# RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement

arXiv: https://arxiv.org/abs/1905.02497 · HTML: https://mlsanity.com/paper/1905.02497/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 12 of 12)

- [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021)
- [Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI](https://mlsanity.com/paper/2211.01753/) (2022)
- [LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge](https://mlsanity.com/paper/2401.10036/) (2024)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [Machine learning on knowledge graphs for context-aware security monitoring](https://mlsanity.com/paper/2105.08741/) (2021)
- [PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification](https://mlsanity.com/paper/2404.19744/) (2024)
- [Recognizing and Extracting Cybersecurtity-relevant Entities from Text](https://mlsanity.com/paper/2208.01693/) (2022)
- [Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization](https://mlsanity.com/paper/2403.16222/) (2024)
- [Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs](https://mlsanity.com/paper/2605.15904/) (2026)
- [Knowledge mining of unstructured information: application to cyber-domain](https://mlsanity.com/paper/2109.03848/) (2021)
- [A Deep Learning Approach for Ontology Enrichment from Unstructured Text](https://mlsanity.com/paper/2112.08554/) (2021)
- [Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults](https://mlsanity.com/paper/2509.25418/) (2025)

## Abstract (arXiv)

Security Analysts that work in a `Security Operations Center' (SoC) play a major role in ensuring the security of the organization. The amount of background knowledge they have about the evolving and new attacks makes a significant difference in their ability to detect attacks. Open source threat intelligence sources, like text descriptions about cyber-attacks, can be stored in a structured fashion in a cybersecurity knowledge graph. A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried. A semantic triple contains two cybersecurity entities with a relationship between them. In this work, we propose a system to create semantic triples over cybersecurity text, using deep learning approaches to extract possible relationships. We use the set of semantic triples generated through our system to assert in a cybersecurity knowledge graph. Security Analysts can retrieve this data from the knowledge graph, and use this information to form a decision about a cyber-attack.