Data mining for security

Kenji Yamanishi, Junnichi Takeuchi, Yuko Maruyama

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

It becomes increasingly important to detect intrusions with unknown patterns in order to protect our business from cyber terrorism threats. This paper introduces data mining technologies designed for this purpose; SmartSifter (outlier detection engine), ChangeFinder (change-point detection engine), AccessTracer (anomalous behavior detection engine). All of them are able to learn statistical patterns of logs adaptively and to detect intrusions as statistical anomalies relative to the learned patterns. We briefly overview the principles of these engines and illustrate their applications to network intrusion detection, worm detection, and masquerader detection.

Original languageEnglish
Pages (from-to)63-69
Number of pages7
JournalNEC Journal of Advanced Technology
Volume2
Issue number1
Publication statusPublished - Dec 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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    Yamanishi, K., Takeuchi, J., & Maruyama, Y. (2005). Data mining for security. NEC Journal of Advanced Technology, 2(1), 63-69.