For the safe operation of complex equipment, it is essential to implement accurate anomaly
detection on the key parts of equipment. However, due to the extreme conditions of the …

The rapid development of the Internet of Things (IoT) accumulates a large number of
communication records, which are utilized for anomaly detection in IoT communication …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

While anomaly detection and the related concept of intrusion detection are widely studied,
detecting anomalies in new operating behavior in environments such as the Internet of …

Targeting for detecting anomalies of various sizes for complicated normal patterns, we
propose a Template-guided Hierarchical Feature Restoration method, which introduces two …

Insider threat is destructive and concealable, making addressing it a challenging task in
cybersecurity. Most existing methods transform user behavior into sequential information …

Anomaly detection is a critical issue across several academic fields and real-world
applications. Artificial neural networks have been proposed to detect anomalies from …

Attributed networks are a type of graph structured data used in many real-world scenarios.
Detecting anomalies on attributed networks has a wide spectrum of applications such as …

More recently, smart agriculture has received widespread attention, which is a deep
combination of modern agriculture and the Internet of Things (IoT) technology. To achieve …

Deep generative networks trained via maximum likelihood on a natural image dataset like
CIFAR10 often assign high likelihoods to images from datasets with different objects (eg …