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Enhancing cybersecurity through autonomous knowledge graph construction by integrating heterogeneous data sources.

Hatoon Alharbi1, Ali Hur2, Hasan Alkahtani1

  • 1Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, Eastern Province, Al-Ahsa, Saudi Arabia.

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|June 26, 2025
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Summary
This summary is machine-generated.

This study introduces a Cybersecurity Knowledge Graph (CKG) to enhance cyber threat detection. By integrating diverse data and using NLP, the CKG improves cybersecurity professionals' ability to analyze and mitigate online threats effectively.

Keywords:
Cybersecurity knowledge graphDeduction ruleGraph analytics algorithmNatural language processing.

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Area of Science:

  • Computer Science
  • Information Security

Background:

  • Cybersecurity is vital for modern society.
  • Knowledge graphs can enhance cybersecurity and privacy.
  • Organizations need proactive threat detection and mitigation.

Purpose of the Study:

  • To construct an efficient Cybersecurity Knowledge Graph (CKG).
  • To autonomously integrate heterogeneous data sources for CKG creation.
  • To enhance CKG with logical rules and graph analytics for improved threat analysis.

Main Methods:

  • Autonomous construction of a Cybersecurity Knowledge Graph (CKG).
  • Integration of heterogeneous structured and unstructured data.
  • Application of Natural Language Processing (NLP) for text data extraction.
  • Enhancement using logical rules and graph analytic algorithms.

Main Results:

  • Developed a functional Cybersecurity Knowledge Graph (CKG).
  • Demonstrated improved data analysis and threat visibility for cybersecurity experts.
  • Validated logical rules through query formulation and analysis.

Conclusions:

  • The CKG empowers cybersecurity experts with better data analysis capabilities.
  • The approach enhances the proactive detection and mitigation of cyber threats.
  • The research contributes to safeguarding cyberspace from malicious activities.