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Online monitoring automation using anomaly detection in IoT/IT environment
- Authors
- Kim, Chul; Joe, Inwhee; Jang, Deokwon; Kim, Eunji; Nam, Sanghun
- Issue Date
- Apr-2019
- Publisher
- Springer Verlag
- Keywords
- Anomaly detection; IoT; IT; Machine learning; Monitoring automation; Time-series
- Citation
- Advances in Intelligent Systems and Computing, v.985, pp.96 - 106
- Indexed
- SCOPUS
- Journal Title
- Advances in Intelligent Systems and Computing
- Volume
- 985
- Start Page
- 96
- End Page
- 106
- ISSN
- 2194-5357
- Abstract
- The increase of the IoT and the cloud environment have played a significant role of making our society knowledgeable and informative. Due to this trends the system environment gets more sophisticated and requires more system resources. In this paper, the monitoring automation without humans being involved has been proposed. It is noted that the 93.75% faults has been detected via the simulation using the proposed technique and the faults that the operators reported have been detected as well in datacenter.
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Related Researcher
- Joe, Inwhee
- COLLEGE OF ENGINEERING (SCHOOL OF COMPUTER SCIENCE)