Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs
| dc.contributor.author | Mitra, Shaswata | |
| dc.contributor.author | Piplai, Aritran | |
| dc.contributor.author | Mittal, Sudip | |
| dc.contributor.author | Joshi, Anupam | |
| dc.date.accessioned | 2022-08-18T22:33:07Z | |
| dc.date.available | 2022-08-18T22:33:07Z | |
| dc.date.issued | 2022-01-13 | |
| dc.description | 2021 IEEE International Conference on Big Data (Big Data),15-18 December 2021, Orlando, FL, USA | |
| dc.description.abstract | Today there is a significant amount of fake cybersecurity related intelligence on the internet. To filter out such information, we build a system to capture the provenance information and represent it along with the captured Cyber Threat Intelligence (CTI). In the cybersecurity domain, such CTI is stored in Cybersecurity Knowledge Graphs (CKG). We enhance the exiting CKG model to incorporate intelligence provenance and fuse provenance graphs with CKG. This process includes modifying traditional approaches to entity and relation extraction. CTI data is considered vital in securing our cyberspace. Knowledge graphs containing CTI information along with its provenance can provide expertise to dependent Artificial Intelligence (AI) systems and human analysts. | en_US |
| dc.description.sponsorship | This work was supported by a U.S. Department of Defense grant and National Science Foundation grant #2133190. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/9671867 | en_US |
| dc.format.extent | 8 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2otqh-m4wz | |
| dc.identifier.citation | Mitra, Shaswata, Aritran Piplai, Sudip Mittal, and Anupam Joshi. “Combating Fake Cyber Threat Intelligence Using Provenance in Cybersecurity Knowledge Graphs.” In 2021 IEEE International Conference on Big Data (Big Data), 3316–23, 2021. https://doi.org/10.1109/BigData52589.2021.9671867. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/BigData52589.2021.9671867 | |
| dc.identifier.uri | http://hdl.handle.net/11603/25497 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| 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 Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
| dc.subject | UMBC Ebiquity Research Group | en_US |
| dc.title | Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0001-9151-8347 | en_US |
| dcterms.creator | https://orcid.org/0000-0002-8641-3193 | en_US |