Recognizing and Extracting Cybersecurtity-relevant Entities from Text
원문 초록
Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to efficiently and accurately extract meaningful insights from CTI. We have created an initial unstructured CTI corpus from a variety of open sources that we are using to train and test cybersecurity entity models using the spaCy framework and exploring self-learning methods to automatically recognize cybersecurity entities. We also describe methods to apply cybersecurity domain entity linking with existing world knowledge from Wikidata. Our future work will survey and test spaCy NLP tools and create methods for continuous integration of new information extracted from text.
함께 살펴볼 논문
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KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models
DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
Cross-Document Contextual Coreference Resolution in Knowledge Graphs
토론
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