ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis

Yair Meidan, Michael Bohadana, Asaf Shabtai, Juan David Guarnizo, Martín Ochoa, Nils Ole Tippenhauer, Yuval Elovici

Proceedings of the symposium on applied computing, 506-509, 2017

In this work we apply machine learning algorithms on network traffic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, we collected and labeled network traffic data from nine distinct IoT devices, and PCs and smartphones. Using supervised learning, we trained a multi-stage meta classifier; in the first stage, the classifier can distinguish between traffic generated by IoT and non-IoT devices. In the second stage, each IoT device is associated a specific IoT device class. The overall IoT classification accuracy of our model is 99.281+.