Intrusion Detection in Industrial IoT
Omar Cheikhrouhou, Ouissem Ben Fredj, Nesrine Atitallah, Salem Hellal · 2022
The Industrial Internet of Things (IIo$T$) is rapidly growing in tandem with security concerns. In this paper, we propose two deep learning models for classifying IIo$T$traffic in binary and multi-class contexts in order to detect intrusions in IIoT networks. To train the models, a recent public dataset is used. The results are very encouraging, with accuracy more than 99%.