OPTIMIZING ADAPTIVE NEURO-FUZZY SYSTEMS FOR ENHANCED PHISHING DETECTION
DOI:
https://doi.org/10.55640/ijidml-v02i05-02Keywords:
Adaptive neuro-fuzzy systems, Phishing detection, Machine learning, CybersecurityAbstract
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.
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).
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