As a core tool, anomaly detection based on a generative adversarial network (GAN) is
showing its powerful potential in protecting the safe and stable operation of industrial control …

In this paper, we propose an unsupervised anomaly detection method based on the
Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative …

In the current industry world, the industrial robot has emerged as a critical device to make
the manufacturing process more efficient through automation. However, abnormal operation …

Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of
machinery and equipment in industrial environments. With the wide deployment of …

Anomaly detection has been of practical interest to many fields of engineering. Generative
Adversarial Networks (GAN), which have excellent performance in modelling the complex …

Deep Learning is emerging as an effective technique to detect sophisticated cyber-attacks
targeting Industrial Control Systems (ICSs). The conventional detection approach is to learn …

Anomaly detection problems in industrial control systems (ICSs) are always tackled by a
network traffic monitoring scheme. However, traffic-based anomaly detection systems may …

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to
build smart industry. Especially, explosive time-series data pose enormous challenges to the …

Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things
(IIoT) due to the increase in cyber attack dangers for distributed devices and critical …

In order to introduce deep learning technologies into anomaly detection, generative
adversarial networks (GANs) are considered as important roles in the algorithm design and …