Predicting Malware Attributes from Cybersecurity Texts

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N19-1293.pdf (1.02 MB)

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https://www.aclweb.org/anthology/N19-1293/

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http://dx.doi.org/10.18653/v1/N19-1293
 http://hdl.handle.net/11603/15951

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UMBC Information Systems Department
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Date

2019-06

Type of Work

conference papers and proceedings
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Citation of Original Publication

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

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Subjects

malware attributes
cybersecurity text
training instances

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.