MDL criterion for NMF with application to botnet detection

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

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
    出版社Springer Verlag
    ページ570-578
    ページ数9
    9947 LNCS
    ISBN(印刷版)9783319466866
    DOI
    出版ステータスPublished - 2016
    イベント23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
    継続期間: 2016 10 162016 10 21

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    9947 LNCS
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

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

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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