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New CTiKG framework boosts threat intelligence extraction accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called CTiKG to improve the extraction of threat entities and their relationships from cybersecurity reports. This framework utilizes a hybrid NLP model that combines SecureBERT contextual embeddings with domain-specific knowledge from an ontology. Experiments show CTiKG achieves significant gains in Named Entity Recognition (NER) and Relation Extraction (RE) performance compared to existing methods, with improvements of 3-4% in NER and up to 8% in RE on a specialized dataset. AI

IMPACT Enhances accuracy in extracting threat intelligence, potentially improving cybersecurity response times and efficiency.

RANK_REASON Academic paper introducing a new framework and demonstrating improved performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New CTiKG framework boosts threat intelligence extraction accuracy

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Academic paper introducing a new framework and demonstrating improved performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · sherif Saad ·

    Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs

    Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable solutions for automating proactive and real-time security responses. Their increasing adoption has significantly enhanced the workflow…

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