UMBC at SemEval-2018 Task 8: Understanding Text about Malware

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S18-1142.pdf (272.73 KB)

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https://www.aclweb.org/anthology/S18-1142/

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http://hdl.handle.net/11603/11641
 10.18653/v1/S18-1142

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UMBC Information Systems Department
UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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Date

2018-06-05

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conference papers and proceedings
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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.
Attribution 4.0 International

Subjects

cybersecurity
information extraction
natural language processing
UMBC Ebiquity Research Group

Abstract

We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieved F1 scores of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at https://bit.ly/cybr2vec.