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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.

At present, single-modal brain-computer interface (BCI) still has limitations in practical application, such as low flexibility, poor autonomy, and easy fatigue for subjects. This study developed an asynchronous robotic arm control system based on steady-state visual evoked potentials (SSVEP) and eye-tracking in virtual reality (VR) environment, including simultaneous and sequential modes. For simultaneous mode, target classification was realized by decision-level fusion of electroencephalography (EEG) and eye-gaze. The stimulus duration for each subject was non-fixed, which was determined by an adjustable window method. Subjects could autonomously control the start and stop of the system using triple blink and eye closure, respectively. For sequential mode, no calibration was conducted before operation. First, subjects’ gaze area was obtained through eye-gaze, and then only few stimulus blocks began to flicker. Next, target classification was determined using EEG. Additionally, subjects could reject false triggering commands using eye closure. In this study, the system effectiveness was verified through offline experiment and online robotic-arm grasping experiment. Twenty subjects participated in offline experiment. For simultaneous mode, average ACC and ITR at the stimulus duration of 0.9 s were 90.50% and 60.02 bits/min, respectively. For sequential mode, average ACC and ITR at the stimulus duration of 1.4 s were 90.47% and 45.38 bits/min, respectively. Fifteen subjects successfully completed the online tasks of grabbing balls in both modes, and most subjects preferred the sequential mode. The proposed hybrid brain-computer interface (h-BCI) system could increase autonomy, reduce visual fatigue, meet individual needs, and improve the efficiency of the system.

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.

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.

The purpose of this paper is to outline and summarize the latest technology on UAV security. This is driven by the growing market share represented by the technology. A silent yet vital revolution is happening: UAVs are becoming cheaper and more widely used. Unfortunately, the security and safety aspects were overlooked by manufacturers who eliminated potential threats and considered small UAVs as toys. To demonstrate the importance of UAV security once again, real examples of vulnerabilities and dangerous attacks are shown.