Multivariate Time Series Anomaly Detection with Deep Learning Models Leveraging Inter-Variable Relationships-Reference-Cited by-同舟云学术

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Multivariate Time Series Anomaly Detection with Deep Learning Models Leveraging Inter-Variable Relationships

Published:2023-05-17 Issue: Volume: Page:
ISSN:
Container-title:2023 Silicon Valley Cybersecurity Conference (SVCC)
language:
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Author:

Seong Changmin¹,Lim Dongjun²,Jang Jiho²,Lee Jonghoon³,Park Jong-Geun³,Cheong Yun-Gyung⁴

Affiliation:

1. Sungkyunkwan University,Department of Computer Science,Suwon,South Korea

2. Sungkyunkwan University,Department of Artificial Intelligence,Suwon,South Korea

3. Electronics and Telecommunications Research Institute,Daejeon,South Korea

4. Sungkyunkwan University,Social Innovation Convergence Program,Department of AI,Suwon,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10164830/10164852/10165468.pdf?arnumber=10165468

Reference24 articles.

1. Analysis of Autoencoders for Network Intrusion Detection

2. Time-Series Aware Precision and Recall for Anomaly Detection

3. Auto-encoder based dimensionality reduction

4. Towards Building Intrusion Detection Systems for Multivariate Time-Series Data

5. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

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