File(s) stored somewhere else
Please note: Linked content is NOT stored on La Trobe and we can't guarantee its availability, quality, security or accept any liability.
TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven Intrusion Detection Systems
journal contributionposted on 2020-11-15, 20:29 authored by Abdullah Alsaedi, Nour Moustafa, Zahir Tari, Abdun MahmoodAbdun Mahmood, Adnan Anwar
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.
This work was partly supported by the Research grants of the Australian Research Data Commons (ARDC) RG192500 and UNSW Canberra PS51776.
- School of Engineering and Mathematical Sciences
Pagination21p. (p. 165130-165150)
Rights StatementThe Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.
TechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringIntrusion detectionTelemetrySensorsInternet of ThingsMachine learningAustraliaInternet of Things (IoT)Industrial Internet of Things (IIoT)cybersecurityintrusion detection systems (IDSs)datasetINDUSTRIAL INTERNETATTACK DETECTIONSECURITYTHINGSRANSOMWAREANALYTICSTHREAT