Improving self-organizing maps with unsupervised feature extraction. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H., & King, I. (Eds.), Neural Information Processing: ICONIP 2020, Pages 474–486, Springer. https://doi.org/10.1007/978-3-030-63833-7_40.

[29] Sandoval-Lara, Z., Gómez-Gil, P., Moreno-Rodríguez, J.C., & Ramírez-Cortés, M. (2023). Self-organizing clustering by Growing-SOM for EEG-based biometrics. In 2023 International Conference on Artificial Intelligence and Applications (ICAIA), Pages 1–4, IEEE. https://doi.org/10.1109/icaiaatcon60371.2023.10551669.

[30] Fan, X., Zhang, S., Xue, X., Jiang, R., Fan, S., & Kou, H. (2025). An improved self-organizing map (SOM) based on virtual winning neurons. Symmetry, 17(3): 449. https://doi.org/10.3390/sym17030449.

[31] Cui, W., Xue, W., Li, L., & Shi, J.A. (2020). Method for intermittent fault diagnosis of electronic equipment based on labeled SOM. In 2020 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Pages 149–154, IEEE. https://doi.org/10.1109/sdpc49795.2020.9352908.

[32] Zhang, X., Xu, J., & Zhang, H. (2023). Fault diagnosis of levitation controller of medium-speed maglev train based on SOM-BP neural network. In Proceedings of the 2023 35th Chinese Control and Decision Conference (CCDC), Pages 4162–4167, IEEE. https://doi.org/10.1109/ccdc58219.2023.10326703.

[33] Iftikhar, N., Rehman, M.U., Shah, M.A., Alenazi, M.J.F., & Ali, J. (2025). Intrusion detection in NSL-KDD dataset using hybrid self-organizing map model. Computer Modeling in Engineering & Sciences, 143(1): 639–671. https://doi.org/10.32604/cmes.2025.062788.

[34] Zadeh-Shirazi, A., Seyyed Mahdavi Chabok, S.J., & Mohammadi, Z. (2018). A novel and reliable computational intelligence system for breast cancer detection. Medical & Biological Engineering & Computing, 56(5): 721–732. https://doi.org/10.1007/s11517-017-1721-z.

[35] Chen, W., An, N., Jiang, M., & Jia, L. (2024). An improved deep temporal convolutional network for new energy stock index prediction. Information Sciences, 682: 121244. https://doi.org/10.1016/j.ins.2024.121244.

Source of Funding:

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Competing Interests Statement:

The authors have declared that no competing financial, professional, or personal interests exist.

Consent for publication:

All the authors contributed to the manuscript and consented to the publication of this research work.

Authors' contributions:

All the authors took part in literature review, analysis, and manuscript writing equally.

Availability of data and materials:

Supplementary information is available from the authors upon reasonable request.

Ethical Approval:

Not applicable for this study.

Institutional Review Board Statement:

Not applicable for this study.