Search Results (334)

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

The evolution of sixth-generation (6G) networks enables transformative edge Artificial Intelligence of Things (AIoT) applications but introduces critical security vulnerabilities during model transmission between the central server and edge devices (e.g., unmanned aerial vehicles). Traditional approaches fail to jointly optimize model accuracy and physical layer security against eavesdropping. To address this gap, we propose a novel dynamic user selection framework that integrates three key innovations: (1) closed-form secrecy outage probability derivation for Rayleigh fading channels, (2) a Secure Model Accuracy (SMA) metric unifying recognition accuracy and secrecy outage probability, and (3) an alternating optimization algorithm for joint model–bandwidth selection under secrecy constraints. Comprehensive simulations demonstrate 22% SMA gains over baselines across diverse channel conditions and eavesdropper capabilities, resolving the fundamental accuracy–security tradeoff for trustworthy edge intelligence. 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

Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable ($\alpha$-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the $\alpha$-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached 10⁻³ confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs. Full article

Private array equality comparison (PAEC) aims to evaluate whether two arrays are equal while maintaining the confidentiality of their elements. Current private comparison protocols predominantly focus on determining the relationships of secret integers, lacking exploration of array comparisons. To address this issue, we propose a swap test-based quantum protocol for PAEC, which satisfies both functionality and security requirements using the principles of quantum mechanics. This protocol introduces a semi-honest third party (TP) that acts as a medium for generating Bell states as quantum resources and distributes the first and second qubits of these Bell states to the respective participants. They encode their array elements into the received qubits by performing rotation operations. These encoded qubits are sent to TP to derive the comparison results. To verify the feasibility of the proposed protocol, we construct a quantum circuit and conduct simulations on the IBM quantum platform. Security analysis further indicates that our protocol is resistant to various quantum attacks from outsider eavesdroppers and attempts by curious participants. 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 paper addresses the optimization problem for mobile jamming assistance schemes in cognitive covert communication (CR-CC), where cognitive users adopt the underlying mode for spectrum access, while an unmanned aerial vehicle (UAV) transmits the same-frequency noise signals to interfere with eavesdroppers. Leveraging the inherent dynamic game-theoretic characteristics of covert communication (CC) systems, we propose a novel covert communication optimization algorithm based on generative adversarial networks (GAN-CCs) to achieve system-wide optimization under the constraint of maximum detection error probability. In GAN-CC, the generator simulates legitimate users to generate UAV interference assistance schemes, while the discriminator simulates the optimal signal detection of eavesdroppers. Through the alternating iterative optimization of these two components, the dynamic game process in CC is simulated, ultimately achieving the Nash equilibrium. The numerical results show that, compared with the commonly used multi-objective optimization algorithm or nonlinear programming algorithm at present, this algorithm exhibits faster and more stable convergence, enabling the derivation of optimal mobile interference assistance schemes for cognitive CC systems. Full article

A homodyne detector, which is also a common element in current telecommunication, is a core component of continuous-variable quantum key distribution (CV-QKD) since it is considered the simplest setup for the distinguishing of coherent states with minimum error. However, the theoretical security of CV-QKD is based on the assumption that the responses of the homodyne detector are always linear with respect to the input, which is impossible in practice. In the real world, a homodyne detector has a finite linear domain, so the linearity assumption is broken when the input is too large. Regarding this security vulnerability, the eavesdropper Eve can perform the so-called homodyne detector-blinding attack by saturating the homodyne detector and then stealing key information without being detected by the legitimate users. In this paper, we propose a countermeasure for the homodyne detector-blinding attack by using an adjustable optical attenuator with a feedback structure. Specifically, we estimate the suitable attenuation value in the data processing of CV-QKD and feed it back to the adjustable optical attenuator before the detector in real time. Numerical simulation shows that the proposed countermeasure can effectively defend against homodyne detector-blinding attacks and ensure the security of the Gaussian-modulated coherent state protocol with finite-size effect. Full article

We consider the scenario in which the source sends status updates, or packets, to the receiver through an infinite capacity transmitter, where the transmitted packets are subject to potential illegal eavesdropping. Time is discretized into identical time slots. In recent years, the age of information (AoI) metric, which was defined as the time has elapsed since the generation instant of the latest received packet, has been widely applied to characterize the freshness of obtained packets. Due to the presence of eavesdroppers, some packets may be eavesdropped during their transmissions, causing information leakages. To assess an infinite-capacity system’s performance of securely transmitting status updates, in this paper, we define an AoI-related metric called the freshness advantage of the legitimate receiver, F, to be average instantaneous gap between eavesdropper’s and legitimate receiver’s AoI. For arbitrarily distributed packet interarrival times, and assuming that in each time slot with probabilities ${\gamma }_d$, ${\gamma }_E$, the transmitted packet is received by the legitimate receiver and the eavesdropper, we derive the explicit formula of F. The concise expression shows that F is fully determined by the average interarrival time and the ratio of ${\gamma }_d$ to ${\gamma }_E$. For special cases where the interarrival time follows geometric distributions, we first determine the explicit distribution of instantaneous AoI gap. Then, given ${\gamma }_d$ and ${\gamma }_E$, we derive the optimal packet generation rate $p^{\ast }$ that minimizes the combined performance Q, which is constructed as the average AoI minus the freshness advantage F. When imposing timeliness and security constraints at the same time, the feasible regions of p and ${\gamma }_d$ such that both two required performances can be satisfied are depicted and discussed. Finally, we investigate the impacts of different parameters on F and show the tradeoffs between timeliness performance and security performance through numerical simulations. 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

Covert wireless communication is designed to securely transmit hidden information between two devices. Its primary objective is to conceal the existence of transmitted data, rendering communication signals difficult for unauthorized parties to detect, intercept, or decipher during transmission. In this paper, we propose a Noise-like Multi-Carrier Random Phase Communication System (NRPCS) to enhance covert wireless communication by significantly complicating the detection and interception of transmitted signals. The proposed system utilizes bipolar modulation and Cyclic Code Shift Keying (CCSK) modulation, complemented by a random sequence generation mechanism, to increase the randomness and complexity of the transmitted signals. A mathematical model of the NRPCS waveform is formulated, and detailed analyses of the system’s time-domain basis functions, correlation properties, and power spectral characteristics are conducted to substantiate its noise-like behavior. Simulation results indicate that, compared to traditional fixed-frequency transmission methods, NRPCS substantially improves both the Low Probability of Detection (LPD) and the Low Probability of Interception (LPI). Further research results demonstrate that unauthorized eavesdroppers are unable to effectively demodulate signals without knowledge of the employed modulation scheme, thus significantly enhancing the overall security of communication. Full article

We compare schemes for testing whether two parties share a two-qubit singlet state. The first, standard, scheme tests Braunstein–Caves (or CHSH) inequalities, comparing the correlations of local measurements drawn from a fixed finite set against the quantum predictions for a singlet. The second, alternative, scheme tests the correlations of local measurements, drawn randomly from the set of those that are $\theta$-separated on the Bloch sphere, against the quantum predictions. We formulate each scheme as a hypothesis test and then evaluate the test power in a number of adversarial scenarios involving an eavesdropper altering or replacing the singlet qubits. We find the ‘random measurement’ test to be superior in most natural scenarios. 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

Physical-layer (PHY) security is widely used as an effective method for ensuring secure wireless communications. However, when the legitimate user (LU) and the eavesdropper (Eve) are in close proximity, the channel coupling can significantly degrade the secure performance of PHY. Frequency diverse array (FDA) technique addresses channel coupling issues by introducing frequency offsets among array elements. However, FDA’s ability to secure communication relies mainly on frequency domain characteristics, lacking the spatial degrees of freedom. The recently proposed movable antenna (MA) technology serves as an effective approach to overcome this limitation. It offers the flexibility to adjust antenna positions dynamically, thereby further decoupling the channels between LU and Eve. In this paper, we propose a novel MA-FDA approach, which offers a comprehensive solution for enhancing PHY security. We aim to maximize the achievable secrecy rate through the joint optimization of all antenna positions at the base station (BS), FDA frequency offsets, and beamformer, subject to the predefined regions for antenna positions, frequency offsets range, and energy constraints. To solve this non-convex optimization problem, which involves highly coupled variables, the alternating optimization (AO) method is employed to cyclically update the parameters, with the projected gradient ascent (PGA) method and block successive upper-bound minimization (BSUM) method being employed to tackle the challenging subproblems. Simulation results demonstrate that the MA-FDA approach can achieve a higher secrecy rate compared to the conventional phased array (PA) or fixed-position antenna (FPA) schemes. Full article