An Anomaly Detection Method on Web-based System by Trend Analysis with Autoregressive Model

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2014/06/01

タイトル(英語): An Anomaly Detection Method on Web-based System by Trend Analysis with Autoregressive Model

著者名: Masaki Samejima (Graduate School of Information Science and Technology Osaka University), Hiroshi Ohno (Graduate School of Information Science and Technology Osaka University), Masanori Akiyoshi (Department of Information Systems and Management Hiroshima

著者名(英語): Masaki Samejima (Graduate School of Information Science and Technology Osaka University), Hiroshi Ohno (Graduate School of Information Science and Technology Osaka University), Masanori Akiyoshi (Department of Information Systems and Management Hiroshima Institute of Technology), Norihisa Komoda (Graduate School of Information Science and Technology Osaka University), Matsuki Yoshino (IT Platform Division Group, IT Platform R & D Management Division, Hitachi, Ltd.)

キーワード: Web-based System，Anomaly Detection，Autoregressive Model

要約(英語): The purpose of our research is to develop the continuous anomaly detection method for a web-based system with avoiding false detection by monitoring resource usage. A conventional method detects the anomaly by applying autoregressive model to the difference between the actual resource usage and the estimated resource usage with the design reference value. When the spike that uses much resource momentarily happens on the web-based system, the anomaly is detected falsely in spite of that the anomaly is not continuous and immediately recovered. In order to detect the continuous anomaly, the proposed method checks whether the detected anomaly is continuous or not by judging the resource usage after the observation of a spike. The proposed method judges the trend of increasing resource usage by autoregressive coefficient with the resource usage after the spike. Applying the test of the structural changes to the resource usage before and after the spike, the proposed method detects anomalies in judging statistically whether the trend of the resource usages changes. Experimental results show that the proposed method can decrease the frequency of the false detection to few times and detect the anomaly in 380 seconds, which is practical enough to use for the management of a web-based system.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.134 No.6 （2014） 特集：デペンダブルなサービスシステムに貢献する情報・システム技術

本誌掲載ページ: 814-820 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/134/6/134_814/_article/-char/ja/