Beam alignment (BA) is a crucial process in millimeter-wave (mmWave) communications for precise directional transmission and efficient link establishment, but its open nature makes it vulnerable to eavesdropping and identity impersonation attacks. To this end, we propose a novel security framework named CovertAuth, designed to provide a unified defense against both threats. For eavesdropping attacks, we advance the existing covert communication design by extending its joint optimization of the beam training budget and transmission power to a more practical BA scenario that incorporates the mutual coupling (MC) effect in antenna array impairments and imperfect eavesdropper channel state information. For impersonation attacks, the MC effect is explored as a device feature to design a novel adaptive weight-based physical layer authentication mechanism. Theoretical models for authentication metrics like detection and false alarm probabilities are first provided to conduct performance analysis. Based on these models, an optimization problem is constructed to determine the optimal weight value that maximizes authentication accuracy. With these optimal weights, a weighted-sum energy detector is employed to achieve identity validation. The resulting adaptive authentication scheme is then integrated with the covert communication to enhance the security of the BA phase. Finally, simulation results demonstrate that CovertAuth achieves high detection accuracy while satisfying the covertness requirement, offering a comprehensive security solution for the mmWave BA stage.

This letter aims to utilize antenna array impairment-based mutual coupling and spatial angle of arrival features to devise a linear weighted physical layer authentication scheme in millimeter wave multiple-input multiple-output systems. The analytical expressions of performance metrics like detection and false alarm probabilities are also derived to conduct a theoretical analysis of the proposed scheme. To effectively exploit the authentication potential gained from the collaboration of adopted two-factor features, we formulate an optimization problem for obtaining the optimal feature weight to maximize the detection accuracy of the proposed scheme, subject to a false alarm constraint. Numerical results demonstrate the superiority of our authentication framework regarding device identity validation compared to existing works.

The super-directive arrays in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems possess some intrinsic beam pattern features like amplitude and phase errors of element excitations. We first show experiment results to illustrate the uniqueness and stability properties of the amplitude and phase error features, and apply the Rician probability density function to efficiently approximate the distributions of these features under hardware impairments. By utilizing such beam pattern features and the hypothesis testing theory, we then develop a new physical layer authentication protocol for transmitter authentication in the mmWave MIMO communication systems. Based on the theories of statistical signal processing and composite hypothesis testing, a related theoretical framework is also developed for the analytical performance modeling of the new authentication protocol under both fixed and random beam pattern scenarios. Finally, we provide simulation and theoretical results to evaluate the reliability and security of the new authentication protocol against the identity-based spoofing attack.

This paper exploits two intrinsic hardware-specific fingerprints in terms of carrier frequency offset (CFO) and phase noise (PHN) to propose a two-dimensional physical layer authentication (PLA) scheme in the unmanned aerial vehicle (UAV)-aided communication systems. By leveraging expectation conditional maximization (ECM), extended Kalman filtering (EKF) algorithms and binary hypothesis testing, we first extract the inherent hardware impairments of UAV-aided systems including CFO and PHN as PHY-layer fingerprints to establish an authentication framework. To accurately characterize authentication performance, we examine the hybrid Cramér-Rao lower bound (HCRLB) for individual estimators of CFO and PHN, and then theoretically derive the analytical expressions for the false alarm and detection probabilities by utilizing tools from statistical signal processing. Finally, extensive numerical results are provided to validate the correctness of the developed theoretical models and to illustrate the authentication performance of the proposed scheme under various system parameters.

This paper exploits the efficient channel modeling and channel sparsity to propose a novel Physical (PHY)-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-ground system. Inspired by the Image Processing theory, we first explore a new Laplace prior approach for the efficient modeling of angular-domain mmWave MIMO channels. With the help of the new channel model, we then reveal the channel sparsity in the concerned system exhibiting a nice spatial correlation property. By a joint use of channel sparsity correlation, efficient sparsity feature extraction with Expectation Maximization (EM)/Generalized Approximate Message Passing (GAMP) algorithms and hypothesis testing, we thus devise a new authentication framework for the concerned system. Theoretical performance analysis is also carried out by deriving the closed-form expressions for false alarm and detection probabilities. Finally, extensive numerical results are provided to validate the feasibility of the proposed channel model and the theoretical analysis, as well as to demonstrate the efficiency of the new authentication framework under various scenarios.

This article proposes a tag-based approach for physical (PHY)-layer authentication in a reconfigurable intelligent surface (RIS) communication system. We first extract the intrinsic PHY-layer features of RIS communication systems in terms of channel gain and background noise, and then apply these PHY-layer features, a random signal as well as the private key of the transmitter to construct a robust cover tag signal against the impersonation attack. We adopt an asymmetric cryptography technique to encrypt tagged signals and to resist against unauthorized detection and tampering attacks during the transmission process. The receiver then applies the maximum a-posteriori (MAP) ratio test to conduct authentication based on the received tag signal, a reference tag signal transmitted in training phase and the knowledge of distributions of the channel gain, background noise and the random signal. We also provide security analysis to demonstrate how the proposed scheme can resist unauthorized detection, tampering attacks, etc. With the help of tools of the MAP ratio test, maximum likelihood estimation, we further analyze the distribution of the test statistics and derive analytical models for the false alarm and detection probabilities. Finally, extensive simulations are conducted to verify the theoretical results and to illustrate the performance of the proposed scheme.