Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The
diversity of the datasets is the foundation for developing comprehensive IAD algorithms …

Time series anomaly detection is widely used to monitor the equipment sates through the
data collected in the form of time series. At present, the deep learning method based on …

In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for
anomaly detection in time series data for Edge Computing in Industrial Internet of Things …

Congratulations on your decision to explore deep learning and the exciting world of
anomaly detection using deep learning. Anomaly detection is finding patterns that do not …

The emergence of IoT and AI has brought revolutionary change in various application
domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to …

Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions
with maximum accuracy and reduce false alarm rates. The feature extraction is also …

Identifying defects in the images of industrial products has been an important task to
enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly …

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 …

Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a
threat to Internet security. Intrusion detection can intelligently detect anomalous activities …

We outline an anomaly detection method for industrial control systems (ICS) that combines
the analysis of network package contents that are transacted between ICS nodes and their …