-
- OTOMO Kazuki
- Graduate School of Information Science and Technology, The University of Tokyo
-
- KOBAYASHI Satoru
- National Institute of Informatics
-
- FUKUDA Kensuke
- National Institute of Informatics Department of Informatics, Sokendai
-
- ESAKI Hiroshi
- Graduate School of Information Science and Technology, The University of Tokyo
説明
<p>System logs are useful to understand the status of and detect faults in large scale networks. However, due to their diversity and volume of these logs, log analysis requires much time and effort. In this paper, we propose a log event anomaly detection method for large-scale networks without pre-processing and feature extraction. The key idea is to embed a large amount of diverse data into hidden states by using latent variables. We evaluate our method with 12 months of system logs obtained from a nation-wide academic network in Japan. Through comparisons with Kleinberg's univariate burst detection and a traditional multivariate analysis (i.e., PCA), we demonstrate that our proposed method achieves 14.5% higher recall and 3% higher precision than PCA. A case study shows detected anomalies are effective information for troubleshooting of network system faults.</p>
収録刊行物
-
- IEICE Transactions on Information and Systems
-
IEICE Transactions on Information and Systems E102.D (9), 1644-1652, 2019-09-01
一般社団法人 電子情報通信学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390845702274659328
-
- NII論文ID
- 130007699784
-
- ISSN
- 17451361
- 09168532
-
- 本文言語コード
- en
-
- 資料種別
- journal article
-
- データソース種別
-
- JaLC
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
-
- 抄録ライセンスフラグ
- 使用不可