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dc.contributor.authorPingle, Aditya
dc.contributor.authorPiplai, Aritran
dc.contributor.authorMittal, Sudip
dc.contributor.authorJoshi, Anupam
dc.contributor.authorHolt, James
dc.contributor.authorZak, Richard
dc.date.accessioned2019-06-11T18:12:13Z
dc.date.available2019-06-11T18:12:13Z
dc.date.issued2019-05-16
dc.description.abstractSecurity 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.en_US
dc.description.urihttps://arxiv.org/abs/1905.02497en_US
dc.format.extent8 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2h1ov-6xex
dc.identifier.citationAditya Pingle, et.al, RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement, https://arxiv.org/abs/1905.02497en_US
dc.identifier.urihttp://hdl.handle.net/11603/14049
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectcybersecurityen_US
dc.subjectdeep learningen_US
dc.subjectknowledge graphsen_US
dc.titleRelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvementen_US
dc.typeTexten_US


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