2017
ACL
ACL 2017
MalwareTextDB: A Database for Annotated Malware Articles
Abstract
AbstractCybersecurity risks and malware threats are becoming increasingly dangerous and common. Despite the severity of the problem, there has been few NLP efforts focused on tackling cybersecurity. In this paper, we discuss the construction of a new database for annotated malware texts. An annotation framework is introduced based on the MAEC vocabulary for defining malware characteristics, along with a database consisting of 39 annotated APT reports with a total of 6,819 sentences. We also use the database to construct models that can potentially help cybersecurity researchers in their data collection and analytics efforts.
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Topic Pioneer
— Text Classification
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Science and Machine Learning and Natural Language Processing
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Trend Setter
— Text Classification
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Keyword Pioneer
— annotation framework
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Multimodal Learning
Machine Learning > Core Methods > Classification
Natural Language Processing > Applications > Information Extraction
Natural Language Processing > Applications > Text Classification
Computer Science > Applications > Cybersecurity
Natural Language Processing > Understanding > Text Classification