In collaboration with SUTD + CSRC

ProfilIoT: A Machine Learning Approach for IoT Device Identification Based on Network Traffic Analysis

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

In Proceedings of The 32nd ACM Symposium On Applied Computing (SAC'17), Marrakesh, Morocco, April 3-7 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 classi-
fier, 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%.