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The popularity of the Internet of Things (IoT) devices makes it increasinglyimportant to be able to fingerprint them, for example in order to detect ifthere are misbehaving or even malicious IoT devices in one's network. The aimof this paper is to provide a systematic categorisation of machine learningaugmented techniques that can be used for fingerprinting IoT devices. This canserve as a baseline for comparing various IoT fingerprinting mechanisms, sothat network administrators can choose one or more mechanisms that areappropriate for monitoring and maintaining their network. We carried out anextensive literature review of existing papers on fingerprinting IoT devices --paying close attention to those with machine learning features. This isfollowed by an extraction of important and comparable features among themechanisms outlined in those papers. As a result, we came up with a key set ofterminologies that are relevant both in the fingerprinting context and in theIoT domain. This enabled us to construct a framework called IDWork, which canbe used for categorising existing IoT fingerprinting mechanisms in a way thatwill facilitate a coherent and fair comparison of these mechanisms. We foundthat the majority of the IoT fingerprinting mechanisms take a passive approach-- mainly through network sniffing -- instead of being intrusive andinteractive with the device of interest. Additionally, a significant number ofthe surveyed mechanisms employ both static and dynamic approaches, in order tobenefit from complementary features that can be more robust against certainattacks such as spoofing and replay attacks.

Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms