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New TSK fuzzy framework achieves 99.95% accuracy in phishing detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new phishing detection system using a Takagi-Sugeno-Kang (TSK) fuzzy inference model. This model is optimized with gradient-based techniques, specifically the Adam optimizer, to combine the interpretability of fuzzy logic with the precision of gradient optimization. Experiments on over 235,000 URLs showed a 99.95% accuracy and a perfect AUC score, demonstrating the framework's effectiveness and explainability for cybersecurity applications. AI

IMPACT Offers a highly accurate and interpretable solution for cybersecurity, potentially improving defenses against sophisticated phishing attacks.

RANK_REASON Academic paper detailing a new model and its performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TSK fuzzy framework achieves 99.95% accuracy in phishing detection

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Academic paper detailing a new model and its performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lohith Srikanth Pentapalli, Jon Salisbury, Josette Riep, Kelly Cohen ·

    A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

    arXiv:2504.18636v2 Announce Type: replace-cross Abstract: Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing…

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