ReDence: A Hybrid Agentic and Decision-Model Framework for Rapid, Evidence-Grounded Detection of Modern Social Engineering Scams
ReDence combines agentic web-browsing with decision models to detect multi-step social-engineering scams — a research step toward automated fraud-site triage that current URL filters miss.
Summary written by editorial AI · Source link below
arXiv:2610.30963v1 Announce Type: new Abstract: 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 fra
Editorial Analysis
Social-engineering scams increasingly use multi-page workflows and external claim verification to appear legitimate; automated evidence-chaining detection could reduce analyst burden.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the Research Desk
- Signed, Sealed, and Relayed, NTLM-style: Bypassing LDAP Signing Through Reauthentication3d
- Not Where You Think: Protecting Location Privacy Against Reasoning-Based Geolocation4d
- Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure4d
- Practical Auditing of Differentially Private Graph Release Mechanisms: Methodology, Toolkit, and Case Study4d
- SecLog-Bench: A Scalable Benchmark for Cybersecurity Log Reasoning with Scaffolded Evidence4d