codymlewis/TON_IoT_network

TON IoT Network

The TON IoT train test network dataset provided by https://research.unsw.edu.au/projects/toniot-datasets

Dataset Details

The datasets have been called 'ToN_IoT' as they include heterogeneous data sources collected from Telemetry datasets of IoT and IIoT sensors, Operating systems datasets of Windows 7 and 10 as well as Ubuntu 14 and 18 TLS and Network traffic datasets. The datasets were collected from a realistic and large-scale network designed at the Cyber Range and IoT Labs, the School of Engineering and Information technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). A new testbed network was created for the industry 4.0 network that includes IoT and IIoT networks. The testbed was deployed using multiple virtual machines and hosts of windows, Linux and Kali operating systems to manage the interconnection between the three layers of IoT, Cloud and Edge/Fog systems. Various attacking techniques, such as DoS, DDoS and ransomware, against web applications, IoT gateways and computer systems across the IoT/IIoT network. The datasets were gathered in a parallel processing to collect several normal and cyber-attack events from network traffic, Windows audit traces, Linux audit traces, and telemetry data of IoT services.

Disclaimer

Free use of the TON_IoT datasets for academic research purposes is granted in perpetuity. Use for commercial purposes is allowable after asking the dataset's author, Dr Nour Moustafa, who has asserted his right under the Copyright. The datasets was sponsored by the grants from the Australian Research Data Commons, https://ardc.edu.au/news/data-and-services-discovery-activities-successful-applicants/, and UNSW Canberra. To whom intend the use of the TON_IoT datasets have to cite the below eight papers.

Dataset Sources

Repository: https://research.unsw.edu.au/projects/toniot-datasets

Papers:

1. Moustafa, Nour. "A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets." Sustainable Cities and Society (2021): 102994. Public Access Here.
2. Booij, Tim M., Irina Chiscop, Erik Meeuwissen, Nour Moustafa, and Frank TH den Hartog. "ToN IoT-The role of heterogeneity and the need for standardization of features and attack types in IoT network intrusion datasets." IEEE Internet of Things Journal (2021). Public Access Here.
3. Alsaedi, Abdullah, Nour Moustafa, Zahir Tari, Abdun Mahmood, and Adnan Anwar. "TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven Intrusion Detection Systems." IEEE Access 8 (2020): 165130-165150.
4. Moustafa, Nour, M. Keshk, E. Debie and H. Janicke, "Federated TON_IoT Windows Datasets for Evaluating AI-Based Security Applications," 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020, pp. 848-855, doi: 10.1109/TrustCom50675.2020.00114. Public Access Here.
5. Moustafa, Nour, M. Ahmed and S. Ahmed, "Data Analytics-Enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasets," 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020, pp. 727-735, doi: 10.1109/TrustCom50675.2020.00100. Public Access Here.
6. Moustafa, Nour. "New Generations of Internet of Things Datasets for Cybersecurity Applications based Machine Learning: TON_IoT Datasets." Proceedings of the eResearch Australasia Conference, Brisbane, Australia. 2019.
7. Moustafa, Nour. "A systemic IoT-Fog-Cloud architecture for big-data analytics and cyber security systems: a review of fog computing." arXiv preprint arXiv:1906.01055 (2019).
8. Ashraf, Javed, Marwa Keshk, Nour Moustafa, Mohamed Abdel-Basset, Hasnat Khurshid, Asim D. Bakhshi, and Reham R. Mostafa. "IoTBoT-IDS: A Novel Statistical Learning-enabled Botnet Detection Framework for Protecting Networks of Smart Cities." Sustainable Cities and Society (2021): 103041.