Recently, unmanned aerial vehicles (UAVs) are crucial in the low-altitude economy due to their high mobility and operational efficiency, which enable rapid and flexible operations in various applications. To support the flight control and management of low-altitude UAVs, ground base stations need to ensure communication quality that is high-capacity, ultra-low latency, and highly reliable. Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, capable of forming high-gain directional beams, are highly suited for supporting the communication requirements of UAVs in the low-altitude economy. However, the communication signal may quickly deviate from the main lobe of the beam due to the mobility of UAVs, resulting in beam misalignment and significant degradation in communication quality. To address this problem, this paper first analyzes the near-field beam pattern of XL-MIMO system serving low-altitude UAVs and then decomposes phase of beam pattern into linear and nonlinear components to derive the angular and distance half-power beamwidth in the near field. Then, the prediction error is used to determine the number of activated antennas for dynamic beamwidth adjustment based on UAV position prediction. In addition, a near-field adaptive beamwidth control-aided tracking (NF-ABCT) algorithm is proposed to improve the effectiveness of beam tracking. Finally, simulation results demonstrate that the proposed NF-ABCT achieves a higher signal-to-noise ratio compared to the existing fixed beam scheme and channel estimation methods, while maintaining beam traking robustness and stability in both near-field and far-field.

5G-Advanced (5G-A) has enabled the vibrant development of low altitude integrated sensing and communication (LA-ISAC) networks. As a core component of these networks, unmanned aerial vehicles (UAVs) have witnessed rapid proliferation in recent years. However, due to the lag in traditional industry regulatory norms, unauthorized flight incidents occur frequently, posing a severe security threat to LA-ISAC networks. To surveil the non-cooperative UAVs, in this paper, we propose a multi-domain supervised contrastive learning (MD-SupContrast) framework for UAV radio frequency (RF) open-set recognition. Specifically, first, the texture features and the time-frequency position features from the ResNet and the TransformerEncoder (TE) are fused, and then the supervised contrastive learning is applied to optimize the feature representation of the closed-set samples. Next, to surveil the invasive UAVs that appear in real life, we propose an improved generative OpenMax (IG-OpenMax) algorithm and construct an open-set recognition model, namely Open-RFNet. According to the unknown samples, we first freeze the feature extraction layers and then only retrain the classification layer, which achieves excellent recognition performance both in closed-set and open-set recognitions. We analyze the computational complexity of the proposed model. Experiments are conducted with a large-scale UAV open dataset. The results show that the proposed Open-RFNet outperforms the existing benchmark methods in terms of recognition accuracy between the known and the unknown UAVs, as it achieves 95.12% in closed-set and 96.08% in open-set under 25 UAV types, respectively.

Palm vein recognition has gained traction in secure authentication due to its unique, stable, and inherently concealed biometric characteristics. However, the electromagnetic (EM) emissions from subcutaneous vein imaging sensors (SVIS) may unintentionally leak sensitive biometric information, fundamentally challenging the assumed security guarantees. In this paper, we propose VeinPhantom, a novel side-channel attack that reconstructs palm vein patterns from unintended EM emissions of SVIS, ultimately enabling spoofing attacks against biometric authentication systems. To overcome the challenge of low information entropy caused by weak EM signals and complex environmental interference, VeinPhantom first analyzes palm vein information from EM signals, then employs a cascaded enhancement strategy and incorporates a Dynamic Guidance Diffusion framework to progressively reconstruct high-fidelity palm vein patterns. Extensive experiments demonstrate that VeinPhantom achieves an average structure similarity index measure (SSIM) of 0.56 on commercial devices, along with a 56.96% spoofing success rate against state-of-the-art authentication systems. We further discuss potential mitigation strategies to defend against the attack.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

The radio frequency fingerprint identification (RFFI) is promising which exploits the hardware defects inherent to realize the zero-trust Internet of Things (IoT) security. Considering the scalability, the data imbalance and the training overhead for the current deep learning (DL) based RFFI, in this paper, we combine the large language model (LLM) and propose a BERT-LightRFFI framework, to enhance the zero-trust edge IoT security. Specifically, we pre-train a BERT model with the unlabeled data via self-supervised learning and obtain a powerful RFF feature extractor. Then, we use the knowledge distillation to inherit the BERT learn-gene to the lightweight BERT-Light model, and then fine-tune the BERT-Light model and a classifier with a few-shot labeled wireless data. In the experiments, we use a large-scale real-world LoRa dataset to evaluate the performances of the proposed framework and propose the interesting insights. The results prove the effectiveness of the proposed framework, achieving an accuracy of 97.52% with less model parameters and computation amount in the presence of multipath fading and Doppler effect, which is better than the benchmark methods.

The emergence of integrated positioning, communication, and computing (IPC2) technology has paved the way for advanced capabilities in physical-digital spatial positioning, intelligent communication, and computing. This article delves into an in-depth exploration of a federated learning-assisted multidimensionality fusion IPC2 system. Within this system, edge nodes collaboratively harness their locally distributed multidimensionality positioning and communication data to coordinate edge computing resources for model training. Throughout the process of fully distributed collaborative training, we focus on addressing two specific security concerns: 1) data tampering and 2) model tampering attacks. In pursuit of bolstering the system’s resilience against potential attacks, we introduce a novel federated-blockchain edge learning (FLBC) framework. This framework capitalizes on the inherent features of the blockchain, namely, its nontampering and traceability attributes. In addition, we present a meticulously designed verification algorithm tailored for the parameters aggregation process. Specifically, an aggregation algorithm is developed to enhance the efficiency and accuracy of the training model’s fitting. To assess the effectiveness of our proposed approach, comprehensive simulations are conducted using an openly accessible wireless artificial intelligence (AI) data set. The outcomes of these simulations clearly demonstrate that the proposed scheme adeptly combats data tampering attacks initiated by multiple malicious nodes and high-intensity model tampering attacks, all while maintaining minimal accuracy loss.

We develop a new integrated communications and security (ICAS) design paradigm by leveraging the concept of reconfigurable intelligent surfaces (RISs). In particular, we propose RIS-assisted simultaneous transmission and secret key generation by sharing the RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of the RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, to obtain the optimal RIS configuration, we formulate the problem as a secure transmission (ST) game and prove the existence of the Nash equilibrium (NE), and then derive the NE point of the static game. For the dynamic ST game, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to obtain the NE point. Particularly, considering that the legitimate transceivers cannot obtain the channel state information (CSI) of the attacker in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy to learn the optimal RIS configuration. The details of the algorithm are provided, and then, the system complexity is analyzed. Our simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves “one time pad” communication by allocating a suitable weight factor for data transmission and PLKG.

As one of the sensing tasks for integrated sensing and communications (ISAC), location distinction based specific emitter identification (SEI) plays an important role in location based services. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted SEI system, in which the legitimate emitter installs an RIS to customize the wireless link signature by controlling the ON-OFF state of RIS. Specifically, we consider the worst-case that the legitimate and a suspicious emitter are in the same spatial location. The received signal strength (RSS) of the specific emitter is adopted to analyze the feasibility of the proposed system. Then, we derive the statistical properties of this wireless link signature, and find the interesting insights about the phase-shift matrix configuration and the signal-to-noise-rate (SNR) gain, which showcase the huge potential of the proposed system on the integrated communications and security (ICAS) design in the near future. Afterwards, we derive the optimal detection threshold in the context of the presented metrics. Next, considering the acquisition difficulty of the RSS samples of the suspicious emitter, we use a one-class support vector machine (OC-SVM) to identify the specific emitter. Finally, the actual feasibility of the proposed system is verified via proof-of-concept experiments. The experiment results show that there are 76% and 99% performance improvements for the test statistic based and the OC-SVM based RIS-assisted SEI, respectively.

Integrated sensing, computing and communications (ISC2) is a promising technology to enable both physical-digital spatial sensing, intelligent communication and computing. This paper studies a federated learning-assisted ISC2system, in which edge nodes coordinate edge computing resource for model training based on their local integrated sensing and communications (ISAC) data. In the process of a completely distributed collaborative training, sharing and transmission of local parameters may lead to a serious Byzantine attack. To improve the system's anti-attack capability, we design a blockchain-federated edge learning framework, which utilizes the non-tampering and traceability features of the blockchain, and design a verification algorithm for federated aggregation. Particularly, an aggregation algorithm is designed to improve the fitting efficiency and accuracy of our model. Experiments based on the measured ISAC data show that the proposed scheme can effectively resist up to 30% of data tampering and up to 30% of model tampering attacks.

Recently, unmanned aerial vehicle (UAV) swarm communication has drawn much attention in search and rescue (SAR) missions owing to its wide wireless coverage and increasing autonomy in navigation. In this article, we consider maximizing the downlink wireless coverage of a UAV swarm in an unknown mission area by controlling the quasistationary deployments of UAVs. Particularly, the stochastic wireless link failures caused by channel fading and noise in UAV-to-UAV communication links are considered in coverage control. Specifically, due to delay sensitivity and onboard energy limitation of UAV-enabled SAR networks, we study a distributed control strategy where swarm UAVs can address the coverage problem by exchanging only the local information. In this case, the wireless coverage problem is divided into several distributed optimization subproblems. However, due to the integer variable and nonlinear constraints, each subproblem is nonconvex and mutually coupling, which makes it difficult to solve via standard convex optimization solvers. Thus, we model the UAV swarm network as an undirected random graph and then solve the optimization subproblems by formulating a UAV swarm wireless coverage game. As per the designed utility function and potential function of the formulated game, existence of the pure Nash equilibrium is discussed and a distributed algorithm is developed to achieve the best Nash equilibrium. We analyze the convergence property and computational complexity of the proposed algorithm. Meanwhile, we analyze effects of the initial learning rate and step size on algorithm performance from both theoretical and simulation results. Simulation results show that the proposed algorithm improves the coverage by around 58% when compared with initial performance.

We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack. In this paper, we propose a novel anti-intelligent UAV jamming strategy, in which the ground users can learn the optimal trajectory to elude such jamming. The problem is formulated as a stackelberg dynamic game, where the UAV jammer acts as a leader and the ground users act as followers. First, as the UAV jammer is only aware of the incomplete channel state information (CSI) of the ground users, for the first attempt, we model such leader sub-game as a partially observable Markov decision process (POMDP). Then, we obtain the optimal jamming trajectory via the developed deep recurrent Q-networks (DRQN) in the three-dimension space. Next, for the followers sub-game, we use the Markov decision process (MDP) to model it. Then we obtain the optimal communication trajectory via the developed deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium and derive the closed-form expression for the stackelberg equilibrium in a special case. Moreover, some insightful remarks are obtained and the time complexity of the proposed defense strategy is analyzed. The simulations show that the proposed defense strategy outperforms the benchmark strategies.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.

In the channel training phase, the attacker launches a pilot contamination attack by sending a synchronized and identical pilot signal with the legitimate transmitter. Such an attack can contaminate the channel estimation and alter the legitimate beamformer design. In this letter, first, we propose a pilot contamination attack detection scheme for wireless communications. By considering the prior uncertainty of the attack, we find that the decision-maker will conservatively decide the state of the attack, which is a subjective choice. In this case, we derive the subjective detection probability, the subjective false alarm probability, and the threshold. We analyze the tradeoff problem between ergodic wiretap channel rate and subjective detection probability. Next, based on the worst case that the attacker adopts the optimal power allocation to launch the optimal attack, we discuss the defense strategy of the optimal attack. Simulations show that the proposed scheme has a better performance than the benchmark method.

Physical-layer security has drawn ever-increasing attention in the next generation wireless communications. In this paper, we focus on studying the secure communication in an HPN-to-devices (HTD) network, in which a new type of MAC spoofing attack is considered. To detect the malicious attack, we propose a novel algorithm, namely, eigenvalue test using random matrix theory (ETRMT) algorithm, which needs no prior information about the channel. In particular, when the number of samples is finite at the receiver or the number of devices is large, the sampled signal is the biased estimation of the actual signal, which inspires us to use the random matrix theory to analyze the spoofing attack detection. The closed-form expressions of the detection probability, the false alarm probability, and the Neyman-Pearson threshold are derived based on eigenvalue distribution of the spiked population model. In addition, taking the channel time-varying into consideration, we provide an adaptive threshold tracking method by using Bayesian forecasting. Finally, the simulations are conducted to validate our proposed method and some insightful conclusions are obtained.

Random numbers are widely used in 5G communication security. In this paper, we propose a wireless physical layer (PHY-layer) characteristics based random number generator in vehicular networks. Firstly, the closed form expression of random transmission success probability is derived under the presence of multiple jamming attackers in a Nakagami-m fading channel. Secondly, a novel Random Transmission Success Probability based Physical Random Number Generator (RTSP-PhRNG) is presented. Finally, numerical results are conducted and a Universal Software Radio Peripheral (USRP) based prototype is implemented to validate our proposed method. Furthermore, the standard randomness test suite from NIST shows that our proposed PhRNG reveals good randomness.