FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

(2018) Mohammad Sabokrou et al.   COMPUTER VISION AND IMAGE UNDERSTANDING

Unsupervised real-time anomaly detection for streaming data

(2017) Subutai Ahmad et al.   NEUROCOMPUTING

Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond

(2017) Rushan Arshad et al.   IEEE Access

Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

(2017) Manuel Lopez-Martin et al.   IEEE Access

Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings

(2017) Alfonso Capozzoli et al.   Sustainable Cities and Society

Expected similarity estimation for large-scale batch and streaming anomaly detection

(2016) Markus Schneider et al.   MACHINE LEARNING

An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks

(2016) Qin Yu et al.   International Journal of Distributed Sensor Networks

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

(2016) Min-Joo Kang et al.   PLoS One

Automatic Time Series Forecasting: TheforecastPackage forR

(2015) Rob J. Hyndman et al.   Journal of Statistical Software

Anomaly detection

(2009) Varun Chandola et al.   ACM COMPUTING SURVEYS