Unmanned aerial vehicles (UAVs) have various applications in different settings, including for example, surveillance, packet delivery, emergency response, data collection in the Internet of Things (IoT), and connectivity in cellular networks. However, this technology comes with many risks and challenges such as vulnerabilities to malicious cyber-physical attacks. This article studies the problem of path planning for UAVs under GPS sensor permanent faults in a cyber-physical system (CPS) perspective. Based on studying and analyzing the CPS architecture of the UAV, the cyber “attacks and threats” are differentiated from attacks on sensors and communication components. An efficient way to address this problem is to introduce a novel approach for UAV’s path planning resilience to cyber-attack artificial potential field (RCA-APF) algorithm. The proposed algorithm completes the three stages in a coordinated manner. In the first stage, the permanent faults on the GPS sensor of the UAV are detected, and the UAV starts to divert from its initial path planning. In the second stage, we estimated the location of the UAV under GPS permanent fault using received signal strength (RSS) trilateration localization approach. In the final stage of the algorithm, we implemented the path planning of the UAV using an open source UAV simulator. Experimental and simulation results demonstrate the performance of the algorithm and its effectiveness, resulting in efficient path planning for the UAV.

Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states, and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring a CPS’s physical state back to a target condition under adversarial attacks. To call for attention on CPS recovery and identify existing efforts, we have surveyed a total of 30 relevant papers. We identify a major partition of the proposed recovery strategies: shallow recovery vs. deep recovery, where the former does not use a dedicated recovery controller while the latter does. Additionally, we surveyed exploratory research on topics that facilitate recovery. From these publications, we discuss the current state-of-the-art of CPS recovery, with respect to applications, attack type, attack surfaces, and system dynamics. Then, we identify untouched sub-domains in this field and suggest possible future directions for researchers.

Safe reinforcement learning (RL) aims to derive a control policy that navigates a safety-critical system while avoiding unsafe explorations and adhering to safety constraints. While safe RL has been extensively studied, its vulnerabilities during the policy training have barely been explored in an adversarial setting. This article bridges this gap and investigates the training time vulnerability of formal language-guided safe RL. Such vulnerability allows a malicious adversary to inject backdoor behavior into the learned control policy. First, we formally define backdoor attacks for safe RL and divide them into active and passive ones depending on whether to manipulate the observation. Second, we propose two novel algorithms to synthesize the two kinds of attacks, respectively. Both algorithms generate backdoor behaviors that may go unnoticed after deployment but can be triggered when specific states are reached, leading to safety violations. Finally, we conduct both theoretical analysis and extensive experiments to show the effectiveness and stealthiness of our methods.

There are various applications of Cyber-Physical systems (CPSs) that are life-critical where failure or malfunction can result in significant harm to human life, the environment, or substantial economic loss. Therefore, it is important to ensure their reliability, security, and robustness to the attacks. However, there is no widely used toolbox to simulate CPS and target security problems, especially the simulation of sensor attacks and defense strategies against them. In this work, we introduce our toolbox CPSim, a user-friendly simulation toolbox for security problems in CPS. CPSim aims to simulate common sensor attacks and countermeasures to these sensor attacks. We have implemented bias attacks, delay attacks, and replay attacks. Additionally, we have implemented various recovery-based methods against sensor attacks. The sensor attacks and recovery methods configurations can be customized with the given APIs. CPSim has built-in numerical simulators and various implemented benchmarks. Moreover, CPSim is compatible with other external simulators and can be deployed on a real testbed for control purposes. ¹

Image-based 3D reconstruction technology has been widely used in automatic driving, medical image reconstruction, positioning and navigation. At present, the digital design is still dominated by drawing 3D models manually, which has some shortcomings such as time-consuming and limited accuracy. To solve the above problems and improve the efficiency of digital design, a learning-based multi-view stereo 3D reconstruction technique is introduced for dense reconstruction, and then a grid model is generated for parametric design. We experimented our method and the results show that it can greatly improve the efficiency of 3D digital design, obtain high precision, and retain the texture information of the object.