English(EN) CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification

新框架CoGReV通过AI推理增强网络钓鱼检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了CoGReV，一个旨在通过整合机器学习和非单调推理来改进网络钓鱼网站检测的新框架。该系统使用一个置信门控规则来修正预测，特别是在有网站元数据可用时，将不确定的分类降级为“合法”。这种方法旨在减少误报和分析师的警报疲劳，同时不显著影响实际网络钓鱼网站的检测率。 AI

影响该框架通过提高网络钓鱼检测系统的准确性，可以减轻人工分析师的警报疲劳。

排序理由该集群描述了一篇关于用于AI网络钓鱼检测的新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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

本文如何被排名

Signal score: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇关于用于AI网络钓鱼检测的新颖框架的最新研究论文。[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, product; 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: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti · 2026-09-10 04:00

CoGReV：一种置信度门控的、事后非单调信念修正框架，用于网络钓鱼网站分类

arXiv:2604.25512v3 Announce Type: replace Abstract: In phishing detection, machine learning classifiers act as a first line of defense, but the false positives they produce are triaged by human analysts. The excessive false alarms cause alert fatigue that erodes human oversight. …