  Device identification is one way to secure a network of IoT devices, whereby
devices identified as suspicious can subsequently be isolated from a network.
In this study, we present a machine learning-based method, IoTDevID, that
recognizes devices through characteristics of their network packets. As a
result of using a rigorous feature analysis and selection process, our study
offers a generalizable and realistic approach to modelling device behavior,
achieving high predictive accuracy across two public datasets. The model's
underlying feature set is shown to be more predictive than existing feature
sets used for device identification, and is shown to generalize to data unseen
during the feature selection process. Unlike most existing approaches to IoT
device identification, IoTDevID is able to detect devices using non-IP and
low-energy protocols.
