  Consumer Internet of Things (IoT) devices are increasingly common in everyday
homes, from smart speakers to security cameras. Along with their benefits come
potential privacy and security threats. To limit these threats we must
implement solutions to filter IoT traffic at the edge. To this end the
identification of the IoT device is the first natural step.
  In this paper we demonstrate a novel method of rapid IoT device
identification that uses neural networks trained on device DNS traffic that can
be captured from a DNS server on the local network. The method identifies
devices by fitting a model to the first seconds of DNS second-level-domain
traffic following their first connection. Since security and privacy threat
detection often operate at a device specific level, rapid identification allows
these strategies to be implemented immediately. Through a total of 51,000
rigorous automated experiments, we classify 30 consumer IoT devices from 27
different manufacturers with 82% and 93% accuracy for product type and device
manufacturers respectively.
