A regression method to compare network data and modeling data using generalized additive model

Sooyoung Chae, Hosub Lee, Jaeik Cho, Manhyun Jung, Jong In Lim, Jongsub Moon

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

Abstract

This paper suggests a method to check whether the real network dataset and modeling dataset for real network has statistically similar characteristics. The method we adopt in this paper is a Generalized Additive Model. By using this method, we show how similar the MIT/LL Dataset and the KDD CUP 99' Dataset are regarding their characteristics. It provided reasonable outcome for us to confirm that MIT/LL Dataset and KDD Cup Dataset are not statistically similar.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages190-200
Number of pages11
Volume5379 LNCS
DOIs
Publication statusPublished - 2009 Nov 9
Event9th International Workshop on Information Security Applications, WISA 2008 - Jeju Island, Korea, Republic of
Duration: 2008 Sep 232008 Sep 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5379 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Workshop on Information Security Applications, WISA 2008
CountryKorea, Republic of
CityJeju Island
Period08/9/2308/9/25

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Keywords

  • Data set evaluation
  • Network data comparing
  • Statistical data analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chae, S., Lee, H., Cho, J., Jung, M., Lim, J. I., & Moon, J. (2009). A regression method to compare network data and modeling data using generalized additive model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5379 LNCS, pp. 190-200). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5379 LNCS). https://doi.org/10.1007/978-3-642-00306-6_14