A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks

The exponential growth of the Internet of Things (IoT) devices provides a large attack surface for intruders to launch more destructive cyber-attacks. The intruder aimed to exhaust the target IoT network resources with malicious activity. New techniques and detection algorithms required a well-designed dataset for IoT networks. Firstly, we reviewed the weaknesses of various intrusion detection datasets. Secondly, we proposed a new dataset namely IoTID20 generated dataset from [1]. Thirdly we provide a significant set of features with their corresponding weights. Finally, we propose a new detection classification methodology using the generated dataset. Our proposed IoT botnet dataset will provide a reference point to identify anomalous activity across the IoT networks. The IoT Botnet dataset can be accessed from [2]. The new IoTID20 dataset will provide a foundation for the development of new intrusion detection techniques in IoT networks.