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)


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
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
  • General Computer Science


Dive into the research topics of 'MDL criterion for NMF with application to botnet detection'. Together they form a unique fingerprint.

Cite this