English(EN) A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

新的TSK模糊框架在网络钓鱼检测中达到99.95%的准确率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一种使用Takagi-Sugeno-Kang（TSK）模糊推理模型的新型网络钓鱼检测系统。该模型通过基于梯度的技术（特别是Adam优化器）进行优化，将模糊逻辑的可解释性与梯度优化的精确性相结合。在超过235,000个URL上进行的实验显示，准确率为99.95%，AUC得分为完美，证明了该框架在网络安全应用中的有效性和可解释性。 AI

影响为网络安全提供了一种高精度且可解释的解决方案，有可能加强对复杂网络钓鱼攻击的防御。

排序理由详细介绍新模型及其性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 10 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍新模型及其性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Lohith Srikanth Pentapalli, Jon Salisbury, Josette Riep, Kelly Cohen · 2026-10-19 04:00

用于可解释网络钓鱼检测的梯度优化TSK模糊框架

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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用于可解释网络钓鱼检测的梯度优化TSK模糊框架

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