@inproceedings{roy-etal-2019-predicting,
title = "Predicting Malware Attributes from Cybersecurity Texts",
author = "Roy, Arpita and
Park, Youngja and
Pan, Shimei",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1293",
doi = "10.18653/v1/N19-1293",
pages = "2857--2861",
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.",
}
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%0 Conference Proceedings
%T Predicting Malware Attributes from Cybersecurity Texts
%A Roy, Arpita
%A Park, Youngja
%A Pan, Shimei
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F roy-etal-2019-predicting
%X 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.
%R 10.18653/v1/N19-1293
%U https://aclanthology.org/N19-1293
%U https://doi.org/10.18653/v1/N19-1293
%P 2857-2861
Markdown (Informal)
[Predicting Malware Attributes from Cybersecurity Texts](https://aclanthology.org/N19-1293) (Roy et al., NAACL 2019)
ACL
- Arpita Roy, Youngja Park, and Shimei Pan. 2019. Predicting Malware Attributes from Cybersecurity Texts. In 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), pages 2857–2861, Minneapolis, Minnesota. Association for Computational Linguistics.