Abstract
Internet of Things (IoT) is a promising technology enabling physical devices like cameras, home appliances, and other devices to communicate and interoperate with each other. The next wave transforms our homes, society, enterprises, and cities with the massive presence of IoT devices. The devices in the Internet of Things (IoT) may exchange sensitive data, and an important issue for any organization is to get the data secured and protected. The preliminary requirement for this is a mechanism detecting and reporting anomalies automatically to some central controller. Therefore, this mechanism should be able to classify legit IoT devices from unauthorized ones. Malicious IoT devices, non-IoT devices, and other types of man-in-the-middle traffic sources must be quarantined for noncompliance. This helps formulate administrative policies and regulate/police traffic in the network for better QoS management. This work proposed a framework-based hierarchical deep neural network (HDNNs) to distinguish IoT devices from non-IoT devices using a feature set of IoT-specific traffic. A system has been designed based on HDNN that classifies IoT devices to their specific categories and identifies new entrants with reasonable accuracy. The results show that HDNN can distinguish IoT and non-IoT devices with higher accuracy and as well as classify IoT devices into the respective classes with the required accuracy.