Previous studies have adopted unsupervised machine learning with dimension reduction
functions for cyberattack detection, which are limited to performing robust anomaly detection …

Abnormal event detection in video is a complex computer vision problem that has attracted
significant attention in recent years. The complexity of the task arises from the commonly …

Technology improvements have made it easier than ever to collect diverse telemetry at high
resolution from any cyber or physical system, for both monitoring and control. In the domain …

Anomaly detection is a long-standing challenge in manufacturing systems, aiming to locate
surface defects and improve product quality. Traditionally, anomaly detection has relied on …

Transfer learning is an important approach that produces pre-trained teacher models which
can be used to quickly build specialized student models. However, recent research on …

Generative models trained in an unsupervised manner may set high likelihood and low
reconstruction loss to Out-of-Distribution (OoD) samples. This increases Type II errors and …

Deep learning methods work well in machine diagnostics where operating conditions affect
diagnostic signals. Classifiers are often used for fault identification, but these methods …

Generative models trained in an unsupervised manner may set high likelihood and low
reconstruction loss to Out-of-Distribution (OoD) samples. This leads to failures to detect …

Anomaly detection is an important problem in many application areas, such as network
security. Many deep learning methods for unsupervised anomaly detection produce good …

The increasing volume of traffic (especially from IoT devices) is posing a challenge to the
current anomaly detection systems. Existing systems are forced to take the support of the …