DOAJ Open Access 2023

A comparative evaluation of intrusion detection systems on the edge-IIoT-2022 dataset

Taraf Al Nuaimi Salama Al Zaabi Mansor Alyilieli Mohd AlMaskari Salim Alblooshi +3 lainnya

Abstrak

We propose and evaluate a data-driven intrusion detection system (IDS) for the Internet of Things (IoT) and Industrial IoT (IIoT) environments using the Edge-IIoT-2022 dataset. We model the IDS problem as a classification problem and learn the classifier via supervised learning algorithms. Our main contribution is an empirical analysis and evaluation of the Edge-IIoT-2022 dataset, which is a recent dataset compiled for developing IDSs in IoT and IIoT environments. We develop several IDSs from standard data analytics algorithms and evaluate their performance on Edge-IIoT-2022. We compare our IDSs with prior arts and demonstrate that highly accurate binary-class IDSs can be built via Edge-IIoT-2022, whereas multi-class IDSs would require careful treatment.

Topik & Kata Kunci

Cybernetics Electronic computers. Computer science

Penulis (8)

Taraf Al Nuaimi

Salama Al Zaabi

Mansor Alyilieli

Mohd AlMaskari

Salim Alblooshi

Fahad Alhabsi

Mohd Faizal Bin Yusof

Ahmad Al Badawi

Format Sitasi

APA MLA BibTeX

Nuaimi, T.A., Zaabi, S.A., Alyilieli, M., AlMaskari, M., Alblooshi, S., Alhabsi, F. et al. (2023). A comparative evaluation of intrusion detection systems on the edge-IIoT-2022 dataset. https://doi.org/10.1016/j.iswa.2023.200298

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.1016/j.iswa.2023.200298
Akses: Open Access ✓