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

Abstract

Cybersecurity plays a critical role in today’s modern human society, and leveraging knowledge graphs can enhance cybersecurity and privacy in the cyberspace. By harnessing the heterogeneous and vast amount of information on potential attacks, organizations can improve their ability to proactively detect and mitigate any threat or damage to their online valuable resources. Integrating critical cyberattack information into a knowledge graph offers a significant boost to cybersecurity, safeguarding cyberspace from malicious activities. This information can be obtained from structured and unstructured data, with a particular focus on extracting valuable insights from unstructured text through natural language processing (NLP). By storing a wide range of cyber threat information in a semantic triples form which machines can interpret autonomously, cybersecurity experts gain improved visibility and are better equipped to identify and address cyber threats. However, constructing an efficient knowledge graph poses challenges. In our research, we construct a cybersecurity knowledge graph (CKG) autonomously using heterogeneous data sources. We further enhance the CKG by applying logical rules and employing graph analytic algorithms. To evaluate the effectiveness of our proposed CKG, we formulate a set of queries as questions to validate the logical rules. Ultimately, the CKG empowers experts to efficiently analyze data and gain comprehensive understanding of cyberattacks, thereby help minimize potential attack vectors.

Keywords

• Cybersecurity knowledge graph
• Deduction rule
• Graph analytics algorithm
• Natural language processing.