MDL criterion for NMF with application to botnet detection

Shoma Tanaka, Yuki Kawamura, Masanori Kawakita, Noboru Murata, Junnichi Takeuchi

研究成果: Chapter in Book/Report/Conference proceedingConference 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.

本文言語英語
ホスト出版物のタイトルNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
編集者Kenji Doya, Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Derong Liu
出版社Springer Verlag
ページ570-578
ページ数9
ISBN(印刷版)9783319466866
DOI
出版ステータス出版済み - 1 1 2016
イベント23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, 日本
継続期間: 10 16 201610 21 2016

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)0302-9743
ISSN(電子版)1611-3349

その他

その他23rd International Conference on Neural Information Processing, ICONIP 2016
Country日本
CityKyoto
Period10/16/1610/21/16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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