Combination of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) is a win-win strategy which can significantly enhance the coverage performance. However, eavesdroppers may enjoy similar performance gains as the legitimate users. To solve this problem, an artificial noise (AN) assisted secure communication strategy is proposed to maximize the secrecy rate. An alternating optimization (AO) based iterative algorithm leveraging the classical successive convex approximation (SCA) and the semidefinite relaxation (SDR) techniques is proposed to derive the optimal AN model and the RIS parameters. It is found that the proposed algorithm provides better secrecy performance with less AN power compared with the benchmark schemes. More RIS elements help reducing the AN power, while this effect shrinks when the number of RIS elements is sufficiently large. Increasing the number of transmit antennas reduces the AN power if the eavesdropper is quite close to the transmitter, while improves it when the eavesdropper is far away.

In this paper, we consider the secure transmission problem in an unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) assisted mmWave networks in the presence of an eavesdropper. The UAV base station (UAV-BS) and IRS are deployed to overcome the blockages. Artificial noise (AN) is exploited against the eavesdropper. Aiming to maximize the secrecy rate, we jointly design the positions and beamforming of UAV-BS and IRS, where the positions here represent the deployed position of the UAV-BS and the activation position of the IRS. Meanwhile, the maximum transmits power, minimum height constraint for UAV-BS, and the legitimate receiver minimum rate constraint are required. To obtain the optimal UAV-BS position, the elevation angle-dependent probabilistic LoS air-to-ground channel is exploited in this network. To tackle the formulated non-convex problem, we divide it into two subproblems: (1) design UAV-BS and IRS positions; (2) design UAV-BS and IRS beamforming. Moreover, to reduce the complexity, we propose an ideal beamforming model to find the near-optimal positions of UAV-BS and IRS. Then, semidefinite relaxation (SDR) is utilized to cope with the highly coupled and high-dimensional variable vectors. Finally, we obtain a sub-optimal solution by an efficient and low-complexity alternating optimization algorithm. Numerical results demonstrate the improvement of secrecy rate by jointly designing the positions as well as the significant performance gains achieved by our proposed schemes over benchmark schemes.

Unmanned aerial vehicles (UAVs) are anticipated to be a potential data collection solution for wireless sensor networks (WSNs). The main challenges of integrating UAVs in WSNs are security threats and UAV’s onboard energy limitation. To cope with these two challenges, this article examines the secrecy energy efficiency (SEE) maximization problem in UAV-enabled WSN. Specifically, a full-duplex (FD) UAV gathers confidential information from ground sensor nodes (SNs) in the uplink while sending jamming signals to confound a ground eavesdropper (Eve) in the downlink. Considering a passive eavesdropping scenario lacking Eve’s instantaneous channel state information (CSI), the resulting problem is subject to the constraints of connection outage probability (COP), secrecy outage probability (SOP), securely collected bits, and flight trajectory. To tackle the intractable nonconvex problem, we first derive the optimal codeword rate and redundancy rate in closed-form expressions and then develop a low-complexity algorithm using the block coordinate descent (BCD) approach to alternatively optimize the SN scheduling, SN transmit power, UAV transmit power, and UAV trajectory. Simulation results verify the performance gains of the proposed scheme compared with the benchmark schemes. In particular, the proposed scheme achieves nearly the same secrecy rate gains at a lower UAV’s energy consumption cost than the sum secrecy rate maximization (SSRM) baseline. Moreover, it is revealed that trajectory optimization of the proposed scheme plays a crucial role in improving SEE performance compared with the circle trajectory (CT) baseline.

Physical (PHY) layer authentication has been a significant trend towards ensuring the identity security of terminals in wireless networks due to the high security and low complexity. However, the independence assumption of existing literature ignores the inherent correlation of the PHY-layer attributes, which limits its generality. In this paper, we propose a multi-attribute-based PHY-layer authentication scheme by taking the correlation into account. To cope with the exponential growth of computational complexity in correlated analysis, this paper studies the reconstruction and heuristic algorithm to find a suboptimal authentication solution with low complexity. Specific to the inherent volatility nature of the PHY-layer attributes, we propose an unsupervised machine learning (ML) based non-parametric clustering algorithm to enhance the reliability of PHY-layer authentication. Unlike existing PHY-layer authentication schemes based on ML, the proposed PHY-layer authentication scheme does not require any prior information or the training set, which has a more potent universality. Extensive simulations are performed under both synthetic and real data sets, and the figures verify that the proposed authentication scheme can achieve a reliable and robust performance with low complexity.

A novel covert communication framework is proposed to enhance the privacy of the unmanned aerial vehicle (UAV)-enabled relaying systems. Specifically, the full-duplex (FD) UAV covertly relays data from each ground sensor to remote base station (BS) against a warden (Willie). Considering a noise uncertainty model, the minimum detection error probability (DEP) of Willie is first derived. Under the constraint of the derived DEP, the minimum average covert transmission rate (ACTR) is then maximized by jointly designing the sensor association, the UAV transmit power, and the UAV trajectory. A low-complexity algorithm based on the penalty successive convex approximation (P-SCA) is further invoked to achieve the locally optimal solution. Finally, numerical results show the effectiveness of the proposed framework and reveal some insights, i.e., increasing uncertainty at Willie can improve the covertness performance.

In this letter, a novel aerial reconfigurable intelligent surface (ARIS) assisted covert communication framework is proposed, where RIS is mounted on unmanned aerial vehicle (UAV) to enhance the covert performance of ground transceivers in the presence of a warden. The detection performance of warden is first analyzed, and then a covert energy efficiency (CEE) maiximization problem is formulated by jointly optimizing power control, passive beamforming, and UAV trajectory. To solve the non-convex problem, an algorithm based on alternating optimization is devised to achieve the suboptimal solution. Numerical evaluations show that the proposed scheme has a larger CEE gain than the scheme without RIS under the condition of more reflecting elements. Moreover, the proposed scheme can achieve near-same covertness performance at a lower energy consumption cost compared with covert transmission rate maximization scheme.