Whether it is used for predictive maintenance, intrusion detection or surveillance, on-device
anomaly detection is a very valuable functionality in sensor and Internet-of-things (IoT) …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

The Internet of Things (IoT) requires sophisticated security measures because of
heterogeneity and resource constraints. Current approaches in Anomaly Detection (AD) do …

Anomaly detection is the process of identifying unusual signals in a set of observations. This
is a vital task in a variety of fields including cybersecurity and the battlefield. In many …

The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments
that produce large amounts of sensor data. Lightweight online on-mote processing may …

In manufacturing industries, it is of fundamental importance to detect anomalies in
production in order to meet the required quality goals and to limit the number of defective …

Semi-supervised anomaly detection is an approach to identify anomalies by learning the
distribution of normal data. Backpropagation neural networks (ie, BP-NNs) based …

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging
the gap between the physical and the cyber worlds. Anomaly detection is a critical task in …

Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …

Deep neural networks (DNNs) have been showing significant success in various anomaly
detection applications such as smart surveillance and industrial quality control. It is …