Anomaly detection is an essential task for different fields in the real world. The imbalanced
data and lack of labels make the task challenging. Deep learning models based on …

The continuously growing amount of monitored data in the Industry 4.0 context requires
strong and reliable anomaly detection techniques. The advancement of Digital Twin …

Quality of data services is crucial for operational large-scale internet-of-things (IoT) research
data infrastructure, in particular when serving large amounts of distributed users. Effectively …

Detecting anomalies in time series is a vital technique in a wide variety of industrial
application in which sensors monitor expensive machinery. The complexity of this task …

Deep learning is increasingly used for unsupervised feature extraction and anomaly
detection in big datasets. Most deep learning based anomaly detection techniques …

Due to the damage of sensors or transmission equipment, abnormal monitoring data
inevitably exists in the measured raw data, and it significantly impacts the condition …

Industrial Internet of Things (IoT) is the most rapidly developing industry in the current IoT
industry, and the intrusion detection system (IDS) remains one of the key technologies for …

Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

Data quality significantly impacts the results of data analytics. Researchers have proposed
machine learning based anomaly detection techniques to identify incorrect data. Existing …

Anomaly detection is a long-standing problem in system designation. High-quality anomaly
detection can benefit plenty of applications (eg system monitoring, disaster precaution and …