Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Kai Zhang; Yushan Jiang; Lee Seversky; Chengtao Xu; Dahai Liu; Houbing Song

doi:10.1109/IPCCC51483.2021.9679367

Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Kai Zhang, Yushan Jiang, Lee Seversky, Chengtao Xu, Dahai Liu, Houbing Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665443319
DOIs	https://doi.org/10.1109/IPCCC51483.2021.9679367
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021 - Austin, United States Duration: Oct 29 2021 → Oct 31 2021

Publication series

Name	Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference
Volume	2021-October
ISSN (Print)	1097-2641

Conference

Conference	2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021
Country/Territory	United States
City	Austin
Period	10/29/21 → 10/31/21

ASJC Scopus Subject Areas

General Engineering

Keywords

Anomaly Detection
Data-efficient Machine Learning
Federate Learning
Net-work Security

Access to Document

10.1109/IPCCC51483.2021.9679367

Cite this

Zhang, K., Jiang, Y., Seversky, L., Xu, C., Liu, D., & Song, H. (2021). Federated Variational Learning for Anomaly Detection in Multivariate Time Series. In 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021 (Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference; Vol. 2021-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPCCC51483.2021.9679367

Federated Variational Learning for Anomaly Detection in Multivariate Time Series. / Zhang, Kai; Jiang, Yushan; Seversky, Lee et al.
2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference; Vol. 2021-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, K, Jiang, Y, Seversky, L, Xu, C, Liu, D & Song, H 2021, Federated Variational Learning for Anomaly Detection in Multivariate Time Series. in 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021. Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference, vol. 2021-October, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021, Austin, United States, 10/29/21. https://doi.org/10.1109/IPCCC51483.2021.9679367

Zhang K, Jiang Y, Seversky L, Xu C, Liu D, Song H. Federated Variational Learning for Anomaly Detection in Multivariate Time Series. In 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference). doi: 10.1109/IPCCC51483.2021.9679367

Zhang, Kai ; Jiang, Yushan ; Seversky, Lee et al. / Federated Variational Learning for Anomaly Detection in Multivariate Time Series. 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference).

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title = "Federated Variational Learning for Anomaly Detection in Multivariate Time Series",

keywords = "Anomaly Detection, Data-efficient Machine Learning, Federate Learning, Net-work Security",

author = "Kai Zhang and Yushan Jiang and Lee Seversky and Chengtao Xu and Dahai Liu and Houbing Song",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021 ; Conference date: 29-10-2021 Through 31-10-2021",

year = "2021",

doi = "10.1109/IPCCC51483.2021.9679367",

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T1 - Federated Variational Learning for Anomaly Detection in Multivariate Time Series

AU - Zhang, Kai

AU - Jiang, Yushan

AU - Seversky, Lee

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AU - Song, Houbing

PY - 2021

Y1 - 2021

KW - Anomaly Detection

KW - Data-efficient Machine Learning

KW - Federate Learning

KW - Net-work Security

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M3 - Conference contribution

AN - SCOPUS:85125197897

T3 - Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference

BT - 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Performance, Computing, and Communications Conference, IPCCC 2021

Y2 - 29 October 2021 through 31 October 2021

ER -

Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this