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.
OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models
Department of Information Engineering, University of Brescia, Italy（博洛尼亚大学信息工程系，意大利）；Department of Computer Science, University of Turin, Italy（都灵大学计算机科学系，意大利）；School of Computer Science and Informatics, Cardiff University, United Kingdom（卡迪夫大学计算机科学与信息学学院，英国）；Department of Electronics and Computer Science, University of Southampton, United Kingdom（南安普顿大学电子与计算机科学系，英国）
Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies
Department of Information Engineering, University of Brescia, Italy（布雷西亚大学信息工程系）；School of Computer Science and Informatics, Cardiff University, United Kingdom（卡迪夫大学计算机科学与信息学学院）；Department of Electronics and Computer Science, University of Southampton, United Kingdom（南安普顿大学电子与计算机科学系）
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization
Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）；Advanced Research in Cyber Systems, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室先进网络系统研究部）；Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County（马里兰大学巴尔的摩分校计算机科学与电气工程系）
