Cited By
View all- Lupton SWashizaki HYoshioka NFukazawa Y(2024)Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection MethodsIEEE Access10.1109/ACCESS.2024.338728712(78193-78218)Online publication date: 2024
DeCorus-NSA: detection and correlation of unusual signals for network syslog analytics
Authors: 
David Ohana, Bruno Wassermann, Moshik Hershcovitch, Elliot K. Kolodner, Michal Malka, Eran Raichstein, Ronen Schaffer, Robert ShahlaAuthors Info & Claims
Article No.: 21, Page 1
Abstract
The management of large data centre (DC) network infrastructure confronts Network Reliability Engineers (NRE) with challenges. A single DC at a modern cloud services provider can host thousands of network devices. The syslog messages generated by these devices are an important type of monitoring data to detect and diagnose failures. Devices in a single DC produce millions of syslog messages per day in a variety of formats.
Reference
[1]
Pinjia He, Jieming Zhu, Zibin Zheng, and Michael Lyu. 2017. Drain: An Online Log Parsing Approach with Fixed Depth Tree. 33--40.
Index Terms
- DeCorus-NSA: detection and correlation of unusual signals for network syslog analytics
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Information
Published In
June 2021
226 pages
ISBN:9781450383981
DOI:10.1145/3456727
- General Chairs:
- Bruno Wassermann,
- Michal Malka,
- Program Chairs:
- Vijay Chidambaram,
- Danny Raz
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- Technion: Israel Institute of Technology
- USENIX Assoc: USENIX Assoc
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 June 2021
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- Research-article
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SYSTOR '21
Sponsor:
SYSTOR '21: The 14th ACM International Systems and Storage Conference
June 14 - 16, 2021
Haifa, Israel
Acceptance Rates
SYSTOR '21 Paper Acceptance Rate 18 of 63 submissions, 29%;
Overall Acceptance Rate 108 of 323 submissions, 33%
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Cited By
View all- Lupton SWashizaki HYoshioka NFukazawa Y(2024)Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection MethodsIEEE Access10.1109/ACCESS.2024.338728712(78193-78218)Online publication date: 2024
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