Anomaly detection generally involves the extraction of features from entities' or users'
properties, and the design of anomaly detection models using machine learning or deep …

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

The increasing number of Internet of Things (IoT) devices and low-cost sensors have
facilitated developments in large-scale monitoring applications. However, the accuracy of …

While emerging technologies such as the Internet of Things (IoT) have many benefits, they
also pose considerable security challenges that require innovative solutions, including those …

When the equipment is working, real-time collection of environmental sensor data for
anomaly detection is one of the key links to prevent industrial process accidents and network …

In the 5G and Internet of Things (IoT) era, the threat of network intrusions has greatly affected
people's work and life. The increasing complexity of intelligent devices in IoT brings huge …

We are now witnessing the rapid growth of advanced technologies and their application,
leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main …

Real-time anomaly detection is essential for the safe launch of some sophisticated
equipment, such as liquid rocket engines (LRE), in order to head off disasters. However, the …

Federated Learning (FL)-based anomaly detection is a promising framework for Internet of
Things (IoT) security. Due to the scarcity of abnormal data, unsupervised deep learning …

The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …