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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
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OPTIMIZING ADAPTIVE NEURO-FUZZY SYSTEMS FOR ENHANCED PHISHING DETECTION

DOI https://doi.org/10.55640/ijidml-v02i05-02
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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

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