Anomaly detection system using resource pattern learning

Yuki Ohno, Midori Sugaya, Andrej Van Der Zee, Tatsuo Nakajima

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In this paper, Anomaly Detection by Resource Monitoring (Ayaka), a novel lightweight anomaly and fault detection infrastructure, is presented for Information Appliances. Ayaka provides a general monitoring method for detecting anomalies using only resource usage information on systems independent of its domain, target application and programming languages. Ayaka modifies the kernel to detect faults and uses a completely application black-box approach based on machine learning methods. It uses the clustering method to quantize the resource usage vector data and learn the normal patterns with Hidden Markov Model. In the running phase, Ayaka finds anomalies by comparing the application resource usage with learned model. The evaluation experiment indicates that our prototype system is able to detect anomalies, such as SQL injection and buffer overrun, without significant overheads.

本文言語English
ホスト出版物のタイトルProceedings - 1st International Workshop on Software Technologies for Future Dependable Distributed Systems, STFSSD 2009
ページ38-42
ページ数5
DOI
出版ステータスPublished - 2009 12 1
イベント1st International Workshop on Software Technologies for Future Dependable Distributed Systems, STFSSD 2009 - Tokyo, Japan
継続期間: 2009 3 172009 3 18

出版物シリーズ

名前Proceedings - 1st International Workshop on Software Technologies for Future Dependable Distributed Systems, STFSSD 2009

Conference

Conference1st International Workshop on Software Technologies for Future Dependable Distributed Systems, STFSSD 2009
国/地域Japan
CityTokyo
Period09/3/1709/3/18

ASJC Scopus subject areas

  • ハードウェアとアーキテクチャ
  • 情報システム

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