2019/05/07 by Aditya Pingle, Aritran Piplai, Pingle, Aditya +9 · 1 citation
Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Cryptography and Security (cs.CR) #Data Quality and Management #FOS: Computer and information sciences #Information and Cyber Security
paper · pdf · doi:10.48550/arxiv.1905.02497
openalex publication_date 2019/05/07 · openalex created_date 2020/09/21 · openalex updated_date 2026/07/28
Security Analysts that work in a `Security Operations Center' (SoC) play a\nmajor role in ensuring the security of the organization. The amount of\nbackground knowledge they have about the evolving and new attacks makes a\nsignificant difference in their ability to detect attacks. Open source threat\nintelligence sources, like text descriptions about cyber-attacks, can be stored\nin a structured fashion in a cybersecurity knowledge graph. A cybersecurity\nknowledge graph can be paramount in aiding a security analyst to detect cyber\nthreats because it stores a vast range of cyber threat information in the form\nof semantic triples which can be queried. A semantic triple contains two\ncybersecurity entities with a relationship between them. In this work, we\npropose a system to create semantic triples over cybersecurity text, using deep\nlearning approaches to extract possible relationships. We use the set of\nsemantic triples generated through our system to assert in a cybersecurity\nknowledge graph. Security Analysts can retrieve this data from the knowledge\ngraph, and use this information to form a decision about a cyber-attack.\n