2023 article
Systems and Methods for Authenticating Manufacturing Machines Through an Unobservable Fingerprinting System
Koprov, P., Gadhwala, S., Walimbe, A., Fang, X., & Starly, B. (2023, August). MANUFACTURING LETTERS, Vol. 35, pp. 1009–1018.
author keywords: Cybersecurity; Connected Manufacturing; Authentication; Physical Unclonable Function; Digital Twin; Vibration
open access via www.lib.ncsu.edu (repository)
Source: Web Of Science
Added: February 12, 2024
Digital transformation leads to the inevitable change in the security paradigm for machines on a factory production floor. A unified namespace for machines in an Industrial Internet of Things (IIoT) network is only reliable when machine assets can trust and verify the identity of assets connected to the IIoT system. Current methods of asset authentication do not consider physical unclonable functions (PUFs) and can easily be spoofed or misused. Our work proposes using PUFs for industrial equipment such as CNC machines, robots, and 3D printers for identifying machines on a network and providing authentication procedures. In this work, we chose to use the vibration associated with machines and its embedded moving parts as a means to identify machine assets on a network. It is hypothesized that the vibrations associated with specific machine movements will be unique to each machine even when machines look exactly the same. The moving parts within a machine may produce a unique vibration pattern that can be used for machine identification throughout the working cycle. Our method requires light computing and relatively cheap measuring devices to capture the ‘fingerprints’ of machines and verify the signal's integrity. An adequate number of equipment has been tested for the worst-case scenario, i.e. when two machines look exactly the same with the same moving parts and produce exactly similar motion to generate the vibration signal. Data preprocessing and standard machine learning techniques like RF, LASSO, and SVM show great performance on raw time series data, enabling 100% TPR and more than 94% TNR in detecting the false class of the machines.