Machine Learning Methods for Device Identification Using Wireless Fingerprinting

TL;DR

This paper systematically evaluates various machine learning algorithms for identifying IoT devices through wireless fingerprinting, enhancing cybersecurity in industrial IoT systems by leveraging static device-channel characteristics.

Contribution

It provides a comprehensive end-to-end analysis and deployment of machine learning methods for device identification using wireless fingerprints in industrial IoT environments.

Findings

Multiple machine learning algorithms achieve high accuracy in device identification.

Wireless fingerprinting can effectively replace traditional identifiers for IoT security.

The approach is currently deployed in a real-world industrial setting.

Abstract

Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards. In a common industrial scenario, indoor wireless IoT devices communicate with access points to deliver data collected from industrial sensors, robots and factory machines. Due to static or quasi-static locations of IoT devices and access points, historical observations of IoT device channel conditions provide a possibility to precisely identify the device without observing its traditional identifiers (e.g., MAC or IP address). Such device identification methods based on wireless fingerprinting gained increased attention lately as an additional cyber-security mechanism for critical IoT infrastructures. In this paper, we perform a systematic study of a large class of machine learning algorithms for device identification using wireless fingerprints for the most popular cellular and Wi-Fi IoT technologies. We design, implement, deploy, collect relevant data sets, train and test a multitude of machine learning algorithms, as a part of the complete end-to-end solution design for device identification via wireless fingerprinting. The proposed solution is currently being deployed in a real-world industrial IoT environment as part of H2020 project COLLABS.