Device identification using optimized digital footprints

TL;DR

This paper presents a device fingerprinting method using optimized digital footprints and machine learning to accurately identify devices in complex networks, enhancing security and robustness.

Contribution

The paper introduces a novel device fingerprinting approach based on selected network features and machine learning, achieving high accuracy in device identification.

Findings

Achieved up to 100% precision in device type classification.

Achieved up to 95.7% precision in individual device classification.

Validated on multiple datasets with different ML algorithms.

Abstract

The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.