Botnet detection using graphical lasso with graph density

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

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

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsKenji Doya, Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Derong Liu
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319466866
Publication statusPublished - 2016
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: Oct 16 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9947 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other23rd International Conference on Neural Information Processing, ICONIP 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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