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Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%.

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.

The introduction of deep learning 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 paper explores the vulnerabilities of deep learning-based unmanned aerial vehicles (UAVs), which are typical CPSs. Although many research works have been reported previously on adversarial attacks of deep learning models, only few of them are concerned about safety-critical CPSs, especially regression models in such systems. In this paper, we analyze the problem of adversarial attacks against deep learning-based UAVs and propose two adversarial attack methods against regression models in UAVs. Experiments demonstrate that the proposed non-targeted 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 deep learning models in UAVs. To our knowledge, this is the first study on adversarial attacks and defenses against deep learning-based UAVs, which calls for more attention to the security and safety of such safety-critical applications.

The high mobility of unmanned aerial vehicles (UAVs) could bring abundant degrees of freedom for the design of wireless communication systems, which results in that UAVs, especially UAV swarm, have attracted considerable attention. This paper considers a UAV Swarm enabled relaying communication system, where multiple UAV relays are organized via coordinated multiple points (CoMP) as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper. In order to maximize achievable secrecy rate of downlink, we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding, and both amplify-and-forward (AF) and decode-and-forward (DF) protocols are considered on the UAV swarm. Due to the non-convexity of the joint optimization problem, we propose an alternating optimization (AO) algorithm to decompose it into two subproblems utilizing block coordinate descent technique, then each subproblem is solved by successive convex optimization method. Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.

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 article studies the reliable and secure communications for a wireless-powered non-orthogonal multiple access (NOMA) system in the presence of multiple eavesdroppers. A joint artificial noise and power allocation (JAP) scheme is proposed to ensure the reliability and security for the system. Based on the JAP scheme, analytical closed-form expressions for connection outage probability (COP), secrecy outage probability (SOP) and effective secrecy throughput (EST) are derived, respectively, to evaluate the performance of the security-required user. Also, the asymptotic SOP and EST are analyzed to gain further insights. The numerical results demonstrate the correctness of the theoretic analysis and indicate that the proposed JAP scheme can achieve a better performance than the benchmark schemes.

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).

Deep neural networks have been applied to many tasks in air traffic management, ranging from anomaly detection to flight trajectory prediction. However, it has been shown that such algorithms are susceptible to adversarial examples. In this letter, we manage to show that current deep learning algorithms proposed for spoofing detection are vulnerable to maliciously crafted ADS-B data. To inject the false messages into the ADS-B system without being detected, we need to find adversarial perturbations to balance the need of overwhelming the channel noise and keeping the decoding error low. Simulation results demonstrate the viability of our approach to evade the DNN-based spoofing detector without increasing the decoding error.

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.

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.

The key idea of non-orthogonal multiple access (NOMA) is to serve multiple users in the same frequency band to improve the spectral efficiency. Whereas a security flaw emerges in the presence of untrusted users or low security clearance users who can access the messages of trusted users due to the spectrum sharing and may work as eavesdroppers. To achieve reliable and secure communications of NOMA systems under untrusted users, we in this paper propose two relay selection (RS) schemes termed decode-and-forward (DF) and amplify-and-forward (AF) protocols based optimal relay selection (i.e., DORS and AORS) schemes, respectively. To explicitly reveal the benefits of the DORS and AORS schemes, we derive the exact and asymptotic closed-form expressions of the secrecy outage probability (SOP) and positive secrecy capacity probability (PSCP) achieved by the two schemes, and analyze the optimality of the two schemes. Analysis and simulation results indicate that both the DORS and AORS schemes significantly outperform the benchmark schemes, in addition to achieving the same SOP and desirable PSCP at high SNR.

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.

Coverage-guided grey-box fuzzing for computer systems has been explored for decades. However, existing techniques do not adequately explore the space of continuous behaviors in Cyber-Physical Systems (CPSs), which may miss safety-critical bugs. Optimization-guided falsification is promising to find violations of safety specifications, but not suitable for identifying traditional program bugs. This article presents a fuzzing process for finding safety violations at the development phase, which is guided by two quantities: a branch coverage metric to explore discrete program behaviors and a Linear Temporal Logic (LTL) robust satisfaction metric to identify undesirable continuous plant behaviors. We implement CPFuzz to demonstrate the utility of the idea and estimate its effectiveness on seven control system benchmarks. The results show up to a better performance in average time to find violations on all benchmarks than S-TaLiRo and six benchmarks than S3CAMX. Finally, we exploit CPFuzz to synthesize the sensor spoofing attack on a DC motor with fixed-point overflow vulnerability as a case study.

This letter studies the problem of secure communication for a dual-hop energy harvesting (EH) relay network in the presence of multiple eavesdroppers. An adaptive cooperative jamming (ACJ) scheme is proposed, which can adaptively adjust power allocation factor to maximize the secrecy rate in practical networks where there is no eavesdroppers’ channel-state-information (CSI). To evaluate the performance of the proposed scheme, we analyze the ergodic secrecy capacity (ESC) of multi-antenna networks using the ACJ, and derive the closed-form expressions of the ESC for large antenna systems. Simulation results show that the proposed scheme outperforms the benchmark scheme.

This paper investigates physical layer security of cooperative NOMA in a severe scenario where there is no direct link from the source to far destination while the direct link between the eavesdropper and source exists. An energy harvesting (EH) jammer aided cooperative NOMA (namely EJCN) scheme is proposed to enable secure communications for the scenario. In particular, the EH jammer can opportunistically harvest energy from ambient radio frequency (RF) signals without occupying special time and power resources, and the harvested energy is exploited to emit the artificial noise (AN) for confusing the eavesdropper. The closed-form expressions of secrecy throughput achieved by the EJCN scheme are derived to evaluate the secrecy performance for the scenario. An iterative algorithm is presented to obtain the optimal codeword rate. The analysis and simulation results show that the EJCN scheme can achieve better secrecy performance than conventional transmission scheme without an EH jammer, indicating the superiority of the proposed scheme. In addition, the codeword rate of users should be carefully designed to maximize the secrecy throughput.

Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a more accurate and computationally efficient method called Adaptive Normalized Attack (ANA) to attack power systems using generate adversarial examples. We then adopt adversarial training to defend against attacks of adversarial examples. Experimental analyses demonstrate that our attack method provides less perturbation compared to the state-of-the-art FGSM (Fast Gradient Sign Method) and DeepFool, while our proposed method increases misclassification rate of learning methods for attacking power systems. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples compared to using state-of-the-art methods.

With the requirements on accuracy, coverage and reliability, the air traffic surveillance is being developed into the next generation. In 2020, ADS-B data is becoming the foundation to establish air traffic situation awareness capabilities. However, ADS-B is designed without sufficient security guarantees, which results in diverse attack threats. Hence, it is in demand of effective attack detections to keep attack data away from decision flows. To improve the accuracy and robustness, attack detection based on generative adversarial network for ADS-B data is proposed. The LSTM networks are the core components to set up the generator and discriminator to make the most of temporal spatial correlations. Utilizing the reconstruction error and discriminative loss, the comprehensive detection metric is obtained to identify attack behaviors. To enhance the robustness, the analysis threshold for detection decision is determined in terms of normal data intrinsic features. By experimental analysis on real ADS-B data, the accuracy and robustness of the proposed method is validated.

Power grid cyber-physical systems face a variety of cyber-attacks. Machine learning based stealthy false data injection attacks are explored in this paper. To avoid the detection of the Bad Data Detector, two issues are considered in the machine learning based attack strategy. One is how to use machine learning to generate suitable attack vectors based on eavesdropping measurements. The other is how to improve the robustness of the attack strategy if there are corrupted measurements or outliers. Considering these two problems, a robust linear regression based false data injection attack strategy is proposed. Moreover, a more sparse attack strategy is also explored to further reduce the cost of attackers. Simulations conducted on the IEEE 14-bus system verify the effectiveness of the attack strategies.

For the next generation air traffic surveillance, ADS-B is becoming the primary method to obtain more accurate data with wide coverage, which establishes the foundation for automatic and intelligent air traffic management system. However, ADS-B is designed without sufficient security considerations, transmitting with plain text without integrity and authentication validations. Thus, ADS-B data is in face of various attack threats, which may cause disruptions on system availability and reliability. To eliminate effects of attack behaviours, attack detection is in demand to avoid attack data injecting into decision making flow. Based on hidden Markov model with sticky hierarchical Dirichlet process, the dynamic temporal detection method is proposed to detect multiple attack patterns. Taking advantage of multiple attribute data, the dimensions of data are reduced to one dimension to set up feature sequences. With sticky hierarchical Dirichlet process, the parameters are obtained for hidden Markov model dynamically. Utilizing hidden Markov model, the generative model is established to predict hidden states of ADS-B data sequence. By analysing the contextual deviation information on hidden state sequences, the attack behaviours are discriminated to determine the attack data. By experiments on real ADS-B data, the feasibility and accuracy of the proposed method are validated.

In the next generation air traffic surveillance, ADS-B is the primary surveillance method to improve situation awareness capabilities. But ADS-B protocol is absent of sufficient security considerations, especially for data integrity and authentication. As a result, attack patterns on ADS-B data are emerging and efficient attack detection strategies are in great demand to enhance data security. To decrease the time delay of detection, enhance accuracy and mitigate the concept drift impacts, the online sequential attack detection strategy based on hierarchical temporal memory are proposed. By applying binary encoding, ADS-B data is transformed into sparse distribution representation with temporal and spatial correlations. The encoded data is push into hierarchical temporal memory and online learning schemes are established for the ADS-B stream data. With the sequential ADS-B data, hierarchical temporal memory is updated and used to generate the deviations between predictions and original values for the corresponding ADS-B data. Designing and applying deviation analysis, sequential analysis and adaptive threshold check, the differences between normal and novelty are magnified and easy to be distinguished. According to experimental analysis, the attack detection strategy is efficient on processing time and accuracy.

In this paper, a two-way amplify-and-forward (AF) energy harvesting (EH) relaying system with the presence of co-channel interference (CCI) is studied. Firstly, a lower bound of outage probability is developed, which is shown to be very tight over the entire signal-to-noise ratio (SNR) regions. Then, a simple and informative high-SNR outage approximation is derived, indicating that the outage probability decays as ρ-1lnρ s , with ρ s being the transmit SNR. Based on this high-SNR approximation, the optimal power splitting (PS) factor minimizing the system outage probability is characterized. Analytical results manifest that the optimal PS factor is irrelevant to the CCI and is an increasing function with respect to ρ s . In addition, it is numerically shown that the optimal PS factor is a decreasing function of the energy conversion efficiency. Finally, all the analytical results are validated through Monte-Carlo simulations.