Predicting Malware Attributes from Cybersecurity Texts
| dc.contributor.author | Roy, Arpita | |
| dc.contributor.author | Park, Youngja | |
| dc.contributor.author | Pan, Shimei | |
| dc.date.accessioned | 2019-10-22T15:34:13Z | |
| dc.date.available | 2019-10-22T15:34:13Z | |
| dc.date.issued | 2019-06 | |
| dc.description | Proceedings of NAACL-HLT 2019, Minneapolis, Minnesota, June 2 - June 7, 2019. | |
| dc.description.abstract | Text analytics is a useful tool for studying malware behavior and tracking emerging threats. The task of automated malware attribute identification based on cybersecurity texts is very challenging due to a large number of malware attribute labels and a small number of training instances. In this paper, we propose a novel feature learning method to leverage diverse knowledge sources such as small amount of human annotations, unlabeled text and specifications about malware attribute labels. Our evaluation has demonstrated the effectiveness of our method over the state-of-the-art malware attribute prediction systems. | en_US |
| dc.description.uri | https://www.aclweb.org/anthology/N19-1293/ | en_US |
| dc.format.extent | 5 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m2nudh-qg7h | |
| dc.identifier.citation | Arpita Roy, Youngja Park, Shimei Pan, Predicting Malware Attributes from Cybersecurity Texts, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), DOI: 10.18653/v1/N19-1293 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.18653/v1/N19-1293 | |
| dc.identifier.uri | http://hdl.handle.net/11603/15951 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Association for Computational Linguistics | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | 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. | |
| dc.subject | malware attributes | en_US |
| dc.subject | cybersecurity text | en_US |
| dc.subject | training instances | en_US |
| dc.title | Predicting Malware Attributes from Cybersecurity Texts | en_US |
| dc.type | Text | en_US |