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open access
Copyright: Copyright 2019, Nour Moustafa
This dataset has been sponsored by the Australian Reserach Data Commons (ARDC). The Copyright is reserved to the Author, Dr Nour Moustafa, who is a Lecturer and Offensice secuity Theme lead at UNSW Canberra. This datasets should be publicly published and sustain their online availability.
Copyright: Copyright 2019, Nour Moustafa
This dataset has been sponsored by the Australian Reserach Data Commons (ARDC). The Copyright is reserved to the Author, Dr Nour Moustafa, who is a Lecturer and Offensice secuity Theme lead at UNSW Canberra. This datasets should be publicly published and sustain their online availability.
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Abstract
Collecting and analysing heterogeneous data sources from the Internet of Things (IoT) and Industrial IoT (IIoT) are essential for training and validating the fidelity of cybersecurity applications-based machine learning. However, the analysis of those data sources is still a big challenge for reducing high dimensional space and selecting important features and observations from different data sources. The study proposes a new testbed for an IIoT network that was utilised for creating new datasets called TON_IoT that collected Telemetry data, Operating systems data and Network data. The testbed is deployed using multiple virtual machines including hosts of windows, Linux and Kali Linux operating systems to manage the interconnections between the three layers of IIoT, Cloud and Edge/Fog systems. The initial statistical evaluation of the datasets reveals their capability for evaluating cybersecurity applications such as intrusion detection, threat intelligence, adversarial machine learning and privacy-preserving models.