Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Nowadays, more than half of the world's web traffic comes from mobile phones, and by 2020
approximately 70 percent of the world's population will be using smartphones. The …

In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …

Numerous industrial environments and IoT systems in the real world contain a range of
sensor devices. These devices, when in operation, produce a large amount of multivariate …

We designed and evaluated an assumption-free, deep learning-based methodology for
animal health monitoring, specifically for the early detection of respiratory disease in …

Precipitation can adversely influence road safety. Slippery road conditions have traditionally
been detected using reactive methods requiring considerable excitation of the tire forces …

With the ever-growing data traffic in computer networks nowadays, the management of large-
scale networks is a challenge for guaranteeing the quality of the provided services. This is …

This paper focuses on the automated learning of driver braking “signature” in the presence
of road anomalies. Our motivation is to improve driver experience using preview information …

Time series anomaly detection is very important to ensure the security of industrial control
systems (ICSs). Many algorithms have performed well in anomaly detection. However, the …