Internet of Things: Digital Footprints Carry A Device Identity
This addresses network security for IoT operators by providing a method to enhance security against breaches and unauthorized access, though it appears incremental as it builds on existing fingerprinting and classification techniques.
The paper tackles device identification for network security by proposing a device fingerprinting model that distinguishes IoT from non-IoT devices and uniquely identifies individual devices, achieving up to 99.8% accuracy for IoT/non-IoT distinction and over 97.6% for individual device classification.
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.