| Autor: |
Zachos, Georgios, Essop, Ismael, Mantas, Georgios, Kyriakos, Porfyrakis, Jose, Ribeiro, Jonathan, Rodriguez |
| Přispěvatelé: |
Conference Committee, IEEE |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). |
| ISSN: |
2378-4865 |
| DOI: |
10.1109/camad52502.2021.9617799 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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