We investigate the secrecy transmission of uplink non-orthogonal multiple access (NOMA) with the aid of energy harvesting (EH) jammers. During each time frame, communication is divided into two phases. At the first phase, the base station (BS) transfers wireless power to EH receivers (EHRs). At the second phase, users perform uplink NOMA transmission to BS, while one of EHRs is selected as a friendly jammer that uses the energy harvested from the previous phase to emit the artificial noise for confusing the eavesdropper. In terms of the requirement of channel state information (CSI), we propose three friendly EH jammer selection schemes, namely random EH jammer selection (REJS) scheme without the requirement of any CSI, maximal EH jammer selection (MEJS) scheme with the CSI between BS and each EHR, and optimal EH jammer selection (OEJS) scheme where both the CSIs from BS to EHRs and from EHRs to the eavesdropper need to be known. Analytical closed-form expressions for the connection outage probability (COP), secrecy outage probability (SOP) and effective secrecy throughput (EST) are derived to evaluate the system performance achieved by the proposed schemes, respectively. Also, the asymptotic analysis is provided to gain further insights. The analytical and numerical results indicate that the proposed schemes can realize better secrecy performance than conventional scheme without an EH jammer. Both the secrecy diversity orders of the REJS and MEJS schemes are one while the OEJS scheme can achieve a full secrecy diversity order. Furthermore, owing to the impact of connection outage, the three schemes converge to the same EST floor with the increase of signal-to-noise ratio (SNR).

Reconfigurable intelligent surface (RIS) enables performance enhancement of communication networks due to its significant reflective gain, which could also be used to strengthen secure transmission. In this paper, we consider a RIS enabled unmanned aerial vehicle (UAV) communication network, in which the UAV serves as a base station for a terrestrial legitimate user accompanied by a terrestrial eavesdropper. Specifically, security performance of the considered network is enhanced under both instantaneous and statistical eavesdropper channel state information (CSI). To maximize the average secrecy rate and minimize the secrecy outage duration, the trajectory and beamformer of UAV and the phase shift matrix of RIS are jointly optimized. For the instantaneous CSI scenario, we propose a successive convex approximation (SCA) based algorithm as a baseline solution, where the collective optimization problem is decomposed into three subproblems and solved separately. After that, benefiting from the state-of-the-art proximal policy optimization (PPO) algorithm, a deep reinforcement learning (DRL) based algorithm is developed to solve the average secrecy rate maximization problem from a holistic perspective. For the statistical CSI case, the close-form expression of secrecy outage probability (SOP) is derived to expound the secrecy outage duration. Since the secrecy outage duration minimization problem is hard to be solved via the SCA based algorithm, the developed DRL based algorithm is utilized again. Simulation results demonstrate that compared with benchmark algorithms, both the security performance and complexity of the proposed DRL based algorithm are competitive.

In view of the scarce spectrum resources and inconvenient deployment in wireless communications, this work focuses on an unmanned aerial vehicle (UAV) enabled non-orthogonal multiple access (NOMA) system where a UAV deployed as a base station serves two users by NOMA. A joint power allocation and aerial jamming (PAAJ) scheme is proposed to achieve reliable and secure communications for the system in the presence of a malicious eavesdropper. To be specific, dynamic power allocation is adopted to ensure the reliability of the system and another friendly UAV jammer is introduced to improve the security of the system. The closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) are derived to evaluate the performance of the security-required user in the practical condition of probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) air-to-ground (A2G) channels. Moreover, the asymptotic EST is analyzed to obtain further insights. Numerical results show the validity of theoretical derivations and the superiority of the proposed PAAJ scheme over benchmark schemes. The EST of the UAV-enabled NOMA communication system outperforms the conventional terrestrial NOMA system at the low transmitting power from the UAV base station. There exists an optimal height of the UAV base station to maximize the EST, which provides a useful framework for designing UAV-enabled communication systems with heterogeneous service requirements.

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security.

Beamforming is a promising technique to enhance the security of wireless transmission, while the optimal beamforming design with partial channel state informing (CSI) is challenging. This letter develops a three-dimensional (3D) robust beamforming method for unmanned aerial vehicle (UAV) communication systems in the physical layer security perspective. Specifically, aiming at maximizing the average secrecy rate of the considered system, a precisely designed neural network is trained to optimize the beamformer for confidential signal and artificial noise (AN), with partial CSIs of legitimate UAV and eavesdropping UAV. Simulation experiments show that the proposed deep learning (DL) based method could achieve better secrecy rate and flexible beam steering than benchmarks.

This paper investigates the green and secure communications of multi-UAV NOMA systems. A joint energy transfer and artificial noise (ETAN) scheme is proposed to enhance the endurance and secrecy performance of the system. In particular, communication can be divided into two phases during each time frame. In the first phase, the multi-UAV system harvests energy from a power beacon. In the second phase, UAVs perform NOMA to transmit confidential messages uplink to a base station in the presence of eavesdropping UAVs. Simultaneously, to safeguard the multi-UAV system, the power beacon transmits artificial noise to degrade the eavesdropping channel. The closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP) and effective secrecy throughput (EST) of the system are derived to evaluate the system performance. The asymptotic analysis of SOP and EST is also performed for further insights. Numerical results are presented to validate the correctness of theoretical derivations and the superiority of the proposed scheme. Analysis and simulation results demonstrate that the secrecy diversity order of the proposed scheme is determined by the jamming channel. When the number of UAVs increases, the time of energy harvesting should be appropriately extended. Moreover, substantial improvement to the system can be achieved by UAV pairing.