Physical Layer Authentication for Mission Critical Machine Type Communication using Gaussian Mixture Model based Clustering

TL;DR

This paper proposes a novel physical layer authentication method for mission critical machine type communication using Gaussian Mixture Model clustering of channel estimates, enhancing security against cyber attacks in wireless systems.

Contribution

It introduces a new clustering-based physical layer security approach for MC-MTC systems, validated through experimental proof-of-concept and comparison with existing methods.

Findings

Effective clustering of channel estimates improves authentication accuracy

The GMM-based method outperforms mean square error detection in experiments

Enhanced security against cyber attacks in wireless industrial applications

Abstract

The application of Mission Critical Machine Type Communication (MC-MTC) in wireless systems is currently a hot research topic. Wireless systems are considered to provide numerous advantages over wired systems in e.g. industrial applications such as closed loop control. However, due to the broadcast nature of the wireless channel, such systems are prone to a wide range of cyber attacks. These range from passive eavesdropping attacks to active attacks like data manipulation or masquerade attacks. Therefore it is necessary to provide reliable and efficient security mechanisms. Some of the most important security issues in such a system are to ensure integrity as well as authenticity of exchanged messages over the air between communicating devices. In the present work, an approach on how to achieve this goal in MC-MTC systems based on Physical Layer Security (PHYSEC) is presented. A new method that clusters channel estimates of different transmitters based on a Gaussian Mixture Model is applied for that purpose. Further, an experimental proof-of-concept evaluation is given and we compare the performance of our approach with a mean square error based detection method.