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. We proposed a new dataset, namely IoTID20, generated dataset from [1]. The new IoT botnet dataset has a more comprehensive network and flow-based features. The flow-based feature can be used to analyze and evaluate a flow-based intrusion detection system. 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.
Free use of the IoT Network Intrusion Dataset for academic research purposes is hereby granted in perpetuity. please cite the following paper that has the dataset’s details.
I. Ullah and Q. H. Mahmoud, "A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks." In: Goutte C., Zhu X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science, vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_52