MalwareTextDB: A Database for Annotated Malware Articles
2017-07-01ACL 2017Unverified0· sign in to hype
Swee Kiat Lim, Aldrian Obaja Muis, Wei Lu, Chen Hui Ong
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Cybersecurity 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.