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

TL;DR

This paper introduces IDWork, a systematic framework for categorizing IoT device fingerprinting mechanisms, based on extensive literature review and feature extraction, to aid network security and device identification.

Contribution

It provides the first comprehensive categorization framework for IoT fingerprinting techniques, integrating machine learning features and establishing a baseline for comparison.

Findings

Most IoT fingerprinting methods are passive, network-based approaches.

Many mechanisms combine static and dynamic features for robustness.

Passive methods dominate over intrusive techniques.

Abstract

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.