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

Abstract

Anomaly detection for 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 with classification algorithms to build an end-to-end architecture for processing, feature extraction and data classification. Autoencoder is used to extract valuable hidden features of the original data, while supervised learning algorithms such as Softmax, Random Forest, Decision Trees, XGBoost, etc. are used for training and testing on AE’s encoder output data. We then test our recommended models on nine recent devices in the NBaIoT dataset and evaluate their performance. According to the experimental results, the proposed model greatly improves the performance of IoT anomaly detection methods.