English(EN) A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

新的MLP框架以99.24%的准确率检测钓鱼URL

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一个新的、高效的实时钓鱼URL检测框架。该系统采用混合方法，结合了黑名单筛选和分析结构化URL特征的多层感知器（MLP）分类器。它通过避免复杂的内容分析或外部API调用来实现高准确性和速度，使其适用于资源受限的环境。一个名为CyberGuard的原型应用程序展示了其在实际部署中的潜力。 AI

影响这项研究为实时钓鱼URL检测提供了一种更有效、更准确的方法，有可能增强网络安全防御能力。

排序理由这是一篇详细介绍新的钓鱼URL检测框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 这是一篇详细介绍新的钓鱼URL检测框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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, other; 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: 127 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

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arXiv cs.LG TIER_1 English(EN) · Uche Unoke Emmanuel, Gideon Francis Oghie · 2026-06-02 04:00

一种基于轻量级混合MLP的框架，用于使用结构化URL特征进行实时网络钓鱼URL检测

arXiv:2606.00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-based detection approaches are reactive and often fail to identify newly emerging …

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一种基于轻量级混合MLP的框架，用于使用结构化URL特征进行实时网络钓鱼URL检测

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