MDL criterion for NMF with application to botnet detection

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

doi:10.1007/978-3-319-46687-3_63

MDL criterion for NMF with application to botnet detection

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
Publisher	Springer Verlag
Pages	570-578
Number of pages	9
Volume	9947 LNCS
ISBN (Print)	9783319466866
DOIs	https://doi.org/10.1007/978-3-319-46687-3_63
Publication status	Published - 2016
Event	23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan Duration: 2016 Oct 16 → 2016 Oct 21

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9947 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	23rd International Conference on Neural Information Processing, ICONIP 2016
Country/Territory	Japan
City	Kyoto
Period	16/10/16 → 16/10/21

Keywords

Botnet
MDL principle
NMF

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-46687-3_63

Cite this

Tanaka, S., Kawamura, Y., Kawakita, M., Murata, N., & Takeuchi, JI. (2016). MDL criterion for NMF with application to botnet detection. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (Vol. 9947 LNCS, pp. 570-578). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9947 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46687-3_63

MDL criterion for NMF with application to botnet detection. / Tanaka, Shoma; Kawamura, Yuki; Kawakita, Masanori et al.
Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Vol. 9947 LNCS Springer Verlag, 2016. p. 570-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9947 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tanaka, S, Kawamura, Y, Kawakita, M, Murata, N & Takeuchi, JI 2016, MDL criterion for NMF with application to botnet detection. in Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. vol. 9947 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9947 LNCS, Springer Verlag, pp. 570-578, 23rd International Conference on Neural Information Processing, ICONIP 2016, Kyoto, Japan, 16/10/16. https://doi.org/10.1007/978-3-319-46687-3_63

Tanaka S, Kawamura Y, Kawakita M, Murata N, Takeuchi JI. MDL criterion for NMF with application to botnet detection. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Vol. 9947 LNCS. Springer Verlag. 2016. p. 570-578. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46687-3_63

Tanaka, Shoma ; Kawamura, Yuki ; Kawakita, Masanori et al. / MDL criterion for NMF with application to botnet detection. Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Vol. 9947 LNCS Springer Verlag, 2016. pp. 570-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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AB - 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.

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MDL criterion for NMF with application to botnet detection

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