Search Results (117)

Ensuring physical layer security for low-altitude economic networking (LAENet) is critical due to the broadcast nature of wireless channels. In dense urban environments, multi-antenna LAENet systems are often impaired by the keyhole effect, which induces rank deficiency and poses significant security challenges. This paper investigates the secrecy performance of a multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system in LAENet with keyhole channels. Depending on the availability of channel state information (CSI) at the transmitter, three wiretap scenarios are considered: (i) broadcasting, (ii) passive eavesdropping, and (iii) spoofing. For each scenario, the optimal precoder is designed to maximize the secrecy transmission rate. Based on these designs, we derive closed-form expressions for the secrecy outage probability (SOP) and average secrecy rate (ASR). To provide insights into the effect of keyholes on secrecy diversity order and array gain under this severe rank-deficiency structure, we also obtain asymptotic expressions for SOP and ASR in the high signal-to-noise ratio (SNR) regime using the Mellin transform. Numerical results validate the analytical expressions and illustrate the influence of key parameters on secrecy performance. These findings provide meaningful guidance for the secure design of future LAENet deployments. Full article

To meet the need for global coverage, space–air–ground integrated networks (SAGINs) are crucial, but the openness of wireless links makes communications vulnerable to eavesdropping. This paper investigates the physical layer security (PLS) of uplink transmissions in a cooperative dual-hop SAGIN. The system comprises a ground source with a directional antenna, an unmanned aerial vehicle (UAV) relay cluster, and a low Earth orbit (LEO) satellite. Utilizing stochastic geometry, we model the spatial randomness of terrestrial eavesdroppers and the multi-layered dual-orbital LEO destination. To combat mixed radio-frequency (RF) and free-space optical (FSO) fading, multiple relay selection and maximum ratio combining (MRC) are integrated into the UAV cluster. We analytically derive the piecewise probability density function for the FSO link distance, obtaining exact closed-form expressions for the end-to-end secrecy outage probability (SOP). Monte Carlo simulations strictly validate the derivations. The results demonstrate that while increasing available relays and antennas enhances PLS via spatial diversity, a security bottleneck restricts the RF-FSO architecture under high-transmit power regimes, generating asymptotic secrecy floors. These findings provide explicit theoretical guidelines for the secure design and parameter optimization of future SAGINs. Full article

We propose a unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and jamming (U-ISJC) framework, in which a multifunctional UAV first detects the sensing target to obtain sensing information, and subsequently transmits the information to communication users via a unified beam in the presence of multiple eavesdroppers. To avoid functional conflicts, a time slot frame structure is designed for the UAV’s multifunctional capabilities, enabling communication, sensing, and jamming tasks within each timeslot. The time slot allocation factor dynamically adjusts based on the UAV’s flight trajectory for efficient UAV resource utilization. Additionally, to prevent security rate leakage caused by eavesdroppers, a jamming beam is added to serve both jamming and sensing functions. Our objective is to maximize the the worst-case total secure data transmission rate by jointly optimizing sub-time slot allocation, beamforming, and UAV trajectory. To address this problem, we propose a joint optimization algorithm that adopts the concave–convex procedure (CCCP) technique and semi-definite relaxation (SDR), under the block coordinate descent (BCD) framework. The simulation results show that compared with the baseline scheme, the proposed algorithm substantially improves the communication security rate while ensuring the quality of communication and sensing. Full article

Movable antennas (MAs) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently been investigated to enhance integrated sensing and communication (ISAC) systems. However, prior work has not exploited the spatial flexibility of MAs and the extended coverage of STAR-RIS to simultaneously address security issues. In this paper, a novel MA- and STAR-RIS-assisted secure ISAC system is proposed that involves multiple legitimate users and potential eavesdroppers. To ensure fairness, we formulate a minimum secrecy rate maximization problem by jointly optimizing the active beamforming covariance matrices at the base station (BS), the passive transmitting and reflecting beamforming coefficients at the STAR-RIS, and the spatial positions of the MAs. To address the highly nonconvex optimization problem, we propose an efficient iterative algorithm based on the alternating optimization (AO) framework. Specifically, we leverage semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques to solve the active and passive beamforming subproblems, and the SCA method is also applied to tackle the highly intractable MA position optimization subproblem. Numerical results demonstrate that the secure performance of the proposed MA and STAR-RIS-assisted scheme significantly outperforms that of other benchmark schemes, validating the benefits of the proposed algorithm. Full article

This study investigates the enhancement in spectral efficiency in wireless networks through the application of Non-Orthogonal Multiple Access (NOMA), particularly within the context of imminent 5G networks. Despite its recognized potential, the application of NOMA in High-Frequency (HF) radio communications for eavesdropping mitigation has not been extensively explored. This paper introduces an innovative NOMA-based strategy for securing end-to-end radio communication against unauthorized interception. Utilizing NOMA’s distinctive capability, this methodology transmits a legitimate message at lower power, overlaid with a deceptive message in the same frequency band, thus effectively camouflaging the genuine communication from eavesdroppers. In our experimental setup, scaled mixed Orthogonal Frequency-Division Multiplexing (OFDM)-modulated waveforms were transmitted over varied distances. The findings demonstrate that under specific conditions, our NOMA-based system can successfully mislead eavesdropping systems, showcasing significant potential for enhancing security and privacy in HF radio communication settings. The experiments underscore the practicality of this approach, with the results indicating a noteworthy improvement in thwarting potential eavesdropping attempts, thereby confirming the theoretical model’s applicability in real-world scenarios. Full article

This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization problem. To address the non-convex optimization challenge of this problem, we develop two innovative Q-learning-based position decision algorithms (Q-PDA and Q-PDA-lite) with a dynamic reward mechanism, allowing drones to adaptively optimize their positions. Additionally, we propose an enhanced Tabu Search-based grouping algorithm (TS-GA) to establish the suboptimal user equipment (UE)–drone association by balancing candidate solution exploration and tabu constraint exploitation. Simulation results demonstrate that the proposed Q-PDA and Q-PDA-lite achieve worst-case secrecy rates significantly exceeding those of Random-PDA and K-means-PDA. While Q-PDA-lite exhibits 2% lower performance than Q-PDA, it offers reduced complexity. Additionally, the proposed TS-GA achieves a worst-case secrecy rate that substantially outperforms random grouping, UE-channel-gain-based grouping, and channel-gain-based grouping. Collectively, the hybrid approach integrating Q-PDA and TS-GA achieves 10% near-global optimality with guaranteed convergence, while preserving computational efficiency. Furthermore, this hybrid approach outperforms other combinations in terms of security metrics. Full article

We consider combinatorial multi-access coded caching with private caches, where users are connected to two types of caches: private caches and multi-access caches. Each user has its own private cache, while multi-access caches are connected in the same way as caches are connected in a combinatorial topology. A scheme is proposed that satisfies the following three requirements simultaneously: (a) Linear Function Retrieval (LFR), (b) content security against an eavesdropper, and (c) demand privacy against a colluding set of users. It is shown that the private caches included in this work enable the proposed scheme to provide privacy against colluding users. For the same rate, our scheme requires less total memory accessed by each user and less total system memory than the existing scheme for multi-access combinatorial topology (no private caches) in the literature. We derive a cut-set lower bound and prove optimality when $r\ge C-1$. For $r<C-1$, we show a constant gap of 5 under certain conditions. Finally, the proposed scheme is extended to a more general setup where different users are connected to different numbers of multi-access caches, and multiple users are connected to the same subset of multi-access caches. Full article

The rapid growth of Internet-connected devices integrating into our everyday lives has no end in sight. As more devices and sensor networks are manufactured, security tends to be a low priority. However, the security of these devices is critical, and many current research topics are looking at the composition of simpler techniques to increase overall security in these low-power commercial devices. Transmission security (TRANSEC) methods are one option for physical-layer security and are a critical area of research with the increasing reliance on the Internet of Things (IoT); most such devices use standard low-power Time-division multiple access (TDMA) or frequency-division multiple access (FDMA) protocols susceptible to reverse engineering. This paper provides a hardware validation of previously proposed techniques for the intentional injection of noise into the phase mapping process of a spread spectrum signal used within a receiver-assigned code division multiple access (RA-CDMA) framework, which decreases an eavesdropper’s ability to directly observe the true phase and reverse engineer the associated PRNG output or key and thus the spreading sequence, even at high SNRs. This technique trades a conscious reduction in signal correlation processing for enhanced obfuscation, with a slight hardware resource utilization increase of less than 2% of Adaptive Logic Modules (ALMs), solidifying this work as a low-power technique. This paper presents the candidate method, quantifies the expected performance impact, and incorporates a hardware-based validation on field-programmable gate array (FPGA) platforms using arbitrary-phase phase-shift keying (PSK)-based spread spectrum signals. Full article

Radar jamming resource allocation is crucial for maximizing jamming effectiveness and ensuring operational superiority in complex electromagnetic environments. However, the existing approaches still sufferfrom inefficiency, instability, and suboptimal global solutions. To address these issues, this work proposes addressing effective jamming resource allocation in dynamic radar countermeasures with multiple jamming types. A deep reinforcement learning framework is designed to jointly optimize transceiver strategies for adaptive jamming under state-switching scenarios. In this framework, a hybrid policy network is proposed to coordinate beam selection and power allocation, while a dynamic fusion metric is integrated to evaluate jamming effectiveness. Then the non-convex optimization is resolved via a proximal policy optimization version 2 (PPO2)-driven iterative algorithm. Experiments demonstrate that the proposed method achieves superior convergence speed and reduced power consumption compared to baseline methods, ensuring robust jamming performance against eavesdroppers under stringent resource constraints. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for integrated sensing and communication (ISAC) due to their mobility and deployment flexibility. By adaptively adjusting their flight trajectories, UAVs can maintain favorable line-of-sight (LoS) communication links and sensing angles, thus enhancing overall system performance in dynamic and complex environments. However, ensuring physical layer security (PLS) in such UAV-assisted ISAC systems remains a significant challenge, particularly in the presence of mobile users and potential eavesdroppers. This manuscript proposes a joint optimization framework that simultaneously designs robust transmit beamforming and UAV trajectories to secure downlink communication for multiple ground users. At each time slot, the UAV predicts user positions and maximizes the secrecy sum-rate, subject to constraints on total transmit power, multi-target sensing quality, and UAV mobility. To tackle this non-convex problem, we develop an efficient optimization algorithm based on successive convex approximation (SCA) and constrained optimization by linear approximations (COBYLA). Numerical simulations validate that the proposed framework effectively enhances the secrecy performance while maintaining high-quality sensing, achieving near-optimal performance under realistic system constraints. Full article

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

This study concentrates on physical layer security (PLS) in UAV-aided Internet of Things (IoT) networks and proposes an innovative approach to enhance security by optimizing the trajectory of unmanned aerial vehicles (UAVs). In an IoT system with multiple eavesdroppers, formulating the optimal UAV trajectory poses a non-convex and non-differentiable optimization challenge. The paper utilizes the successive convex approximation (SCA) method in conjunction with hypograph theory to address this challenge. First, a set of trajectory increment variables is introduced to replace the original UAV trajectory coordinates, thereby converting the original non-convex problem into a sequence of convex subproblems. Subsequently, hypograph theory is employed to convert these non-differentiable subproblems into standard convex forms, which can be solved using the CVX toolbox. Simulation results demonstrate the UAV’s trajectory fluctuations under different parameters, affirming that trajectory optimization significantly improves PLS performance in IoT systems. Full article

This paper investigated the configuration of the reflecting elements for uplink non-orthogonal multiple access (NOMA) unmanned aerial vehicle (UAV)–reconfigurable intelligent surface (RIS) systems. By analyzing the practical air-to-ground (A2G) channels and phase estimation errors, a closed-form expression for the range of reflecting elements has been formulated to enhance the reliability and security of the system. Considering the energy efficiency of the system, the number of reflecting elements is optimized, aiming to maximize the energy secrecy efficiency (ESE) index under the given constraints. The simulation results verified the correctness of the derivation, which offers theoretical guidance for configuring RISs in uplink NOMA UAV systems with heterogeneous service demands. The uplink NOMA UAV system outperforms traditional terrestrial systems. The results also show that when the number of eavesdroppers increases, the influence of the number of reflecting elements on the system’s ESE becomes more significant. This demonstrates the benefits of equipping UAVs with RISs for the security of multiple eavesdropping systems. Full article

With the wide application of unmanned aerial vehicles (UAVs) in the military and civilian fields, the physical layer security of UAV-assisted communication has attracted more and more attention in recent years. Reconfigurable intelligent surface (RIS) is a revolutionizing and promising technology that can improve spectrum efficiency through intelligent reconfiguration of the propagation environment. In this paper, we investigate the physical layer security of RIS and UAV-assisted communication systems. Specifically, we consider the scenario of multiple eavesdroppers wiretapping the communication between the base station and the legitimate user and propose a secure mechanism that deploys the RIS on a dynamic UAV for security assistance. In order to maximize the average secrecy rate of the system, we propose a joint optimization problem of joint UAV flight trajectory, RIS transmit phase shift, and base station transmit power. Since the proposed problem is non-convex, it is difficult to solve it directly, so we propose a joint optimization algorithm based on block coordinate descent and successive convex optimization techniques. Simulation results verify the effectiveness of our proposed design in improving the secrecy performance of the considered system. Full article

This paper investigates a multi-antenna, multi-input multi-output (MIMO) dual-functional radar and communication (DFRC) system platform. The system simultaneously detects radar targets and communicates with downlink cellular users. However, the modulated information within the transmitted waveforms may be susceptible to eavesdropping. To ensure the security of information transmission, we introduce non-orthogonal multiple access (NOMA) technology to enhance the security performance of the MIMO-DFRC platform. Initially, we consider a scenario where the channel state information (CSI) of the radar target (eavesdropper) is perfectly known. Using fractional programming (FP) and semidefinite relaxation (SDR) techniques, we maximize the system’s total secrecy rate under the requirements for radar detection performance, communication rate, and system energy, thereby ensuring the security of the system. In the case where the CSI of the radar target (eavesdropper) is unavailable, we propose a robust secure beamforming optimization model. The channel model is represented as a bounded uncertainty set, and by jointly applying first-order Taylor expansion and the S-procedure, we transform the original problem into a tractable one characterized by linear matrix inequalities (LMIs). Numerical results validate the effectiveness and robustness of the proposed approach. Full article