Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms
This provides a baseline framework for network administrators to compare and select IoT fingerprinting mechanisms, addressing the need for monitoring and maintaining networks against misbehaving or malicious devices, but it is incremental as it builds on existing literature without introducing new methods.
The paper tackles the problem of systematically categorizing machine learning-augmented IoT device fingerprinting mechanisms by conducting a literature review and extracting comparable features, resulting in the IDWork framework that facilitates coherent comparisons and reveals that most mechanisms use passive network sniffing and combine static and dynamic approaches for robustness.
The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one's network. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices -- paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach -- mainly through network sniffing -- instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks.