@inproceedings{lim-etal-2017-malwaretextdb,
    title = "{M}alware{T}ext{DB}: A Database for Annotated Malware Articles",
    author = "Lim, Swee Kiat  and
      Muis, Aldrian Obaja  and
      Lu, Wei  and
      Ong, Chen Hui",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/P17-1143/",
    doi = "10.18653/v1/P17-1143",
    pages = "1557--1567",
    abstract = "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."
}