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, Fahad Alhabsi, Mohd Faizal Yusof, Ahmad Al Badawi
Abstract
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.