  Device identification is one way to secure a network of IoT devices, whereby
devices identified as suspicious can subsequently be isolated from a network.
We introduce a novel fingerprinting method, IoTDevID, for device identification
that uses machine learning to model the behaviour of IoT devices based on
network packets. Our method uses an enhanced combination of features from
previous work and includes an approach for dealing with unbalanced device data
via data augmentation. We further demonstrate how to enhance device
identification via a group-wise data aggregation. We provide a comparative
evaluation of our method against two recent identification methods using three
public IoT datasets which together contain data from over 100 devices. Through
our evaluation we demonstrate improved performance over previous results with
F1-scores above 99%, with considerable improvement gained from data
aggregation.
