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

## TL;DR

This paper presents RelExt, a deep learning-based system for extracting semantic triples from cybersecurity texts to enhance knowledge graphs, aiding security analysts in threat detection.

## Contribution

It introduces a novel deep learning approach for relation extraction in cybersecurity texts to improve knowledge graph construction.

## Key findings

- Effective extraction of cybersecurity relations demonstrated
- Enhanced knowledge graph accuracy for threat detection
- Supports security analysts in decision-making

## Abstract

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.