Application of Machine Learning for Channel based Message Authentication in Mission Critical Machine Type Communication

TL;DR

This paper explores using machine learning to enhance message authentication in mission-critical wireless communications by leveraging physical layer security, focusing on channel characteristics to detect attacks efficiently.

Contribution

It introduces a machine learning approach for PHYSEC-based message authentication and compares its performance with existing methods in industrial and mobility scenarios.

Findings

Machine learning improves attack detection accuracy.

Channel-based features enable resource-efficient authentication.

The approach outperforms traditional cryptography in certain contexts.

Abstract

The design of robust wireless communication systems for industrial applications such as closed loop control processes has been considered manifold recently. Additionally, the ongoing advances in the area of connected mobility have similar or even higher requirements regarding system reliability and availability. Beside unfulfilled reliability requirements, the availability of a system can further be reduced, if it is under attack in the sense of violation of information security goals such as data authenticity or integrity. In order to guarantee the safe operation of an application, a system has at least to be able to detect these attacks. Though there are numerous techniques in the sense of conventional cryptography in order to achieve that goal, these are not always suited for the requirements of the applications mentioned due to resource inefficiency. In the present work, we show how the goal of message authenticity based on physical layer security (PHYSEC) can be achieved. The main idea for such techniques is to exploit user specific characteristics of the wireless channel, especially in spatial domain. Additionally, we show the performance of our machine learning based approach and compare it with other existing approaches.