MDL criterion for NMF with application to botnet detection

Shoma Tanaka, Yuki Kawamura, Masanori Kawakita, Noboru Murata, Jun’Ichi Takeuchi

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

    1 Citation (Scopus)

    Abstract

    A method for botnet detection from traffic data of the Internet by the Non-negative Matrix Factorization (NMF) was proposed by (Yamauchi et al. 2012). This method assumes that traffic data is composed by several types of communications, and estimates the number of types in the data by the minimum description length (MDL) criterion. However, consideration on the MDL criterion was not sufficient and validity has not been guaranteed. In this paper, we refine the MDL criterion for NMF and report results of experiments for the new MDL criterion on synthetic and real data.

    Original languageEnglish
    Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
    PublisherSpringer Verlag
    Pages570-578
    Number of pages9
    Volume9947 LNCS
    ISBN (Print)9783319466866
    DOIs
    Publication statusPublished - 2016
    Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
    Duration: 2016 Oct 162016 Oct 21

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9947 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other23rd International Conference on Neural Information Processing, ICONIP 2016
    CountryJapan
    CityKyoto
    Period16/10/1616/10/21

    Keywords

    • Botnet
    • MDL principle
    • NMF

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Tanaka, S., Kawamura, Y., Kawakita, M., Murata, N., & Takeuchi, JI. (2016). MDL criterion for NMF with application to botnet detection. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (Vol. 9947 LNCS, pp. 570-578). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9947 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46687-3_63