This article investigates the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted secure transmissions in wireless powered communication (WPC) systems. According to the conditions of communication links, three scenarios are considered. Correspondingly, two transmission schemes are proposed for outdoor and indoor users to enhance the reliability and security of the communication system in each scenario. To be specific, for the scenario-I with blocked energy harvesting links, the scenario-I based information transmission of outdoor-user and cooperative jamming of indoor-user (IbTOJI) scheme, and cooperative jamming of outdoor-user and information transmission of indoor-user (IbJOTI) scheme are proposed, respectively. For the scenario-II with blocked information transmission (IT) links, scenario-II-based IT of outdoor-user and cooperative jamming of indoor-user (IIbTOJI) scheme, and cooperative jamming of outdoor-user and IT of indoor-user (IIbJOTI) scheme are proposed, respectively. For the scenario-III which deploys a hybrid access point (HAP), the direct links of the energy harvesting and IT are blocked. Scenario-III-based IT of outdoor-user and cooperative jamming of indoor-user (IIIbTOJI) scheme, and cooperative jamming of outdoor-user and IT of indoor-user (IIIbJOTI) scheme are proposed, respectively. We analyze the closed-form expressions of outage probability and intercept probability for each scheme. The result shows that IIIbTOJI scheme has the best reliable performance among the proposed schemes, and IbJOTI and IIIbJOTI schemes perform best in security.

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.

With the rapid growth of global air traffic, 4D-flight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications, such as flight delay prediction, fuel consumption analysis, and traffic management. However, the adversarial attacks pose significant security threats to DL-based 4D-FTP systems reliant on automatic dependent surveillance-broadcast (ADS-B) sensors. Furthermore, existing vulnerabilities in time-series prediction (TSP) models for 4D-FTP remain underexplored due to the lack of effective and stealthy attack methods. To address this gap, we propose the dynamic quasi-hyperbolic momentum (QHM) iterative attack with small perturbation (DQM-Attack). This method leverages QHM and dynamic step sizes to optimize gradient utilization in 4D-FTP models. In addition, attack stealth is enhanced through the manifold smooth module (MSM) and sparse smooth reinforcement learning (SS-RL). Experimental results demonstrate that DQM-Attack effectively disrupts predictions with minimal perturbations across four state-of-the-art TSP models. This study appears to be the first to address stealthy adversarial attacks on 4D-FTP systems, revealing a critical security vulnerability in air traffic management (ATM).

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.

Reconfigurable intelligent surface (RIS) is viewed as a promising technique that can be utilized to improve the performance of systems by reconfiguring signal propagation environments. This article investigates green and secure unmanned aerial vehicle (UAV) Internet of Things (IoT) communications with the aid of RIS, where multiple UAVs harvest energy from a power beacon (PB) and send information uplink to access point (AP) with nonorthogonal multiple access (NOMA). In particular, communication can be divided into two phases during each time frame: 1) energy transfer and 2) information transmission (IT). Three RIS deployment strategies are proposed. In mode I, RISs are deployed between UAVs and AP to enhance the IT. In mode II, RISs are deployed between PB and UAVs to enhance the energy transfer. In mode III, RISs are deployed between UAVs and a hybrid AP (HAP) to enhance energy transfer and IT simultaneously. Considering phase compensation error caused by imperfect conditions, we define and evaluate ergodic capacity (EC), EC probability (ECP) and ergodic secrecy capacity (ESC) of three modes to measure the reliability and security of the system. The asymptotic expressions are also derived for further insights. Numerical results are presented to validate the correctness of theoretical derivations. Results demonstrate that the passive beamforming gain promised by RIS can significantly enhance the performance of systems. Mode III outperforms other modes in terms of reliability and security. When the transmission power and the number of UAVs increase, the ESCs of modes I and III converge to the same performance floor.

As a core technology of the new generation air traffic management (ATM) system, automatic dependent surveillance-broadcast (ADS-B) becomes increasingly crucial and its anomaly detection is important for safeguarding flight safety and enhancing the efficiency of air traffic. Deep learning has been used for ADS-B flight trajectory prediction and anomaly detection, but it is known for its susceptibility to poisoning attacks when updated to adapt feature distributions from new ADS-B flight data. In the study, we propose time neighborhood interpolation combined back gradient descent poisoning attacks which not only make full use of the gradient information of ADS-B anomaly detection models but also fully consider temporal correlations and maneuvering characteristics of ADS-B data during the aircraft’s take-off, climb, turning, and descent phase. The experimental results show that our proposed poisoning attack method can successfully poison the four state-of-the-art deep learning-based ADS-B time series unsupervised anomaly detection models. What is more, the experimental results also show that our method is superior to the other poisoning attack methods in terms of success rate and stealthiness. To the fullest extent of our knowledge, we exhibit, for the first time, the susceptibility of ADS-B time series unsupervised anomaly detection models to poisoning attacks, which is vital in safety-critical and cost-critical air traffic industry. For ease of understanding, our proposed poisoning attack method is referred to as ADS-Bpois.

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids.

This article investigates a reliable and secure wireless-powered communication system assisted by a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) serving indoor and outdoor users. To align with practical application, we consider two eavesdropping conditions: mixed and indirect eavesdropping links. To improve the reliability and security of downlink energy transfer (ET) and uplink information transmission (IT), we propose four STAR-RIS schemes utilizing time-switching (TS) and energy-splitting (ES) protocols: 1) The dual-TS (DTS) scheme employs the TS protocol for both downlink ET and uplink IT; 2) The mixed ES-TS (MET) scheme switches from the ES protocol in downlink ET to the TS protocol in uplink IT; 3) The dual-ES (DES) scheme employs the ES protocol for both downlink ET and uplink IT; 4) The mixed TS-ES (MTE) scheme switches from the TS protocol in downlink ET to the ES protocol in uplink IT. Further, accurate and asymptotic connection outage probability, secrecy outage probability, and effective secrecy throughput are analyzed for each proposed scheme. Theoretical analysis and simulation results demonstrate that: 1) At high transmission power or with a large number of STAR-RIS elements, the MTE scheme achieves the highest reliability. Conversely, the DES scheme provides the best reliability at lower transmission power or with fewer STAR-RIS elements; 2) Under indirect eavesdropping links, the DES scheme achieves the highest security, followed by the MTE, MET, and DTS schemes. However, this performance order is reversed under mixed eavesdropping links; 3) The DES scheme achieves the best overall performance, followed by the MTE, MET, and DTS schemes, all of which outperform the benchmark schemes.

Graph neural networks (GNNs) have achieved impressive success in various applications. However, training dedicated GNNs for small-scale graphs still faces many problems such as over-fitting and deficiencies in performance improvements. Traditional methods such as data augmentation are commonly used in computer vision (CV) but are barely applied to graph structure data to solve these problems. In this paper, we propose a training framework named MTDA (Multi-Task learning with Data Augmentation)-GNN, which combines data augmentation and multi-task learning to improve the node classification performance of GNN on small-scale graph data. First, we use Graph Auto-Encoders (GAE) as a link predictor, modifying the original graphs’ topological structure by promoting intra-class edges and demoting interclass edges, in this way to denoise the original graph and realize data augmentation. Then the modified graph is used as the input of the node classification model. Besides defining the node pair classification as an auxiliary task, we introduce multi-task learning during the training process, forcing the predicted labels to conform to the observed pairwise relationships and improving the model’s classification ability. In addition, we conduct an adaptive dynamic weighting strategy to distribute the weight of different tasks automatically. Experiments on benchmark data sets demonstrate that the proposed MTDA-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.

This article investigates physical-layer security (PLS) of a typical wireless-powered communication (WPC) system with the aid of intelligent reflecting surface (IRS) in the presence of a passive eavesdropper for Internet of Things (IoT), and proposes three IRS-aided secure WPC modes. Specifically, in mode-I, the IRS is deployed between hybrid access point (HAP) and wireless user (U) for co-located power station (PS) and access point (AP). In mode-II, the IRS is deployed between AP and U for separate PS and AP, while in mode-III, the IRS is deployed between PS and U for separate PS and AP. For each mode, the optimal phase shift is designed to maximize the reception of energy and information at the legitimate receiver. We comprehensively analyze the performance of each mode, and derive the closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) for each mode, respectively. The theoretical analysis and simulation results reveal that from the perspective of reliability, mode-I can achieve the best COP with the increased number of IRS elements, while mode-II and mode-III have a similar COP. From the perspective of security, mode-II can achieve the best SOP with the increased number of IRS elements, while mode-I and mode-III have a similar SOP. Moreover, under the condition of small transmission power at HAP/PS or small number of IRS elements, mode-I has the best EST, while as the power or the number increases, the EST of mode-II becomes the best one.

For semantic segmentation of remote sensing images, convolutional neural networks (CNNs) have proven to be powerful tools. However, the existing CNN-based methods have the problems of feature information loss, serious interference by clutter information, and ignoring the correlation between different scale features. To solve these problems, this article proposes a novel hidden feature-guided semantic segmentation network (HFGNet) for remote sensing images, which achieves accurate semantic segmentation by hierarchically extracting and fusing valuable feature information. Specifically, the hidden feature extraction module (HFE-M) is introduced to suppress the salient feature representation to mine more valuable hidden features. Meanwhile, the multifeature interactive fusion module (MIF-M) establishes the correlation between different features to achieve hierarchical feature fusion. The multiscale feature calibration module (MSFC) is constructed to enhance the diversity and refinement representation of hierarchical fusion features. Besides, the local-channel attention mechanism (LCA-M) is designed to improve the feature perception capability of the object region and suppress background information interference. We conducted extensive experiments on the widely used ISPRS 2-D Semantic Labeling dataset and the 15-Class Gaofen Image dataset. Experimental results demonstrate that the proposed HFGNet has advantages over several state-of-the-art methods. The source code and models are available athttps://github.com/darkseid-arch/RS-HFGNet.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free transmission admits grant-free users to access the channels otherwise solely occupied by grant-based users, and has been recently attracting considerable attention in terms of accommodating massive connectivity and reduce access delay. In this paper, we investigate the security of semi-grant-free NOMA transmission in the presence of passive and active eavesdropping attacks. In particular, the maximal user scheduling (MUS) and optimal user scheduling (OUS) schemes are proposed to combat the passive and active eavesdropping, respectively. Based on the proposed schemes, the exact secrecy outage probability (SOP) are analyzed to evaluate the system performance. The simulation results show the correctness of theoretic analysis and the superiority of the proposed schemes. The OUS scheme can achieve better performance than the MUS scheme owing to the use of active eavesdropper’s channel state information (CSI). The SOP achieved by the proposed schemes can be further improved with the increasing number of grant-free users and decreasing target rate (or target secrecy rate).

Nonorthogonal multiple access (NOMA) assisted semi-grant-free transmission admits grant-free users to access the channels otherwise solely occupied by grant-based users, and has been recently attracting considerable attention in terms of accommodating massive connectivity and reducing access delay in Internet of Things (IoT). In this work, we investigate the security of semi-grant-free NOMA transmission in the presence of passive and active eavesdropping attacks. In particular, for the scenario-I with strong grant-based user and weak grant-free users, the scenario-I-based maximal user scheduling (IbMUS) and scenario-I-based optimal user scheduling (IbOUS) schemes are proposed to combat the passive and active eavesdropping, respectively. For the scenario-II with weak grant-based user and strong grant-free users, two parallel schemes, namely, the scenario-II-based maximal user scheduling (IIbMUS) and scenario-II-based optimal user scheduling (IIbOUS) schemes, are proposed to combat the passive and active eavesdropping, respectively. These proposed schemes enhance the security by scheduling a grant-free user with maximal main channel capacity/maximal secrecy capacity to access the NOMA channel on the premise of ensuring the grant-based user’s Quality of Service. Based on these proposed schemes, the exact secrecy outage probability (SOP) are analyzed to evaluate the system performance. The simulation results validates the theoretic analysis and the superiority of the proposed schemes. The IbOUS and IIbOUS schemes can achieve better performance than the IbMUS and IIbMUS schemes owing to the use of active eavesdropper’s channel state information (CSI). The SOP achieved by the proposed schemes can be further improved with the increasing number of grant-free users and decreasing target rate (or target secrecy rate).

The introduction of deep learning (DL) technology can improve the performance of cyber–physical systems (CPSs) in many ways. However, this also brings new security issues. To tackle these challenges, this article explores the vulnerabilities of DL-based unmanned aerial vehicles (UAVs), which are typical CPSs. Although many research works have been reported previously on adversarial attacks of DL models, only few of them are concerned about safety-critical CPSs, especially regression models in such systems. In this article, we analyze the problem of adversarial attacks against DL-based UAVs and propose two adversarial attack methods against regression models in UAVs. The experiments demonstrate that the proposed nontargeted and targeted attack methods both can craft imperceptible adversarial images and pose a considerable threat to the navigation and control of UAVs. To address this problem, adversarial training and defensive distillation methods are further investigated and evaluated, increasing the robustness of DL models in UAVs. To our knowledge, this is the first study on adversarial attacks and defenses against DL-based UAVs, which calls for more attention to the security and safety of such safety-critical applications.

State estimation methods used in cyber–physical systems (CPSs), such as smart grid, are vulnerable to false data injection attacks (FDIAs). Although substantial deep learning methods have been proposed to detect such attacks, deep neural networks (DNNs) are highly susceptible to adversarial attacks, which modify input of DNNs with unnoticeable but malicious perturbations. This article proposes a method to explore targeted and stealthy FDIAs via adversarial machine learning. We pose FDIAs as sparse optimization problems to achieve initial attack objectives and remain stealthy during attacks. We propose a parallel optimization algorithm to efficiently solve the problems and explore additional sparse-state attacks. The experimental results show that for IEEE 14-bus and 118-bus systems, the success rate of two-state sparse attacks with small-scale targets is as high as 80%. In addition, the attack success rate can continue to increase as the number of attack states increases. The proposed attacks demonstrate that attackers can implement attacks that can bypass both bad data detectors and neural network detectors while keeping the initial attack objectives unchanged, which is a critical and urgent security threat in CPS.

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in satellite observation and military decisions. Due to discrete scattering properties, speckle noise interference, and various aircraft types, many existing methods struggle to achieve the desired detection performance. In this article, we propose an innovative semantic condition constraint guided feature aware network (SCFNet) for detecting different aircraft categories in SAR images. First, considering the discrete scattering properties of aircraft, we design a local-global feature aware module (LGA-M) and morphological-semantic feature aware module (MSF-M), which can effectively extract the fine-grained feature information contained in SAR images. Second, to effectively fuse different feature information, we construct a feature fusion pyramid (FFP), which uses different branches and paths to reasonably merge multiple feature information types and suppresses background information interference. Third, according to the structure characteristics of aircraft, the global coordinate attention mechanism (G-CAT) is presented to highlight foreground target features and suppress speckle noise interference. Finally, we construct semantic condition constraints, including constraint condition setting, semantic information calculation, and template matching, to improve aircraft localization and recognition accuracy. Extensive experiments demonstrate that the proposed SCFNet can obtain state-of-the-art performance on the SAR aircraft detection dataset, which achieves AP and F1 Score of 94.83% and 95.58%, respectively. The related implementation codes will be made publicly available at https://github.com/darkseid-arch/AirDetection.

Sonar image object detection is essential in underwater rescue and resource exploration. Although many convolution neural network (CNN)-based object detection algorithms have achieved great success in natural images. However, for underwater sonar images, problems, such as seabed reverberation noise interference, low proportion of foreground object region pixels, and poor imaging resolution, present considerable challenges to achieving accurate underwater object detection. To address these problems, we propose a novel sonar image object detector called the multilevel feature fusion network (MLFFNet). The detector consists of multiscale convolution module (MS-Conv), multilevel feature extraction module (ML-FEM), multilevel feature fusion module (ML-FFM), neighborhood channel attention mechanism (N-CAM), multiscale feature pyramid module (MS-FPN), and feature association module (FA). First, we use the MS-Conv to extract different scale feature information in the object region. Second, the ML-FEM and ML-FFM are used to obtain the local detail and global context features. Third, the N-CAM and MS-FPN are used to obtain the foreground objects’ semantic feature and position feature, and suppress the background region noise interference. Finally, we use the FA module to enhance the category and feature correlation of different objects. Extensive experiments are conducted on the real scene sonar image dataset. The experimental results demonstrate that MLFFNet performs better than other state-of-the-art object detection methods. Code and dataset are publicly athttps://github.com/darkseid-arch/SonarMLFFNet.

Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in satellite observation and military decisions. Due to discrete scattering properties, speckle noise interference, and various aircraft types, many existing methods struggle to achieve the desired detection performance. In this article, we propose an innovative semantic condition constraint guided feature aware network (SCFNet) for detecting different aircraft categories in SAR images. First, considering the discrete scattering properties of aircraft, we design a local-global feature aware module (LGA-M) and morphological-semantic feature aware module (MSF-M), which can effectively extract the fine-grained feature information contained in SAR images. Second, to effectively fuse different feature information, we construct a feature fusion pyramid (FFP), which uses different branches and paths to reasonably merge multiple feature information types and suppresses background information interference. Third, according to the structure characteristics of aircraft, the global coordinate attention mechanism (G-CAT) is presented to highlight foreground target features and suppress speckle noise interference. Finally, we construct semantic condition constraints, including constraint condition setting, semantic information calculation, and template matching, to improve aircraft localization and recognition accuracy. Extensive experiments demonstrate that the proposed SCFNet can obtain state-of-the-art performance on the SAR aircraft detection dataset, which achieves AP and F1 Score of 94.83% and 95.58%, respectively. The related implementation codes will be made publicly available at https://github.com/darkseid-arch/AirDetection.

Side-scan sonar (SSS) is a vital sensor for marine survey, which is widely used in military and civilian fields. The accurate segmentation of SSS images is critical in sonar image intelligent interpretation. Existing SSS image segmentation methods have several limitations, such as insufficient feature extraction, relatively worse segmentation results for tiny target categories, and serious interference by seabed reverberation noise and bright shadow region. To overcome these issues, we propose a novel encoder-decoder architecture SSS image segmentation method based on convolution neural network (CNN). First, we extract the multi-scale feature information contained in target region using the dynamic multi-scale dilated convolution (DMDC_Conv). Second, to further obtain the global and detail feature information, we construct the adaptive receptive field mechanism block (ARFM_Block). Third, we design a feature fusion attention mechanism block (FFAM_Block) to fuse high-level and low-level feature information with different scales and suppress background information interference. Final, we construct a tree structure optimization module (TSOM) to solve the problem of pixel misclassification and obtain refine SSS image segmentation results. Extensive experiments are carried out on the constructed real scene SSS image dataset. The experimental results show that the proposed method achieves 93.24% and 90.82% of MPA and MIoU, respectively, which outperforms other state-of-the-art methods and has a substantial advantage in inference speed and calculation parameters.

This article explores the problem of protection against false data injection attacks (FDIAs) on the power system state estimation. Although many research works have been reported previously to solve the same problem, yet most of them are only for perfect FDIAs. To address the problem reasonably, all related factors influencing the success probability and corresponding attack impact of imperfect FDIAs should also be considered. Based on such considerations, a topology, parameter, accuracy, level (TOTAL) protection strategy considering all corresponding factors is proposed. The TOTAL protection strategy minimizes the attack impact of typical imperfect FDIAs (single measurement attacks) while defending against typical perfect FDIAs (single-state variable attacks). Depending on whether the protection scheme contains phasor measurement units (PMUs), we formulate the meter selection as a linear binary programming or integer programming problem, which can be solved by suitable solvers. The proposed strategy is compared with the existing methods in the literature and evaluated using the standard IEEE test cases.

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.

With the fast increase in airspace density and high‐safety requirements on aviation, automatic dependent surveillance‐broadcast (ADS‐B) is regarded as the primary method in the next generation air traffic surveillance. The ADS‐B data is broadcast with the plain text without sufficient security measures, which results in various attack patterns emerging. However, in terms of constrictions with laws and regulations, ADS‐B attack data is difficult to collect and obtain, which is essential for data security research studies. To deal with the absence of ADS‐B attack data in real environments, the construction strategy on ADS‐B attack data is proposed. For construction fidelity, ADS‐B data features are analysed and modelled at first. Then the popular and classical attack patterns on ADS‐B data are analysed to establish threat models. Based on the original ADS‐B data sets, the construction strategy is designed to focus on attack target selection, key parameter determination, and mixture strategy, reproducing the attack intentions. The constructed ADS‐B attack data sets are hybrid data sets including the normal and attack data. By simulation analyses, the feasibility and availability of the construction strategy were validated with real ADS‐B data.

We investigate physical layer security of an uplink NOMA system consisting of one base station, multiple users and one eavesdropper. During each uplink transmission, two users are paired to perform NOMA and another user is opportunistically selected from the remaining idle users to act as a friendly jammer to emit artificial noise for confusing the eavesdropper. To enhance the transmission security for the system, we propose two friendly jammer selection aided uplink NOMA transmission schemes, namely random jammer selection aided uplink NOMA transmission (RJS-UNT) scheme without knowing the eavesdropper's channel state information (CSI), and optimal jammer selection aided uplink NOMA transmission (OJS-UNT) scheme where the eavesdropper's CSI is available. For comparison purpose, a non-jammer selection aided uplink NOMA transmission (NJS-UNT) scheme is also considered. Analytical closed-form expressions for the secrecy outage probability (SOP) are derived to evaluate the secrecy performance achieved by the proposed schemes. Also, the asymptotic SOPs are provided to obtain further insights. The analysis and simulation results indicate that the schemes converge to SOP floors with the increasing SNR, while the floors achieved by the RJS-UNT and OJS-UNT schemes are significantly lower than that achieved by the NJS-UNT scheme, showing the security advantage of the proposed schemes.

The key idea of non-orthogonal multiple access (NOMA) is to serve multiple users in the same resource block to improve the spectral efficiency. Whereas due to the resource sharing, a security flaw of NOMA emerges in the presence of internal untrusted users, especially untrusted near users who are closer to the base station and can easily access the confidential information for paired far users. To mitigate the flaw, in this paper, we investigate the reliable and secure transmission of NOMA systems with untrusted near users, and propose joint beamforming and power allocation (JBP) scheme for the scenario. Meanwhile, from security point of view, we extend to a worse-case scenario where both untrusted near users and external eavesdroppers exist, and propose joint artificial noise aided beamforming and power allocation (JANBP) scheme to achieve a reliable and secure transmission for the scenario. The exact and asymptotic closed-form expressions of secrecy outage probability (SOP) for the two scenarios are derived to evaluate the secrecy performance achieved by the proposed schemes, respectively. The analysis and simulation results show the superiority of the proposed JBP and JANBP schemes in terms of combating internal and external eavesdropping, and also indicate the two schemes can achieve the same SOP at high SNR.

Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS-B messages in distributed receivers without any mutual interference, such attacker can efficiently construct attack vectors to change the results of multilateration. The feasibility of a multidevice false data injection attack is demonstrated experimentally. Compared with previous multidevice attacks, the multidevice false data injection attacks can offer lower cost and more covert attacks. The simulation results show that the proposed attack can reduce the attackers’ cost by half and achieve better time synchronization to bypass the existing anomaly detection. Finally, we discuss the real-world constraints that limit their effectiveness and the countermeasures of these attacks.

Recent researches on data-driven and low-sparsity data injection attacks have been presented, respectively. To combine the two main goals (data-driven and low-sparsity) of research, this paper presents a data-driven and low-sparsity false data injection attack strategy. The proposed attacking strategy (EID: Eliminate-Infer-Determine) is divided into three stages. In the first step, the intercepted data is preprocessed by sparse optimization techniques to eliminate the outliers. The recovered data is then exploited to learn about the system matrix based on the parallel factorization algorithm in the second step. In the third step, the approximated system matrix is applied for the design of sparse attack vector based on the convex optimization. The simulation results show that the EID attack strategy achieves a better performance than the improved ICA-based attack strategy in constructing perfect sparse attack vectors. What is more, data-driven implementation of the proposed strategy is also presented which ensures attack performance even without the prior information of the system.