Data mining for security

Kenji Yamanishi, Jun Ichi Takeuchi, Yuko Maruyama

研究成果: ジャーナルへの寄稿学術誌査読

5 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)63-69
ページ数7
ジャーナルNEC Journal of Advanced Technology
2
1
出版ステータス出版済み - 12月 2005
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 工学(その他)
  • 電子工学および電気工学

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