Botnet detection based on non-negative matrix factorization and the MDL principle

Sayaka Yamauchi, Masanori Kawakita, Jun'ichi Takeuchi

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

8 被引用数 (Scopus)

抄録

We propose a method for botnet detection from darknet data by non-negative matrix factorization (NMF), which can decompose the vector valued time series data into several components. In addition, we propose a new method to estimate the number of components in the data, by the minimum description length (MDL) principle. Our method for botnet detection consists of change point detection and analysis based on variance of the decomposed data.

本文言語英語
ホスト出版物のタイトルNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
ページ400-409
ページ数10
PART 5
DOI
出版ステータス出版済み - 11 19 2012
イベント19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, カタール
継続期間: 11 12 201211 15 2012

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)
番号PART 5
7667 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他19th International Conference on Neural Information Processing, ICONIP 2012
Countryカタール
CityDoha
Period11/12/1211/15/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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引用スタイル