A deep learning approach combining autoencoder with supervised classifiers for IoT anomaly detection

Anomaly detection for Internet of things (IoT) networks is a challenging issue due to the huge number of devices that connect to each other and generate huge amounts of data. In this study, we propose a model combining Autoencoder (AE) with classification algorithms to build an endto-end architecture for processing, feature extraction and data classification.