ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks

TL;DR

This paper demonstrates that machine learning models like DCNN and RF can effectively detect eavesdroppers in B5G IIoT networks with near-perfect accuracy, enhancing physical layer security.

Contribution

It introduces ML/DL-based eavesdropper detection methods tailored for B5G IIoT networks, showing high accuracy in simulated industrial scenarios.

Findings

DCNN and RF models achieve near 100% detection accuracy

Models classify users based on CSI, position, and power data

Zero false alarms in eavesdropper detection

Abstract

Advanced fifth generation (5G) and beyond (B5G) communication networks have revolutionized wireless technologies, supporting ultra-high data rates, low latency, and massive connectivity. However, they also introduce vulnerabilities, particularly in decentralized Industrial Internet of Things (IIoT) environments. Traditional cryptographic methods struggle with scalability and complexity, leading researchers to explore Artificial Intelligence (AI)-driven physical layer techniques for secure communications. In this context, this paper focuses on the utilization of Machine and Deep Learning (ML/DL) techniques to tackle with the common problem of eavesdropping detection. To this end, a simulated industrial B5G heterogeneous wireless network is used to evaluate the performance of various ML/DL models, including Random Forests (RF), Deep Convolutional Neural Networks (DCNN), and Long Short-Term Memory (LSTM) networks. These models classify users as either legitimate or malicious ones based on channel state information (CSI), position data, and transmission power. According to the presented numerical results, DCNN and RF models achieve a detection accuracy approaching 100\% in identifying eavesdroppers with zero false alarms. In general, this work underlines the great potential of combining AI and Physical Layer Security (PLS) for next-generation wireless networks in order to address evolving security threats.