Lightweight long short-term memory variational auto-encoder for multivariate time series anomaly detection in industrial control systems
Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong
impact on the physical world in recent decades. Connecting devices to the internet enables …
impact on the physical world in recent decades. Connecting devices to the internet enables …
Unsupervised anomaly detection in multivariate time series through transformer-based variational autoencoder
Modern industrial devices often use multiple sensors to detect the status of system, which
produce a large amount of multivariate time series. Due to the complex temporal …
produce a large amount of multivariate time series. Due to the complex temporal …
Anomaly detection for multivariate times series through the multi-scale convolutional recurrent variational autoencoder
T Xie, Q Xu, C Jiang - Expert Systems with Applications, 2023 - Elsevier
To realize the anomaly detection for industrial multi-sensor data, we develop a novel multi-
scale convolutional recurrent variational autoencoder (MSCRVAE) model. It is a hybrid of …
scale convolutional recurrent variational autoencoder (MSCRVAE) model. It is a hybrid of …
Light-weight federated learning-based anomaly detection for time-series data in industrial control systems
With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart
manufacturing systems are increasingly becoming challenging, causing severe damage to …
manufacturing systems are increasingly becoming challenging, causing severe damage to …
Federated variational learning for anomaly detection in multivariate time series
Anomaly detection has been a challenging task given high-dimensional multivariate time
series data generated by networked sensors and actuators in Cyber-Physical Systems …
series data generated by networked sensors and actuators in Cyber-Physical Systems …
An attention-based ConvLSTM autoencoder with dynamic thresholding for unsupervised anomaly detection in multivariate time series
As a substantial amount of multivariate time series data is being produced by the complex
systems in smart manufacturing (SM), improved anomaly detection frameworks are needed …
systems in smart manufacturing (SM), improved anomaly detection frameworks are needed …
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data
Nowadays, multivariate time series data are increasingly collected in various real world
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …
GRU-based interpretable multivariate time series anomaly detection in industrial control system
C Tang, L Xu, B Yang, Y Tang, D Zhao - Computers & Security, 2023 - Elsevier
Interpretable multivariate time series anomaly detection is an important technology to
prevent accidents and ensure the reliable operation of Industrial Control Systems. A key …
prevent accidents and ensure the reliable operation of Industrial Control Systems. A key …
Dc-vae, fine-grained anomaly detection in multivariate time-series with dilated convolutions and variational auto encoders
GG González, SM Tagliafico, AF Iie-Fing… - 2022 IEEE European …, 2022 - ieeexplore.ieee.org
Due to its unsupervised nature, anomaly detection plays a central role in cybersecurity, in
particular on the detection of unknown attacks. A major source of cybersecurity data comes …
particular on the detection of unknown attacks. A major source of cybersecurity data comes …
Robust unsupervised anomaly detection with variational autoencoder in multivariate time series data
Accurate detection of anomalies in multivariate time series data has attracted much attention
due to its importance in a wide range of applications. Since it is difficult to obtain accurately …
due to its importance in a wide range of applications. Since it is difficult to obtain accurately …
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