Physical Layer Authentication for Industrial Control Based on Convolutional Denoising Autoencoder
Yanru Chen, Haoyu He, Shengjie Liu, Yuanyuan Zhang, Yang Li, Bin Xing, Bing Guo, Liangyin Chen
Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -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.