ProfilIoT

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+.