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Many IoT devices are vulnerable to attacks due to flawed security designs andlacking mechanisms for firmware updates or patches to eliminate the securityvulnerabilities. Device-type identification combined with data fromvulnerability databases can pinpoint vulnerable IoT devices in a network andcan be used to constrain the communications of vulnerable devices forpreventing damage. In this contribution, we present and evaluate two deeplearning approaches to the reliable IoT device-type identification, namely arecurrent and a convolutional network architecture. Both deep learningapproaches show accuracies of 97% and 98%, respectively, and thereby outperforman up-to-date IoT device-type identification approach using hand-craftedfingerprint features obtaining an accuracy of 82%. The runtime performance forthe IoT identification of both deep learning approaches outperforms thehand-crafted approach by three magnitudes. Finally, importance metrics explainthe results of both deep learning approaches in terms of the utilization of theanalyzed traffic data flow.

Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning