Botnet detection using graphical lasso with graph density

Chansu Han, Kento Kono, Shoma Tanaka, Masanori Kawakita, Jun’Ichi Takeuchi

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

1 被引用数 (Scopus)

抄録

A botnet detection method using the graphical lasso is studied. Hamasaki et al. proposed a botnet detection method based on graphical lasso applied on darknet traffic, which captures change points of outputs of graphical lasso caused by a botnet activity. In their method, they estimate cooperative relationship of bots using graphical lasso. If the regularization coefficient of graphical lasso is appropriately tuned, it can remove false cooperative relationships to some extent. Though they represent the cooperative relationships of bots as a graph, they didn’t use its graphical properties. We propose a new method of botnet detection based on ‘graph density’, for which we introduce a new method to set the regularization coefficient automatically. The effectiveness of the proposed method is illustrated by experiments on darknet 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537-545
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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