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.

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).

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.

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.

This letter studies physical layer security (PLS) of an intelligent reflecting surface (IRS) aided wireless powered communication (WPC) system in the presence of a passive eavesdropper, and proposes three secure IRS-WPC modes. 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 legitimate receiver. The accurate and asymptotic ergodic secrecy capacity (ESC) of each mode is analyzed, and various new closed-form expressions are obtained. The results reveal that mode-I and mode-II significantly outperform benchmark without IRS, while mode-III outperforms benchmark without IRS under lower transmission power or small number of IRS elements.

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.