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

Sayaka Yamauchi, Masanori Kawakita, Jun'ichi Takeuchi

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

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages400-409
Number of pages10
EditionPART 5
DOIs
Publication statusPublished - Nov 19 2012
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: Nov 12 2012Nov 15 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume7667 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Neural Information Processing, ICONIP 2012
CountryQatar
CityDoha
Period11/12/1211/15/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Botnet detection based on non-negative matrix factorization and the MDL principle'. Together they form a unique fingerprint.

Cite this