  We study the problem of inferring the type of a networked device in a home
network by leveraging low level traffic activity indicators seen at commodity
home gateways. We analyze a dataset of detailed device network activity
obtained from 240 subscriber homes of a large European ISP and extract a number
of traffic and spatial fingerprints for individual devices. We develop a two
level taxonomy to describe devices onto which we map individual devices using a
number of heuristics. We leverage the heuristically derived labels to train
classifiers that distinguish device classes based on the traffic and spatial
fingerprints of a device. Our results show an accuracy level up to 91% for the
coarse level category and up to 84% for the fine grained category. By
incorporating information from other sources (e.g., MAC OUI), we are able to
further improve accuracy to above 97% and 92%, respectively. Finally, we also
extract a set of simple and human-readable rules that concisely capture the
behaviour of these distinct device categories.
