Toward a lightweight machine learning based solution against cyber-intrusions for IoT

Internet of Things starts to integrate deeply our daily life through the comfort and the services that it offers. This technology made us already more intertwined with the external environment via deployed communicating devices. However, even though this proximity offers many advantages, it presents also strong security issues. The cyber-attack surface is significantly increased and the intruder impact is becoming more compromising. In this context, we present the preliminary results of our study toward the conception of a machine learning based solution against cyber-intrusions. The main purpose is to develop a resource-preserving solution with high precision of detection. We were interested in the IoTID20 dataset over which we experimented the most accurate machine learning models. The employed pre-processing approach that we propose pushes the machine learning models to the peak of their performances. Our study provides a comprehensive view of models efficiency with respect to detection rate, size and delay.