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

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