Data mining for security

Kenji Yamanishi, Jun Ichi Takeuchi, Yuko Maruyama

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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
Issue number1
Publication statusPublished - Dec 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering


Dive into the research topics of 'Data mining for security'. Together they form a unique fingerprint.

Cite this