Sparse random neural networks for online anomaly detection on sensor nodes

Abstract

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) systems. In this paper, we introduce a novel anomaly detection technique based on sparse, random neural networks. The sparsity in the model allows for a very efficient implementation on embedded or resource constrained hardware. Our approach supports continuous online learning where the model is deployed to the sensor device without any prior training. As new data becomes available, the model is updated and becomes better at detecting anomalies. We experimentally validate our approach on several default benchmark data sets in the visual domain as well as on industrial quality inspection and predictive maintenance tasks. We show that our approach achieves a very favorable trade-off between computational cost and anomaly detection accuracy.