Recent Research Solutions on Deep Learning-based Anomaly Detection in Internet of Things

The rise of Internet of Things (IoT) devices and networks has generated an overwhelming urge to determine whether it is safe for individuals from attacks or even to detect strange behavior. The ability to detect anomalies has been proven using conventional machine learning approaches, but the complexity and heterogeneity of the data from IoT sensors are not suitable for such machine learning techniques. From the standpoint of log anomaly detection, the research put the spotlight on deep learning as a technology that outperforms data mining and machine learning techniques that had previously been dominant in this field. This paper explores the application of deep learning techniques in IoT networks to detect anomalies. The objective of the paper is to focus on the problems of deep learning based on the gathered IoT anomalies and to present existing deep learning research solutions for detecting IoT anomalies.