As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

Given high-dimensional time series data (eg, sensor data), how can we detect anomalous
events, such as system faults and attacks? More challengingly, how can we do this in a way …

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

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Unsupervised representation learning has proved to be a critical component of anomaly
detection/localization in images. The challenges to learn such a representation are two-fold …

Many real-world Internet of Things (IoT) systems, which include a variety of Internet-
connected sensory devices, produce substantial amounts of multivariate time-series data …

Novelty detection, ie, identifying whether a given sample is drawn from outside the training
distribution, is essential for reliable machine learning. To this end, there have been many …