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.

In the sixth generation and Internet of Things (6G-IoT) systems, impersonation attacks pose significant risks due to the high interconnectivity, and complexity of devices. This study develops a new, improved physical layer (PHY-layer) authentication method to counter impersonation attacks in an 6G-IoT system by leveraging radiation pattern feature in gain, phase, and position errors. We analyze the statistical behavior of radiation pattern with random errors in gain, phase, and position, and establish statistical models based on this analysis. Subsequently, we devise an authentication scheme that utilizes these statistical models to validate the legitimacy of the IoT device. Finally, a performance assessment has been carried out to validate the dependability and efficacy of the authentication scheme across diverse settings.

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.

Optical Data Center Networks (ODCNs) are high-performance interconnect architectures in parallel and distributed computing, providing higher bandwidth and lower power consumption. However, current optical DCNs struggle to achieve both high scalability and incremental scalability simultaneously. In this paper, we propose an extendedExchanged hyperCube, denoted by ExCube, which is a highly scalable network architecture for optical data centers. Firstly, we detail the address scheme and constructing method for ExCube, including exponential, linear, and composite scalability, which can adapt to different scalability requirements. ExCube boasts flexible scalability modes, including exponential, linear, and composite scalability, meeting diverse scalability requirements. In particular, the diameter of ExCube remains unchanged as its size increases linearly, indicating superior incremental scalability. Secondly, an efficient routing algorithm with linear time complexity is presented to determine the shortest path between any two different ToRs in ExCube. Additionally, we propose a per-flow scheduling algorithm based on the disjoint paths to enhance the performance of ExCube. The optical devices in ExCube are identical to those in existing optical DCNs, such as WaveCube and OSA, facilitating its construction. Experimental results demonstrate that ExCube outperforms WaveCube in terms of throughput and reduces data transmission time by 5%-35%. Further analysis reveals that ExCube maintains comparable performance to WaveCube across several critical metrics, including low diameter and link complexity. Compared with advanced networks, the overall cost-effectiveness and energy efficiency of ExCube have been reduced by 36.7% and 46.5%, respectively.

Authentication is a crucial method for ensuring the security of the unmanned aerial vehicle (UAV)-assisted communication systems; however, it also raises concerns about the leakage of private data. To address this problem, this paper focuses on the problem of identity authentication with privacy-preserving consideration. We propose a two-way privacy-preserving physical layer (PHY-Layer) authentication framework in a UAV-assisted multiple-input multiple-output (MIMO) communication system, by exploiting the carrier frequency offset (CFO) characterizing UAV identity and CFO-based session keys constructed by elliptic curve cryptography (ECC) to encrypt data frames. In particular, we first employ a MOOSE algorithm to extract the hardware fingerprint feature related to UAV, and based on the extracted CFO feature parameters, we devise an ECC-based algorithm for a session key negotiation. To achieve identity validation for the network parties, we establish a two-way authentication framework based on the resulting CFO feature parameters, and apply the CFO-based session key to encrypt data frames for avoiding the leakage of private data. Moreover, we derive the closed-form analytical expressions for the probabilities of the detection and false alarm for the rigorous performance analysis. Finally, we provide extensive numerical results to validate the proposed theoretical model and demonstrate its effectiveness in both identity authentication and privacy preservation. In comparison with the prior scheme, the proposed framework has a better robustness and privacy.

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 focuses on developing a reliable physical layer authentication (PLA) scheme in an millimeter wave (mmWave) communication system. To this end, we first derive the statistical quantities of the radiation pattern with random array errors in terms of gain, phase and position, and demonstrate that both Beckmann distribution and Rice distribution can effectively characterize the distorted radiation pattern. We then design a highly reliable PLA scheme, which combines three array error features to increase the distinguishability of the radiation pattern fused these array errors, as well as creates constructive two-beam pattern transmission that can not only resist to occasional blockages of few constituent beams but also enhance the reliability of the PLA. Applying the principles of statistical signal processing and composite hypothesis testing, a theoretical framework modeling of the typical performance metrics is also established to assess the performance of the proposed novel authentication scheme, under Rice distribution approximation model for radiation pattern statistics. Finally, performance evaluation is verified the reliability, effectiveness of the proposed authentication scheme with various settings in the presence of the identity-based impersonate attack, and performance comparison is also provided to highlight performance gain using the three array errors and two-beam pattern transmission.

Authentication in millimeter-Wave (mmWave) Multiple-Input Multiple-Output (MIMO) systems is a critical issue due to the unique characteristics of mmWave communication, such as highly directional beamforming and the ability to support massive device connectivity. To address this challenge, this paper proposes a novel low-complexity decision-level-based Distributed Physical Layer Authentication (DPLA) framework to combat identity-based impersonation attacks in mmWave MIMO systems. The DPLA framework leverages Beam Pattern (BP) deviation, which arises from hardware-specific gain errors, as a key authentication feature. A fusion center is introduced to make the final authentication decision by aggregating local decisions from multiple collaborative nodes, enabling multi-directional perception. Specifically, a low-complexity hybrid combining fusion rule is carefully designed to accommodate the fully connected structure of mmWave MIMO systems, balancing computational efficiency and authentication performance. A rigorous performance analysis is conducted by deriving closed-form analytical expressions for the probabilities of correct detection and false alarm. Furthermore, the asymptotic detection and discrimination performance are systematically analyzed in the large-scale antenna regime. To further enhance authentication accuracy, digital signaling matrices are designed using the deflection coefficient maximization principle. The feasibility of the proposed framework is validated through a comprehensive evaluation, demonstrating its superior robustness and efficiency compared to benchmark methods.

With the rapid increase in user demand for business traffic, the deployment and migration of virtual service function chains (VSFC) considering network load balancing has become a fascinating research topic. In this paper, we delve into the cascading failures in virtual network function (VNF) migration and design a topology-awareness fault-tolerant migration mechanism for VSFC. Firstly, we design a measurement model for the evolution of network nodes under cascading faults, which provides an evaluation method for node importance. Secondly, we present aTopology andResourceAware VNF fault-tolerant migration framework (TRA) under cascading faults. Finally, we investigate a topology-awareness energy consumption optimization algorithm based on the cascading failure. The proposed algorithm can reduce network energy consumption and alleviate network link congestion while reasonably migrating virtual machines to meet system performance requirements. Through extensive simulation experiments and real testbed evaluations,TRAreduced migration time and average latency by 28.6% and 24.7% compared with the average performance of VVi, TeaVisor, and MiOvnm, respectively. In addition,TRAhas improved revenue to expense ratio and load balancing index by 19.6% and 26.5%, respectively.

This paper investigates the use of physical (PHY)-layer characteristics of wireless channels for identity authentication in Reconfigurable Intelligent Surface (RIS)-assisted wireless communication systems. We propose a generalized PHY-layer authentication scheme that involves both direct and cascaded channels for RIS-assisted IoT systems. The channels in each coherence block of the authentication phase are estimated using least squares estimation, and the estimation error covariance matrices are determined for performance evaluation. Leveraging the extracted features of the direct and cascaded channels, we establish authentication verification as a problem of differentiating two classes of changes. Auxiliary information in the form of second-order statistics assists the authentication verification procedure. Through statistical derivation using tools from statistical signal processing, matrix analysis, and composite hypothesis testing, we characterize false alarm and detection probabilities of the proposed scheme. Finally, we provide extensive numerical results to evaluate the performance and demonstrate the efficiency of the proposed authentication scheme across various system parameters.

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.

Cooperative jamming (CJ) is a promising technique for enhancing the physical-layer security in wireless powered sensor networks. The secrecy performance of CJ-aided wireless powered sensor networks is affected by three issues including disguised eavesdropper as cooperative node, estimation error of the channel between sink node and each sensor node, and distance-related limitation on the transmit power of cooperative nodes. To address the above issues, this paper proposes a CJ-aided secure communication scheme for wireless powered sensor networks with disguised eavesdropper and imperfect channel estimation. Since each cooperative node could be the disguised eavesdropper, the proposed scheme incorporates all possible cases of the eavesdropper disguising itself as unfixed cooperative jamming node. The maximization of secrecy rate over all possible cases is formulated for designing secrecy optimization problem. The imperfect channel estimation and distance-related jamming power limitation are both integrated as constraints into the secrecy rate maximization problem. Since the original optimization problem is complex and non-convex, a two-level optimization algorithm is proposed to solve it. Simulation results show that the proposed scheme achieves significant secrecy rate improvement over typical existing schemes.

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 exploits the spatial–temporal features of user screen-touch trajectory (STT) to develop a user authentication framework for Industrial Internet of Things (IIoT) systems. We first model the STT as a trajectory image and apply the speeded-up robust features (SURF) algorithm for STT spatial feature characterization. We then model the STT as a time series and employ the hidden Markov model (HMM) for the STT temporal feature extraction. We further design a classifier based on HMM for the above temporal feature and also a classifier based on eXtreme Gradient Boosting for the spatial feature. By combining the two classifiers and assigning each classifier an appropriate weight, we develop a passive user authentication framework. The new framework has the potential to significantly impact the IIoT security practices by offering a flexible and efficient authentication method for IIoT systems, and it also can serve as a complementary solution or an enhancement for the traditional authentication mechanism of such systems.

CRYSTALS-Kyber, as the only public key encryption (PKE) algorithm selected by the National Institute of Standards and Technology (NIST) in the third round, is considered one of the most promising post-quantum cryptography (PQC) schemes. Lattice-based cryptography uses complex discrete algorithm problems on lattices to build secure encryption and decryption systems to resist attacks from quantum computing. Performance is an important bottleneck affecting the promotion of post quantum cryptography. In this paper, we present a High-performance Implementation of Kyber (named HI-Kyber) on the NVIDIA GPUs, which can increase the key-exchange performance of Kyber to the million-level. Firstly, we propose a lattice-based PQC implementation architecture based on kernel fusion, which can avoid redundant global-memory access operations. Secondly, We optimize and implement the core operations of CRYSTALS-Kyber, including Number Theoretic Transform (NTT), inverse NTT (INTT), pointwise multiplication, etc. Especially for the calculation bottleneck NTT operation, three novel methods are proposed to explore extreme performance: the sliced layer merging (SLM), the sliced depth-first search (SDFS-NTT) and the entire depth-first search (EDFS-NTT), which achieve a speedup of 7.5%, 28.5%, and 41.6% compared to the native implementation. Thirdly, we conduct comprehensive performance experiments with different parallel dimensions based on the above optimization. Finally, our key exchange performance reaches 1,664 kops/s. Specifically, based on the same platform, our HI-Kyber is 3.52× that of the GPU implementation based on the same instruction set and 1.78× that of the state-of-the-art one based on AI-accelerated tensor core.

Abstract This paper studies the problem of spatially correlated channel estimation for reconfigurable intelligent surface (RIS)‐assisted multiple‐input multiple‐output (MIMO) systems with arbitrarily correlated Gaussian perturbation. In particular, composite RIS channel estimation is first explored based on minimum mean square error (MMSE) method, and optimal training sequence design of pilots with MSE minimization. Applying the Kronecker‐structured model, correlated composite RIS‐related channels and correlated Gaussian perturbation are then characterized. The optimal training sequence structure and spatial training power allocation conditions are derived, and the choice of the optimal training sequence length is also analyzed. Finally, numerical results are provided to assess the performance of the proposed estimate method under different training sequence structures, the optimal length of the training sequence, and information statistics. Extensive numerical results show that the proposed estimate method has supreme performance compared to state of‐the‐art baseline methods (e.g. maximum likelihood and two‐sided linear channel estimation methods).

Passive user authentication is critical for the secure operation of Industrial Internet of Things (IIoT) systems. By jointly utilizing both the time-varying characteristics of the user sequential operation actions and spatial variation characteristics of channel state information (CSI) caused by these actions, this paper proposes a novel two-dimensional passive authentication framework for IIoT systems. In particular, we construct the time-varying operation action sequences from the routine work process of a user and apply the Hidden Markov Model to characterize behavioral biometric characteristics of the user, and also employ the eXtreme Gradient Boosting model to depict the spatial variation characteristics of CSI related to the user. By designing two classifiers corresponding these two characteristics and assigning each classifier an appropriate weight, we propose a two-dimensional user authentication framework for continuous and non-intrusive user authentication in IIoT scenarios. Extensive experiments are conducted to illustrate the authentication performance of the proposed authentication framework in terms of false acceptance rate, false rejection rate and equal-error rate. We further investigate the related authentication efficiency issues like the sensitivity to the weights for classifiers, the sensitivity to authentication time and the capability of resisting against impersonation attacks.

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.

The communication channels in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems possess some unique fine-grained angle domain features such as channel gain, azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA). This paper combines AAoA, EAoA, channel gain as well as phase noise features to propose a novel physical layer authentication scheme for mmWave MIMO communication systems. Based on the limit posterior Bayesian Cramér-Rao bound (LPBCRB) and maximum-likelihood (ML) estimation theories, we first develop an efficient approach for the evaluation of hardware phase noise and mmWave channel features. To depict the authentication performance of the new scheme, we then apply the statistical signal processing and hypothesis testing theories to derive the closed-form expressions for false alarm and detection probabilities under the scheme. Finally, extensive numerical results are provided to validate our theoretical models and to demonstrate the capability of the proposed authentication scheme against impersonation attacks.

With the continuous development of Industry 4.0 technology, the embedded devices in Industrial Internet of Things (IIoT) are showing explosive growth, and large-scale cyber attacks or related security incidents continue to sound the alarm bell of information security. IIoT has strict requirements on computing performance and energy consumption, which poses severe challenges to cryptographic algorithms, especially public key cryptographic algorithms with high computational complexity. Embedded graphic processing unit (GPU) devices, always as edge computing nodes or AI accelerators, are widely deployed in IIoT applications. In this article, we propose an embedded GPU-based Four$\mathbb {Q}$(EG-Four$\mathbb {Q}$) elliptic curve public key cryptographic acceleration scheme. As far as we know, EG-Four$\mathbb {Q}$is the first work to completely implement Four$\mathbb {Q}$on the GPU platforms, including finite field operations, point arithmetic, and scalar multiplication. Relying only on 36-W power consumption, our scalar multiplication performance reaches 1717 kops/s with the latency of 2.38 ms. In terms of the energy-efficiency ratio, EG-Four$\mathbb {Q}$has significant advantages over other platforms such as advanced RISC machines (ARM) CPU, Intel CPU, field programmable gate array (FPGA), and desktop GPUs. The throughput of EG-Four$\mathbb {Q}$is 1.75 times that of the fastest elliptic curve cryptography implementation based on the same platform and even exceeds the performance of Intel top server CPU E5-2699v3 (18-core). Based on the embedded GPU Xavier, EG-Four$\mathbb {Q}$can act as a cryptographic edge computing module or even a cloud cryptographic accelerator, providing more efficient elliptic curve cryptographic services for IIoT.

Hash is one of the most important algorithms of cryptography, it is widely used in cryptographic primitives, such as digital signature, key exchange and so on. Further, hash cryptography is also the core operation of blockchain technology. With the explosive growth of the number of IoT devices and the rapid development of blockchain technology, the computing performance of hash has received widespread attention. The GPU high-performance computing platforms with a number of arithmetic cores are widely used in cryptographic optimization and acceleration. In this paper, we propose an efficient parallel accelerated framework of SM3 cryptography hash function based on GPU parallel computing devices, short for GPU-based SM3 (G-SM3). Our G-SM3 optimizes the implementation of the hash cryptographic algorithm from three aspects: parallelism, memory access and instructions. On the desktop GPU NVIDIA Titan V, the peak performance of G-SM3 reaches 23 GB/s, which is more than 7.5 times the performance of OpenSSL on a top-level server CPU (E5-2699V3) with 16 cores. On the embedded GPU which consumes less than 40 W, the SM3 throughput reaches 3.8 GB/s, which is even better than the performance of the serverlevel CPU. Based on the same GTX 1080, our performance is 1.12 times that of the fastest known GPU implementation, and the latency is reduced by more than 95%. Compared to other platforms, our G-SM3 has a huge advantage.

Passive authentication is of great importance for security guarantee in industrial Internet of Things (IIoT) systems. Based on the behavioral biometrics from sequential operation actions in IIoT, this article proposes a nonintrusive and passive authentication framework for continuous user authentication against the impersonation attack. We first provide experimental results to demonstrate the discriminability and stability for the intrinsic features of sequential operation actions, and then leverage the Kalman filtering and wavelet techniques for noise elimination and the singular value decomposition method for the dimensionality reduction of feature space. We further exploit the one-class classification technique to formulate the authentication decision process as a hidden Markov model (HMM). Extensive experiments are conducted to illustrate the authentication performance of the passive authentication framework in terms of the false acceptance rate, false rejection rate, and equal-error rate. We also investigate the related authentication efficiency issues in terms of the usability to the operation-action sequence length, the scalability to the number of features and user space, and the sensitivity to the operation action features.

By exploiting two intrinsic physical (PHY)-layer features in terms of location-specific channel gain and transmitter-specific phase noise, this paper proposes a new PHY-layer authentication scheme for massive multiple-input multiple-output (MIMO) systems. In particular, we apply the linear minimum mean square error technique to estimate the time-varying channel gain and adopt extended Kalman filtering to track the time-varying phase noise. Based on the estimation error covariance matrices of channel gain and phase noise, we then formulate the PHY-layer authentication as a composite hypothesis testing problem. With the help of tools from statistical signal processing, matrix analysis, and composite hypothesis testing, we develop theoretical models to capture the false alarm and detection probability performances of the proposed scheme. Finally, we provide extensive numerical results to validate these theoretical models and to illustrate the efficiency of the proposed authentication scheme.

This article proposes a general and lightweight PHY-layer authentication framework for the Internet of Things (IoT) devices in smart cities, based on tag embedding and tag verification. More specifically, a tag signal carefully designed to be independent of the message signal of a transmitter [i.e., an IoT device (IoTD)] is encrypted and embedded into the signal of the device, and the tag signal is then retrieved at a receiver based on signal detection techniques to verify if it is from the legitimate IoTD or from an illegitimate adversary. With the help of matrix analysis and composite hypothesis testing theories, analytical models are further developed to depict the authentication performance of the proposed authentication framework under various tag signal models. We then provide numerical results to validate these analytical models and to illustrate how authentication performance against the typical impersonation attack varies with system parameters. Finally, we include discussions to demonstrate the effectiveness of the proposed authentication solution in resisting against other various attacks like replay, unauthorized detection, tampering, and man-in-the-middle.

In this paper, we propose a physical layer authentication scheme in heterogeneous coexist multiple-input-multiple-output (MIMO) systems. This scheme utilizes two physical layer features in terms of location-specific channel gains and transmitter-specific phase noise caused by imperfect oscillators to identify transmitters. Three properties of the proposed scheme: covertness, robustness, and security, are analyzed in detail. By using a maximum-likelihood estimator (MLE) and extended Kalman filter (EKF), we estimate channel gains and phase noise, and formulate variances of estimation errors. We also quantize the temporal variations of channel gains and phase noise through the developed quantizers. Based on quantization results and theories of hypothesis testing and stochastic process, we then derive the closed-form expressions for false alarm and detection probabilities with the consideration of quantization errors. Simulations are carried out to validate the theoretical results of the two probabilities. Based on theoretical models, we further demonstrate that the proposed scheme makes it possible for us to flexibly control authentication performance by adjusting thresholds (for channel gain, phase noise, and decision, respectively) to achieve a required authentication performance in specific MIMO applications.

We study transmitter authentication in massive multiple-input multiple-output (MIMO) systems with non-ideal hardware for the fifth generation (5G) and beyond networks. A new channel-based authentication scheme is proposed by taking hardware impairments into account. Based on signal processing theory, we first formulate channel estimation under hardware impairments and determine error covariance matrix to assess the quantity caused by hardware impairments on authentication performance. With the help of hypothesis testing and matrix transformation theories, we are then able to derive exact expressions for the probabilities of false alarm and detection under different channel covariance matrix models. Extensive simulations are carried out to validate theoretical results and illustrate the efficiency of the proposed scheme. Impacts of system parameters on performance are revealed as well.