International Journal of Scientific Research and Engineering Development

In the modern digital era, cybersecurity threats are increasing rapidly, making traditional security mechanisms Insufficient to handle complex and evolving attacks. Threat Knowledge Graphs (TKGs) have emerged as an effective approach for representing, analysing , and understanding cybersecurity threats in a structured manner. This paper presents a comprehensive study on the construction and application of threat knowledge graphs. The proposed approach focuses on integrating heterogeneous security data sources such as logs, alerts, and vulnerability reports into a unified graph structure. Entities such as attackers, vulnerabilities, and attack techniques are represented as nodes, while their relationships are modeled as edges. This enables efficient threat analysis, pattern recognition, and prediction of future attacks. The study also highlights the use of graph-based techniques and machine learning methods to enhance threat intelligence. Experimental analysis shows that knowledge graphs improve threat detection accuracy and provide better visualization of attack patterns. The proposed system is highly scalable and adaptable for realworld cybersecurity environments.

Uma Maheswar Ch, Lalith Vikkash E, Jyoshna D, Mr. G Brahmaji,"A Cybersecurity Knowledge Graph for Advanced Persistent Threat Organization Attribution" International Journal of Scientific Research and Engineering Development, V9(2): Page(1735-1738) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.