DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection
To ensure the normal operation of the system, the enterprise’s operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid f...
Main Authors: | Yun Zhao, Xiuguo Zhang, Zijing Shang, Zhiying Cao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-11-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/24/11/1613 |
Similar Items
-
A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD
by: Yun Zhao, et al.
Published: (2021-11-01) -
LSTM-Based VAE-GAN for Time-Series Anomaly Detection
by: Zijian Niu, et al.
Published: (2020-07-01) -
Comparative Evaluation of VAEs, VAE-GANs and AAEs for Anomaly Detection in Network Intrusion Data
by: Mahmoud Mohamed
Published: (2023-12-01) -
KPI-TSAD: A Time-Series Anomaly Detector for KPI Monitoring in Cloud Applications
by: Juan Qiu, et al.
Published: (2019-11-01) -
iVAE-GAN: Identifiable VAE-GAN Models for Latent Representation Learning
by: Bjorn Uttrup Dideriksen, et al.
Published: (2022-01-01)