  Many IoT devices are vulnerable to attacks due to flawed security designs and
lacking mechanisms for firmware updates or patches to eliminate the security
vulnerabilities. Device-type identification combined with data from
vulnerability databases can pinpoint vulnerable IoT devices in a network and
can be used to constrain the communications of vulnerable devices for
preventing damage. In this contribution, we present and evaluate two deep
learning approaches to the reliable IoT device-type identification, namely a
recurrent and a convolutional network architecture. Both deep learning
approaches show accuracies of 97% and 98%, respectively, and thereby outperform
an up-to-date IoT device-type identification approach using hand-crafted
fingerprint features obtaining an accuracy of 82%. The runtime performance for
the IoT identification of both deep learning approaches outperforms the
hand-crafted approach by three magnitudes. Finally, importance metrics explain
the results of both deep learning approaches in terms of the utilization of the
analyzed traffic data flow.
