Jaishnoor Kaur, Josef Horáček, Ian Waskom, Sayak Saha Roy · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30963 · open-access PDF
オンラインソーシャルエンジニアリング(SE)詐欺の検出には、しばしばウェブサイトのワークフローを調査し、外部情報源に対する主張を検証し、複数のページにわたって証拠を接続する必要があります。既存の探知器はこれらの調査のための有限なサポートを提供し、アナリストは手動で証拠を収集し、検証することを余儀なくされ、これらの攻撃の検出とその後の取り除きを遅らせます。
Machine translation (Meta M2M100); the English original follows.
Detecting online social engineering (SE) scams often requires examining website workflows, verifying claims against external sources, and connecting evidence across multiple pages. Existing detectors provide limited support for these investigations, leaving analysts to manually gather and validate evidence, which inevitably delays detection and subsequent takedown of these attacks. To address this gap, we present ReDence, an open-source hybrid framework for adaptive, evidence-grounded detection across diverse SE scam categories. ReDence combines two locally hosted models with complementary res
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