Abstract
The rise of the Internet of Things (IoT) and Industrial IoT (IIoT), over the past few years, has been beneficial for the citizens, societies and industry. However, their resource-constrained and heterogenous nature renders them vulnerable to a wide range of threats. Therefore, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), are required to be developed before IoT/IIoT networks reach their full potential in the market. However, there is a lack of up-to-date, representative and well-structured IoT/IIoT-specific datasets that are publicly available to the research community and constitute benchmark datasets for effective training and evaluation of Machine Learning models suitable for AIDSs in IoT/IIoT networks. Contribution to filling this research gap is of utmost importance and toward this direction the novel 'TON_IoT Telemetry' dataset was recently published. Taking the opportunity to explore further this dataset, we targeted at its network-related part so as to generate IoT edge network specific datasets for effective development of more accurate and efficient IoT/IIoT-specific AIDSs. Therefore, in this paper, we present the methodology we followed to generate a set of IoT edge network specific datasets based on the 'ToN_IoT Telemetry' dataset.
Original language | English |
---|---|
Title of host publication | 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 978-1-6654-1779-2, 978-1-6654-1780-8 |
DOIs | |
Publication status | Published - 6 Dec 2021 |
Event | 26th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Porto, Portugal Duration: 25 Oct 2021 → 27 Oct 2021 |
Conference
Conference | 26th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 |
---|---|
Country/Territory | Portugal |
City | Porto |
Period | 25/10/21 → 27/10/21 |
Keywords
- anomaly-based intrusion detection
- dataset generation
- IoT cybersecurity
- record selection