eISSN: 3087-4262 editor@aimjournals.com
All Journals
Submit your article

International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

OPTIMIZING ADAPTIVE NEURO-FUZZY SYSTEMS FOR ENHANCED PHISHING DETECTION

DOI https://doi.org/10.55640/ijidml-v02i05-02
pdf
Dr. Samuel Moyo 1 ,
4 School of Information Technology, University of Cape Town, South Africa

Abstract

Phishing attacks continue to pose a significant and evolving threat to individuals and organizations, leading to substantial financial losses and compromising sensitive information [1]. Traditional detection methods, often reliant on static blacklists or rule-based systems, struggle to keep pace with the dynamic nature and increasing sophistication of these scams. This article explores the critical role of parameter optimization within Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for developing intelligent and robust phishing detection capabilities. ANFIS, by combining the learning capabilities of neural networks with the interpretability of fuzzy logic, offers a powerful framework for classifying malicious web content. The paper details how fine-tuning ANFIS parameters, which govern the system's learning and inference processes, can significantly enhance its accuracy, reduce false positives, and improve its adaptability to novel phishing tactics, including zero-hour attacks. The discussion highlights the advantages of such optimized systems in providing a more resilient defense against the persistent threat of phishing.

Keywords

Adaptive neuro-fuzzy systems, Phishing detection, Machine learning, Cybersecurity

References

📄 Financial Fraud Action UK, Cheque & Credit Clearing Company, UKCARDS Association. (2012). Deception crimes drive small increase in card fraud and online banking fraud losses. Press Release, p. 2. Retrieved July 24, 2013, from www.financialfraudaction.org.uk
📄 Aburrous, M., Hossain, M. A., Dahal, K., & Thabtah, F. (2009). Modelling intelligent phishing detection system for e-banking using fuzzy data mining. International.
📄 Sanglerdsinlapachai, N., & Rungsawang, A. (2010). Using domain top-page similarity feature in machine learning-based web phishing detection. In Proceedings of IEEE 3rd International Conference on Knowledge Discovery and Data Mining (pp. 187–190).
📄 Xiang, G., Pendleton, B. A., & Hong, J. (2009). Modelling content from human-verified blacklist for accurate zero-hour phish detection: Probabilistic approach for zero hour phish detection. In Proceedings of the 15th European Conference.
📄 Ma, J., Saul, L., Savage, S., & Voelker, G. (2009). Beyond blacklists: Learning to detect malicious websites from suspicious URLs. In Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining (pp. 1245–1254). Paris, France.
📄 PhishTank Site Checker. (2013). GS! Networks. Retrieved February 22, 2014, from https://addons.mozilla.org/en-US/firefox/addon/phishtanksitechecker/reviews/
📄 Whittaker, C., Ryner, B., & Nazif, M. (2010). Large-scale automatic classification of phishing pages. In 17th Annual Network and Distributed System Security (NDSS ’10) Symposium.
📄 Sheng, S., Wardman, B., Warner, G., Cranor, L., Hong, J., & Zhang, C. (2009). An empirical analysis of phishing blacklists. In Proceedings of the 6th Conference on Email and Anti-Spam.
📄 Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3).
📄 Suriya, R., Saravanan, K., & Thangavelu, A. (2009). An integrated approach to detect phishing mail attacks: A case study. In Proceedings of the 2nd International Conference on Security of Information and Networks (pp. 193–199). North Cyprus, Turkey: ACM.
📄 Wenyin, L., Fang, N., Quan, X., Qiu, B., & Liu, G. (2010). Discovering phishing targets based on semantic link network. Future Generation Computer Systems, 26(3), 381–388.
📄 Xiang, G., Pendleton, B. A., Hong, J. I., & Rose, C. P. (2010). A hierarchical adaptive phishing detection system. In Symposium on Research in Computer Security (ESORICS ’10) (pp. 268–285).
📄 Dong, X., Clerk, J. A., & Jacob, J. L. (2010). Defending the weakest link: Phishing website detection by analysing user behaviours. IEEE Telecommunications Systems, 45, 215–226.
📄 Wardman, B., Stallings, T., Warner, G., & Skjellum, A. (2011). High-performance content-based phishing attack detection. In eCrime Researchers Summit (eCrime) (pp. 1–9). San Diego, CA.
📄 Barraclough, A. P., Hossain, M. A., Tahir, M. A., Sexton, G., & Aslam, N. (2013). Intelligent phishing detection and protection scheme for online transactions. Expert Systems with Applications, 40, 4697–4706.
📄 Le, A., Markopoulou, A., & Faloutsos, M. (2010). PhishDef: URL names say it all. In INFOCOM, Proceedings IEEE (pp. 191–195).

Similar Articles

1-10 of 26

You may also start an advanced similarity search for this article.