Internet of Things: Digital Footprints Carry A Device Identity

TL;DR

This paper presents a device fingerprinting model that accurately identifies IoT and non-IoT devices and distinguishes individual devices using statistical features and machine learning, enhancing network security.

Contribution

The paper introduces a novel device fingerprinting approach using statistical features and Random Forest classification for IoT device identification and differentiation.

Findings

Achieves up to 99.8% accuracy in IoT vs non-IoT classification.

Over 97.6% accuracy in individual device identification.

Effective in improving network security and preventing unauthorized access.

Abstract

The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The method has been evaluated using the Random Forest (RF) classifier and different datasets. Experimental results have shown that the proposed method achieves up to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over 97.6% in classifying individual devices. These signify that the proposed method is useful in assisting operators in making their networks more secure and robust to security breaches and unauthorized access.