A Deep Learning Based Anomaly Detection Model for IoT Networks

Internet of Things (IoT) has demonstrated tremendous advantages in various industry and research fields. The IoT device number rapidly increased, followed by more serious safety hazard. These anomalies can influence the performance of system or even worse destroy the function of entire system. Anomaly detection methods are investigated to identify unusual states or malicious behaviors. This paper proposes a deep learning-based anomaly detection model to detect and classify anomalies in IoT. The proposed model is based on Residual Networks and Bi-directional GRU, which can fully utilize the spatial and temporal features of network traffic data. Moreover, attention mechanism is utilized to extract key features to improve the classification performance of the model. Experimental results show that the proposed model has better detection and classification performance.