A Bayesian LSTM Based Active Anomaly Detection Service for Large Online Systems-Reference-Cited by-同舟云学术

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A Bayesian LSTM Based Active Anomaly Detection Service for Large Online Systems

Published:2024-07-24 Issue: Volume: Page:407-416
ISSN:
Container-title:Proceedings of the 15th Asia-Pacific Symposium on Internetware
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Author:

Wang Chen¹^ORCID,Huang Tao²^ORCID,Li Min³^ORCID,Chen Pengfei⁴^ORCID,Chen Zhiwen¹^ORCID

Affiliation:

1. Institute of Computer Application, Chinese Academy of Engineering Physics, China

2. EmergingAI, China

3. School of systems science and engineering, Sun Yat-sen University, China

4. school of computer science and engineering, Sun Yat-sen University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3671016.3674818

Reference38 articles.

1. Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture

2. TSFEL: Time Series Feature Extraction Library

3. Outlier detection using isolation forest and local outlier factor

4. Sung-Bae Cho and Hyuk-Jang Park. 2003. Efficient anomaly detection by modeling privilege flows using hidden Markov model. computers & security 22, 1 (2003), 45–55.

5. Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)

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