RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement
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2019-05-16
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Citation of Original Publication
Aditya Pingle, Aritran Piplai, Sudip Mittal, Anupam Joshi, James Holt, and Richard Zak. 2019. RelExt: relation extraction using deep learning approaches for cybersecurity knowledge graph improvement. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM ’19). Association for Computing Machinery, New York, NY, USA, 879–886. DOI:https://doi.org/10.1145/3341161.3343519
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© 2019 Association for Computing Machinery.
© 2019 Association for Computing Machinery.
Abstract
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.