Deep learning approaches in semantic triple generation for knowledge graph population
| dc.contributor.advisor | Joshi, Anupam | |
| dc.contributor.author | Pingle, Aditya | |
| dc.contributor.department | Computer Science and Electrical Engineering | |
| dc.contributor.program | Computer Science | |
| dc.date.accessioned | 2021-01-29T18:12:33Z | |
| dc.date.available | 2021-01-29T18:12:33Z | |
| dc.date.issued | 2019-01-01 | |
| dc.description.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. | |
| dc.format | application:pdf | |
| dc.genre | theses | |
| dc.identifier | doi:10.13016/m2qatd-eczv | |
| dc.identifier.other | 12011 | |
| dc.identifier.uri | http://hdl.handle.net/11603/20720 | |
| dc.language | en | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Theses and Dissertations Collection | |
| dc.relation.ispartof | UMBC Graduate School Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.source | Original File Name: Pingle_umbc_0434M_12011.pdf | |
| dc.subject | cybersecurity | |
| dc.subject | deep learning | |
| dc.subject | knowledge graph | |
| dc.title | Deep learning approaches in semantic triple generation for knowledge graph population | |
| dc.type | Text | |
| dcterms.accessRights | Distribution Rights granted to UMBC by the author. | |
| dcterms.accessRights | Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission. | |
| dcterms.accessRights | This 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. |