This paper addresses the spoofing detection issue for the Global Positioning System (GPS) based on radio frequency fingerprinting (RFF). We first introduce a new RFF feature in the post-despreading domain of GPS signal processing to better capture the hardware characteristics of GPS satellites. We model the new RFF feature as grayscale constellation images (GCIs) and provide in-depth analysis to show the hardware characteristics that can be captured by GCIs. Using this feature, we develop a deep-learning based spoofing detection framework named GCI-GANomaly, which applies a generative adversarial network (GAN) with a cosine latent anomaly scoring strategy for robust detection. We evaluate the detection accuracy and false positive rate (FPR) of the proposed method based on the open-source Texas Spoofing Test Battery (TEXBAT) dataset. The results showed that GCI-GANomaly improves the detection accuracy of traditional signal quality monitoring (SQM)-based methods by up to 30.8% and reduces the average FPR of existing RFF-based methods by 4.2% with much less training data while achieving slightly better average detection accuracy. We further evaluate the robustness (against spoofing power and time) of GCI-GANomaly based on a specific GPS signal dataset that we collected from the real-world constellation. The results showed that GCI-GANomaly achieves robust detection performance under varying spoofing power and shows acceptable stability as time elapses.

Traditional physical layer authentication (PLA) schemes in wireless networks typically depend on individual nodes for feature observation, lacking cooperative gain and thus suffering from limited accuracy and robustness. This paper investigates a cooperative transmission and PLA framework for wireless networks, wherein multiple legitimate devices serve as both message relays and identity verifiers by extracting hardware fingerprints. We first establish a theoretical model for the system’s bit error rate (BER), false alarm rate (FAR), and detection probability (PD), and derive closed-form upper bounds to characterize the transmission reliability and authentication performance. Based on our theoretical model, we then define an accuracy improvement ratio (AIR) metric that quantifies FAR gain without compromising BER and PD performance, and derive channel conditions to ensure a positive AIR. To validate the proposed integrated framework, a time-division multiple access (TDMA)-based case study is conducted using carrier frequency offset as the authentication feature. Simulation results demonstrate that the proposed scheme can reduce FAR by up to 100.0% under favorable signal-to-noise ratio (SNR) conditions, while maintaining the BER and PD performance of the conventional non-cooperative scheme, thereby confirming its effectiveness for enhancing secure wireless communications.

Traditional device authentication techniques relying only on a single device for authentication are susceptible to poor performance due to insufficient channel observations. Although these challenges are mitigated by centralized collaborative authentication schemes, such schemes assume that all users desire to learn the same model. They further assume constant collaborator presence, leading to poorly trained models in Internet of Vehicles (IoV) environments with fleeting encounters. Moreover, they are vulnerable to GPS spoofing attack. To overcome these limitations, we propose a Gossip Learning (GL)-based collaborative Physical Layer Authentication (PLA) scheme, where collaborators assist vehicles in authenticating transmitters by distributively training their Long Short-term Memory (LSTM) models using the location and Channel State Information (CSI) of the transmitters. The collaborators also improve their model instances by incorporating the learned experiences of other vehicles they meet opportunistically. Instead of using the claimed location of transmitters during authentication, which may be affected by the GPS spoofing attack, their Received Signal Strength (RSS) and Angle of Arrival (AoA) features are used to estimate their current location. The estimated location is then used to predict their current CSI for authentication. Moreover, we propose a distance-based GPS signal spoofing detection algorithm, which ensures that only collaborators not under GPS spoofing attack are selected for collaborative training. The simulation results from experiments conducted using realistic channel attributes obtained from the Quasi-Deterministic Radio channel Generator (QuaDRiGa) platform demonstrate the effectiveness of our system in IoV scenarios, underscoring its relevance to Intelligent Transportation Systems and its superiority over existing techniques.

Unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) with a blockchain consensus algorithm is a promising solution for addressing mobile offloading of computation-intensive or latency-sensitive tasks with extensive service range, while ensuring the authenticity of the offloading. Existing UAV-aided MEC studies focus on task offloading or trajectory planning of single-antenna UAVs, missing multiple-input multiple-output (MIMO) UAV systems. In addition, most blockchain-based UAV-aided edge computing studies adopt energy-consuming proof-of-work-based consensus mechanisms, which are unsuitable for energy-limited UAV scenarios. To address the above issues, in this paper, we develop a joint deadline-aware task offloading and UAV trajectory planning scheme, utilizing the effects simulated through an energy-efficient consensus protocol. In order to prevent the exhaustion of UAVs' energy and ensure the authenticity of decision-making, we propose an energy-based Raft (E-Raft) consensus algorithm, enabling dynamic leader selection through a series of decreasing energy thresholds. Subsequently, we present a computational profit maximization problem to jointly optimize deadline-aware task offloading and the UAV swarm's trajectory planning. To address this NP-hard problem, we develop an online priority-based task offloading and trajectory selection algorithm, which is performed by the selected leader of the E-Raft consensus in each round. Simulation results demonstrate that our proposed scheme achieves up to 40% performance improvement over other approaches.

Traditional physical‐layer authentication (PLA) approaches primarily rely on a limited set of hardware features, which restricts their robustness and accuracy in dynamic wireless environments. This paper proposes a novel PLA enhancement framework based on nonlinear signal analysis, introducing detrended fluctuation analysis (DFA) as a distinctive hardware fingerprinting feature. We first model the statistical properties of DFA and analytically derive its dependence on intrinsic hardware imperfections, thereby establishing its feasibility for device authentication. To improve overall system performance, the DFA feature is further fused with conventional features such as fractal dimension and carrier frequency offset (CFO) within a shallow classification framework. Experimental evaluation is conducted on real signal data collected from 28 commercial devices, demonstrating that the proposed multifeature PLA scheme can significantly improve authentication accuracy, confirming the effectiveness of DFA in enhancing physical‐layer security without additional cryptographic overhead.

The fifth-generation mobile communication technology (5G) supports wireless data transmission across civil, commercial, industrial, and even military networks, where vast amounts of privacy data are constantly transmitted. However, the open nature of the air interface in 5G systems makes them vulnerable to various attacks. Physical-layer key generation (PKG) has been recognized as a highly promising technology for ensuring data security in 5G systems, while existing PKG schemes achieve low key entropy (i.e., low key randomness) due to poor channel probing and quantization. In this paper, we propose a PKG scheme with high key entropy, tailored to the unique characteristics of 5G systems. First, we design a channel probing method based on the demodulation reference signal in accordance with 5G standards, enhancing the similarity between channel measurements. Next, we introduce a quantization method based on local increment and monotonicity, which effectively leverages channel characteristics to achieve high-speed key generation and improve key entropy. Finally, we use both MATLAB simulation and real-world channel measurements to achieve comprehensive verification of the proposed scheme. The simulation results showed that the proposed scheme increases the key entropy by at least 25% with nearly the same key generation rate compared with existing PKG schemes for 5G systems. The experiment using real-world channel measurements also confirmed that the proposed scheme has higher key entropy.

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.

Warden collusion represents a hazardous threat to wireless covert communication, where wardens can combine their observations to perform a more aggressive detection attack. This paper investigates the impact of warden collusion on covert communication in a multi-antenna wireless network consisting of one source, one destination, multiple wardens and interferers. By employing the techniques of Laplace Transform and Cauchy Integral Theorem, we first establish a framework to model the aggregate interference distribution (AID) for covert communication in the network under the typical additive white Gaussian noise (AWGN) and Rayleigh fading channels. Based on the AID results, we then develop theoretical models to reveal the inherent relationship between the collusion intensity and fundamental communication metrics in terms of the covert outage probability, connection outage probability and covert throughput. With the help of these models, we further explore the covert throughput optimization problems and present extensive numerical results to illustrate the impact of warden collusion on the covert throughput under both channel models.

Edge computing is conducive to reducing service response time and improving service quality by pushing cloud functions to a network's edges. Most existing works in edge computing focus on utility maximization of task offloading on static edges with a single antenna. Besides, trajectory planning of mobile edges, e.g., autonomous aerial vehicles (AAVs) is also rarely discussed. In this paper, we are the first to jointly discuss the deadline-ware task offloading and AAV trajectory planning problem in a multi-input multi-output (MIMO) AAV-aided mobile edge computing system. Due to discrete variables and highly coupling nonconvex constraints, we equivalently convert the original problem into a more solvable form by introducing auxiliary variables. Next, a penalty dual decomposition-based algorithm is developed to achieve a global optimal solution to the problem. Besides, we proposed a profit-based fireworks algorithm in a relatively lower time to reduce the execution time for large-scale networks. Extensive evaluation results reveal that our proposed optimal algorithms could significantly outperform static offloading algorithms and other algorithms by 25% on average.

This paper examines the enhancement of secrecy energy efficiency (SEE) in a reconfigurable intelligent surface (RIS)-assisted two-way relay (TWR) system. We first establish a theoretical model for the system's secrecy rate, energy consumption, and SEE, and formulate the SEE maximization problem through the joint design of the RIS phase shifts and beamforming matrix. Using techniques such as weighted minimum mean square error (WMMSE), alternating optimization, and the augmented Lagrange method, we then develop a theoretical framework that identifies locally optimal solutions for the RIS and beamforming settings under unit-modulus and power constraints. The proposed framework is also shown to be applicable to solving the system's secrecy rate maximization problem. To address the computational complexity involved in optimizing the RIS phase shifts, we further propose a suboptimal scheme leveraging the Newton's method, which significantly reduces the computational burden while achieving performance close to the optimal SEE. Extensive numerical results validate the effectiveness of the proposed schemes, showing significant SEE improvements compared to traditional channel-capacity-based secure transmission scheme.

This paper investigates the enhancement of secrecy energy efficiency (SEE) in a THz MIMO two-way relay system utilizing simultaneous wireless information and power transfer (SWIPT) and precoding techniques. We first model the SEE metric of the system under random SWIPT and precoding settings, and subsequently formulate the SEE maximization problem as a non-convex programming problem. By employing techniques such as Dinkelbach transformation, alternating optimization, and difference of convex programming, we then develop a multi-level theoretical framework to jointly optimize the energy splitting factor and precoding matrices. As a special case, we further demonstrate that the proposed theoretical framework can also be applied to solving the secrecy rate maximization problem. Finally, with the help of orthogonal-triangular decomposition and the Newton-Raphson method, we provide the optimal structure of the relay precoding matrix and a low-complexity suboptimal solution scheme for SEE maximization. Extensive numerical results validate our theoretical findings and demonstrate that the proposed schemes can significantly improve the system’s SEE compared to the conventional microwave-based schemes and the random baseline.

This paper studies the possible physical layer security (PLS) enhancement in a wiretapped two-way relay system from utilizing the reconfigurable intelligent surface (RIS) and beamforming techniques. We first jointly apply the RIS and beamforming (RIS-B) to develop an advanced secure transmission scheme for the system, and conduct related theoretical modeling for the system secrecy sum rate (SSR) under the RIS-B scheme. We then apply the theories of alternating optimization, successive convex approximation, augmented Lagrange method and the quasi-Newton method to devise both the optimal and low-complexity sub-optimal frameworks to identify the optimal settings of RIS phase shifts and beamforming matrix of the RIS-B scheme for SSR maximization. We further investigate a special case when solely the RIS technique is applied for SSR performance enhancement. With the help of Charnes-Cooper transformation, semidefinite programming and Gaussian randomization, we devise an optimization framework to identify the optimal settings of RIS phase shifts for SSR maximization. Finally, extensive numerical results are provided to illustrate the efficiency of the RIS and RIS-B schemes in PLS enhancement in comparison with the conventional channel-capacity-based secure transmission.

Satellite internet serves as an indispensable component of the upcoming sixth‐generation networks for providing global broadband internet access service. Due to the open nature of satellite‐ground communication, security issue in satellite internet has always been an important concern for both industry and academia. Although many researchers focus on secure communications in satellite internet, the literature is surprisingly sparse, with no comprehensive overview of the state‐of‐the‐art security techniques. This paper provides an in‐depth survey of secure communications for various satellite internet scenarios. Based on different security mechanisms, we first categorize the existing works of secure communications in satellite internet into two categories: cryptography‐based and physical layer security‐based. The former includes classical encryption‐based and quantum encryption‐based secure communication, and the latter is further divided into precoding‐based, cooperative jamming‐based, relay selection‐based, and physical‐layer authentication‐based secure communication depending on the applied techniques. Finally, we provide some future research directions.

While the millimeter-wave (mmWave) communication is less susceptible against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an attacker can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.

This paper focuses on the multiple-input-multiple-output (MIMO) two-way relay system with an untrusted relay and investigates its secure and energy-efficient precoding design issue based on the physical layer security technology. We first provide theoretical modeling for the index of secrecy energy efficiency (SEE) and formulate the optimal precoding design for SEE maximization (SEEM) as a high-dimensional non-convex programming problem. By exploring the techniques like fractional programming, alternate optimization and semi-definite programming, we then develop a hierarchical theoretical framework to solve the SEEM problem and thus to identify the optimal precoding designs for the source and relay. Furthermore, we demonstrate the proposed theoretical framework is also applicable to the problem of precoding design for secrecy sum rate maximization. Finally, with the help of generalized singular value decomposition, we propose a sub-optimal relay precoding design scheme with significantly lower computational complexity. Extensive numerical results provided in the paper indicate that the proposed schemes can remarkably improve the SEE performance in MIMO two-way untrusted relay systems.

This paper focuses on a full-duplex multiple-input multiple-output two-way untrusted relay system, and investigates the optimal beamforming design of such system to maximize its secrecy sum rate (SSR) based on the physical layer security technology. We first provide the modeling of SSR under a general beamforming setting as well as the theoretical formulation of the optimal beamforming design problem. Based on the ideal assumption that the full channel state information is available, we then develop a novel theoretical framework to solve the optimal design problem and thus establish an upper bound on SSR, where the techniques of alternate optimization, fractional programming, semi-definite programming and barrier function method are jointly employed. With the consideration of the constraints in practical implementations, we further propose two sub-optimal beamforming design solutions based on either Wiener prediction or asymptotic approximation. The issue of how to reduce the computational complexity in the optimal relay beamforming design is also discussed in this paper. Finally, we present extensive numerical results to illustrate our theoretical findings and to demonstrate the performance of the proposed sub-optimal beamforming design solutions.

In this paper, we investigate energy-efficient secure communications in an untrusted two-way relay network, where two source nodes exchange messages via an untrusted relay. Considering both security threats and energy limitation, the performance metric secure energy efficiency is defined as the ratio of the secrecy sum rate to the total power consumption. Our objective is to maximize the secure energy efficiency by jointly designing the source and relay beamformers. We first derive the expression of secure energy efficiency under a general beamforming configuration. Then, an iterative algorithm based on the techniques of alternate optimization, fractional programming, barrier function method and semi-definite programming, is proposed to find the optimal solutions of the source and relay beamformers. Our analysis shows that the proposed scheme can not only solve the problem of secure energy efficiency maximization but also be applicable for solving the problem of secrecy sum rate maximization. Simulation results demonstrate that the proposed scheme achieves a good gain in terms of secure energy efficiency.