  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.
