Physical Layer Authentication for Industrial Control Based on Convolutional Denoising Autoencoder

Industrial control systems rely on wireless devices and sensors, necessitating critical security. Physical layer authentication (PLA) is a promising mechanism for device authentication, utilizing its unique spatiotemporal characteristics and channel state randomness, which offers unforgeability and high informatics security with low computational overhead and efficiency in resource-constrained scenarios. However, existing PLA mechanisms face challenges in complex industrial wireless environments, including insufficient accuracy, computational complexity, inadequate noise consideration, and poor performance. To address these challenges, we propose a convolutional denoising autoencoder (CDAE) model that reduces feature dimensions, eliminates noise, and extracts key vectors. The weighted
$k$
-nearest neighbor algorithm classifies the extracted vectors for comprehensive authentication in control system networks. Accurate authentication enables efficient detection of malicious attacks. Simulation experiments show that using CDAE-extracted feature vectors achieves over 95% accuracy with only 1% training samples, surpassing channel state information-based authentication by 46.15%, validating the proposed mechanism’s effectiveness.