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CyberCane uses neuro-symbolic RAG for privacy-preserving phishing detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CyberCane, a novel neuro-symbolic framework designed for privacy-preserving phishing detection. This system combines symbolic analysis with retrieval-augmented generation (RAG) to handle sensitive data and comply with regulations. CyberCane utilizes an OWL ontology called PhishOnt for verifiable attack classification and has demonstrated significant improvements in detecting AI-generated threats while maintaining high precision. AI

IMPACT Enhances privacy-preserving AI capabilities for security applications, potentially reducing false positives and improving detection of AI-generated threats.

RANK_REASON This is a research paper detailing a new framework for phishing detection.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CyberCane uses neuro-symbolic RAG for privacy-preserving phishing detection

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This is a research paper detailing a new framework for phishing detection.
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High
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133 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Safayat Bin Hakim, Aniqa Afzal, Qi Zhao, Vigna Majmundar, Pawel Sloboda, Houbing Herbert Song ·

    CyberCane: Neuro-Symbolic RAG for Privacy-Preserving Phishing Detection with Formal Ontology Reasoning

    arXiv:2604.23563v1 Announce Type: cross Abstract: Privacy-critical domains require phishing detection systems that satisfy contradictory constraints: near-zero false positives to prevent workflow disruption, transparent explanations for non-expert staff, strict regulatory complia…

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