This paper investigates a novel anti-eavesdropping strategy based on unmanned aerial vehicle (UAV)-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, a UAV equipped with a STAR-RIS acts as a passive relay to reflect desired signals and simultaneously acts as a friendly jammer to transmit artificial noise (AN) against eavesdroppers. Based on the phase shift coupling characteristics of STAR-RIS, three phase shift configuration strategies are proposed, namely reliability-priority (RP), security-priority (SP), and element-partitioning (EP) schemes. Analytical closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), effective secrecy throughput (EST) and secrecy energy efficiency (SEE) are derived to evaluate the reliable and secure performance achieved by the proposed schemes, respectively. The asymptotic analysis is also performed for further insights. Analysis and simulation results demonstrate that the proposed three schemes outperform traditional benchmark schemes. From the perspective of reliability, the RP scheme can achieve the best COP. In terms of security, as the number of STAR-RIS elements increases, the SOPs of the SP and EP exponentially decrease, whereas the SOP of the RP scheme increases. The EP scheme achieves the optimal EST, and the asymptotic EST is independent of phase estimation errors. Additionally, it is recommended that the UAV be deployed near the eavesdropper for the SP and EP schemes to enhance SEE.

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.

As a promising technique for the next generation communication network, integrated sensing and communication (ISAC) has attracted incremental research attentions due to its capabilities in spectrum sharing, cost saving, and data collecting. In this paper we utilize unmanned aerial vehicle (UAV) swarm to perform downlink ISAC transmission to serve multiple terrestrial legitimate users and sensing targets. To accommodate more practical application scenarios, we assume that there are also multiple malicious eavesdroppers in the network attempting to eavesdrop on the confidential signal. In order to enhance the security of the downlink transmission while maintaining sufficient sensing performance, we propose a joint optimization of the centralized trajectory of UAV swarm, the transmit beamforming on each UAV, and the ISAC schedule, which is eventually formulated as an average secrecy rate (ASR) maximization problem. A deep reinforcement learning (DRL) based algorithm is developed to solve the considered optimization problem and its effectiveness is validated via experimental simulations, which also proves its superiority over benchmark methods.

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).

In this study, a precoding scheme which is called singular value decomposition and null space (SVDNS) based scheme is proposed to enhance physical layer security of a multiple‐input multiple‐output multiantenna eavesdropper (MIMOME) system. Under the partial channel state information (CSI) of the eavesdropper, the precoding matrix for the information bearing signal is constructed firstly via SVDNS‐based scheme to acquire a compromise between improving performance of the main channel and impairing that of the wiretap channel. Then the optimal power allocation over subchannels formed by precoding matrix is obtained through solving the secrecy rate maximise problem to further improve secrecy performance. After that, under the scenario that CSI of the wiretap channel is less knowable, precoding matrix for artificial noise (AN) is proposed via SVDNS‐based scheme, and then the optimal power allocation between the information bearing signal and AN is obtained while optimal power allocation over subchannels is solved subsequently. Simulation results show that the SVDNS‐based scheme with optimal power allocation achieves a higher secrecy rate than the existing schemes, and the SVDNS‐based scheme with AN aiding could make up for the performance degradation of it without AN in the situation that CSI of the wiretap channel is more unknowable.

Motivation: As protein structure is more conserved than sequence during evolution, multiple structure alignment can be more informative than multiple sequence alignment, especially for distantly related proteins. With the rapid increase of the number of protein structures in the Protein Data Bank, it becomes urgent to develop efficient algorithms for multiple structure alignment. Results: A new multiple structure alignment algorithm (mTM-align) was proposed, which is an extension of the highly efficient pairwise structure alignment program TM-align. The algorithm was benchmarked on four widely used datasets, HOMSTRAD, SABmark_sup, SABmark_twi and SISY-multiple, showing that mTM-align consistently outperforms other algorithms. In addition, the comparison with the manually curated alignments in the HOMSTRAD database shows that the automated alignments built by mTM-align are in general more accurate. Therefore, mTM-align may be used as a reliable complement to construct multiple structure alignments for real-world applications. Availability and implementation: http://yanglab.nankai.edu.cn/mTM-align. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.