@inproceedings{sikdar-etal-2018-flytxt,
    title = {{F}lytxt{\_}{NTNU} at {S}em{E}val-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Na{\"i}ve {B}ayes Classifiers},
    author = {Sikdar, Utpal Kumar  and
      Barik, Biswanath  and
      Gamb{\"a}ck, Bj{\"o}rn},
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/S18-1144/",
    doi = "10.18653/v1/S18-1144",
    pages = "890--893",
    abstract = "Cybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware sentences given a document (subTask1 of SemEval 2018, Task 8), as well as to classifying malware tokens in the sentences (subTask2). The approach achieved good results, ranking second of twelve participants for both subtasks, with F-scores of 57{\%} for subTask1 and 28{\%} for subTask2."
}