Towards the Development of a Realistic Multidimensional IoT Profiling Dataset

The Internet of Things (IoT) is an emerging technology that enables the development of low-cost and energy-efficient IoT devices across various solutions from smart cities to healthcare domains. With such a complex and heterogeneous instance of IoT devices and their applications, numerous challenges arise in both device management and security concerns. Thus, it is essential to develop intelligent IoT identification/profiling and intrusion detection components that are tailored to IoT applications. Such systems require a realistic and multidimensional reference IoT dataset for training and evaluation. Device identification/profiling ensures the authenticity of the devices attached to the IoT network and environment which can be achieved by fingerprinting a device. Since fingerprinting is mostly examined by device network flows and device local attributes, we have proposed this study to intelligently recognize machine-to-machine communication and identify each device properly. In this paper, we analyzed the behaviour of 60 IoT devices during experiments conducted in our lab setup at the Canadian Institute for Cybersecurity (CIC). Our IoT devices include WiFi, ZigBee, and Z-Wave devices. We collected data from each device in four stages: powered on, idle, active, and interactions. Besides these stages, different scenario experiments were conducted using a microcosm of devices to simulate the network activity of a smart home. Additionally, we have generated two attack datasets, namely flood denial-of-service attack and RTSP brute-force attack. Lastly, we implement an extensive case study on the transferability of the RF classifier and train our model with the dataset from our lab, transfer the model to the dataset from a different lab and test the trained model on their dataset. This paper’s dataset materials are available on the CIC dataset page under the CIC IoT dataset 20221.