Identifying products in online cybercrime marketplaces: A Dataset for fine-grained domain adaptation

Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca S. Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.1

    Original languageEnglish (US)
    Title of host publicationEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
    PublisherAssociation for Computational Linguistics (ACL)
    Pages2598-2607
    Number of pages10
    ISBN (Electronic)9781945626838
    StatePublished - Jan 1 2017
    Event2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017 - Copenhagen, Denmark
    Duration: Sep 9 2017Sep 11 2017

    Publication series

    NameEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings

    Conference

    Conference2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
    CountryDenmark
    CityCopenhagen
    Period9/9/179/11/17

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    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems
    • Computational Theory and Mathematics

    Cite this

    Durrett, G., Kummerfeld, J. K., Berg-Kirkpatrick, T., Portnoff, R. S., Afroz, S., McCoy, D., Levchenko, K., & Paxson, V. (2017). Identifying products in online cybercrime marketplaces: A Dataset for fine-grained domain adaptation. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 2598-2607). (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings). Association for Computational Linguistics (ACL).