TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is cruci...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9189760/ |
| _version_ | 1818663289670860800 |
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| author | Abdullah Alsaedi Nour Moustafa Zahir Tari Abdun Mahmood Adnan Anwar |
| author_facet | Abdullah Alsaedi Nour Moustafa Zahir Tari Abdun Mahmood Adnan Anwar |
| author_sort | Abdullah Alsaedi |
| collection | DOAJ |
| description | Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such IDSs require an updated and representative IoT dataset for training and evaluation. However, there is a lack of benchmark IoT and IIoT datasets for assessing IDSs-enabled IoT systems. This paper addresses this issue and proposes a new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/IIoT applications for multi-classification problems. The proposed dataset, which is named TON_IoT, includes Telemetry data of IoT/IIoT services, as well as Operating Systems logs and Network traffic of IoT network, collected from a realistic representation of a medium-scale network at the Cyber Range and IoT Labs at the UNSW Canberra (Australia). This paper also describes the proposed dataset of the Telemetry data of IoT/IIoT services and their characteristics. TON_IoT has various advantages that are currently lacking in the state-of-the-art datasets: i) it has various normal and attack events for different IoT/IIoT services, and ii) it includes heterogeneous data sources. We evaluated the performance of several popular Machine Learning (ML) methods and a Deep Learning model in both binary and multi-class classification problems for intrusion detection purposes using the proposed Telemetry dataset. |
| first_indexed | 2024-12-17T05:14:29Z |
| format | Article |
| id | doaj.art-478764ecbd9b4168a278f4c7154ce88c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:14:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-478764ecbd9b4168a278f4c7154ce88c2022-12-21T22:02:10ZengIEEEIEEE Access2169-35362020-01-01816513016515010.1109/ACCESS.2020.30228629189760TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection SystemsAbdullah Alsaedi0https://orcid.org/0000-0003-4588-5562Nour Moustafa1https://orcid.org/0000-0001-6127-9349Zahir Tari2https://orcid.org/0000-0002-1235-9673Abdun Mahmood3https://orcid.org/0000-0001-7769-3384Adnan Anwar4School of Science, RMIT University, Melbourne, VIC, AustraliaSchool of Engineering and Information Technology, University of New South Wales at ADFA, Campbell, ACT, AustraliaSchool of Science, RMIT University, Melbourne, VIC, AustraliaSchool of Computer Science and Information Technology, La Trobe University, Bundoora, VIC, AustraliaSchool of Information Technology, Centre for Cyber Security Research and Innovation (CSRI), Deakin University, Geelong, VIC, AustraliaAlthough the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such IDSs require an updated and representative IoT dataset for training and evaluation. However, there is a lack of benchmark IoT and IIoT datasets for assessing IDSs-enabled IoT systems. This paper addresses this issue and proposes a new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/IIoT applications for multi-classification problems. The proposed dataset, which is named TON_IoT, includes Telemetry data of IoT/IIoT services, as well as Operating Systems logs and Network traffic of IoT network, collected from a realistic representation of a medium-scale network at the Cyber Range and IoT Labs at the UNSW Canberra (Australia). This paper also describes the proposed dataset of the Telemetry data of IoT/IIoT services and their characteristics. TON_IoT has various advantages that are currently lacking in the state-of-the-art datasets: i) it has various normal and attack events for different IoT/IIoT services, and ii) it includes heterogeneous data sources. We evaluated the performance of several popular Machine Learning (ML) methods and a Deep Learning model in both binary and multi-class classification problems for intrusion detection purposes using the proposed Telemetry dataset.https://ieeexplore.ieee.org/document/9189760/Internet of Things (IoT)Industrial Internet of Things (IIoT)cybersecurityintrusion detection systems (IDSs)dataset |
| spellingShingle | Abdullah Alsaedi Nour Moustafa Zahir Tari Abdun Mahmood Adnan Anwar TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems IEEE Access Internet of Things (IoT) Industrial Internet of Things (IIoT) cybersecurity intrusion detection systems (IDSs) dataset |
| title | TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems |
| title_full | TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems |
| title_fullStr | TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems |
| title_full_unstemmed | TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems |
| title_short | TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems |
| title_sort | ton x005f iot telemetry dataset a new generation dataset of iot and iiot for data driven intrusion detection systems |
| topic | Internet of Things (IoT) Industrial Internet of Things (IIoT) cybersecurity intrusion detection systems (IDSs) dataset |
| url | https://ieeexplore.ieee.org/document/9189760/ |
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