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We study the problem of inferring the type of a networked device in a homenetwork by leveraging low level traffic activity indicators seen at commodityhome gateways. We analyze a dataset of detailed device network activityobtained from 240 subscriber homes of a large European ISP and extract a numberof traffic and spatial fingerprints for individual devices. We develop a twolevel taxonomy to describe devices onto which we map individual devices using anumber of heuristics. We leverage the heuristically derived labels to trainclassifiers that distinguish device classes based on the traffic and spatialfingerprints of a device. Our results show an accuracy level up to 91% for thecoarse level category and up to 84% for the fine grained category. Byincorporating information from other sources (e.g., MAC OUI), we are able tofurther improve accuracy to above 97% and 92%, respectively. Finally, we alsoextract a set of simple and human-readable rules that concisely capture thebehaviour of these distinct device categories.

Inferring Networked Device Categories from Low-Level Activity Indicators