To address low threat avoidance efficiency and poor global path adaptability in UAV path planning under threatening environments, this paper proposes a hybrid A*-Artificial Potential Field (APF) path planning method based on spatio-temporal grid optimization. First, a new global fine-grained spatio-temporal grid system is developed by integrating advantages of GeoSOT binary encoding and BeiDou grid location code subdivision rules, enabling unified modeling of complex spatio-temporal environments. Ground threat and maze scenarios are constructed for verification. Second, traditional A* and APF algorithms are improved: the A* algorithm is enhanced with threat costs, dynamic neighborhood search, and local backtrack mechanisms to address low efficiency and incompatibility with threat avoidance; the APF algorithm is optimized with a dual gravitational field collaboration mechanism and distance-parameter-based repulsive field model to overcome local minima and unreachable goals. Finally, a sliding window-driven path association model achieves seamless collaboration between global and local planning. Experimental results show the proposed method outperforms traditional algorithms in comprehensive performance, with the improved A* algorithm excelling in path length, computation time, threat value, and search nodes, and the improved APF algorithm achieving complete safe obstacle avoidance in dynamic environments. The collaborative mechanism effectively handles complex scenarios.

In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial patches being a relatively straightforward method to implement. However, current research on adversarial patches, especially those targeting UAV object detection, is limited. This scarcity is notable given the complex and dynamically changing environment inherent in UAV image acquisition, which necessitates the development of more robust adversarial patches to achieve effective attacks. To address the challenge of adversarial attacks in UAV high-altitude reconnaissance, this paper presents a robust adversarial patch generation framework. Firstly, the dataset is reconstructed by considering various environmental factors that UAVs may encounter during image collection, and the influences of reflections and shadows during photography are integrated into patch training. Additionally, a nested optimization method is employed to enhance the continuity of attacks across different altitudes. Experimental results demonstrate that the adversarial patches generated by the proposed method exhibit greater robustness in complex environments and have better transferability among similar models.

Deep neural network (DNN)-based object detection has been extensively implemented in Unmanned Aerial Vehicles (UAVs). However, these architectures reveal significant vulnerabilities when faced with adversarial attacks, particularly the physically realizable adversarial patches, which are highly practicable. Existing methods for generating adversarial patches are easily affected by factors such as motion blur and color distortion, leading to a decline in the attack success rate (ASR). To address these limitations, a low-frequency robust adversarial patch (LFRAP) generation framework that integrates three dimensions of color, texture, and frequency domain is proposed. Firstly, a dynamic extraction mechanism for the environmental color pool based on clustering is proposed. This mechanism not only improves the degree of environmental integration but also reduces printing losses. Secondly, mathematical modeling of the effects of Unmanned Aerial Vehicle (UAV) high-speed motion is incorporated into the patch training process. The specialized texture derived from this modeling alleviates patch blurring and the subsequent decrease in attack efficiency caused by the high-speed movement of UAVs. Finally, a frequency domain separation strategy is introduced in the generation process to optimize the frequency space distribution, thereby reducing information loss during image recapture by UAV vision systems. The experimental results show that this framework increased the environment integration rate of the generated patches by 18.9%, and the attack success rate under the condition of motion blur increased by 19.2%, which significantly outperformed conventional methods.

Aiming at the problem of random environment interference in the process of strategy interaction and the behavioral evolution of an aerial attack–defense game, this paper considers the influence of the difference in the performance and value between both game players in terms of strategy evolution; explores the randomness of the complex battlefield environment, the uncertainty of the behavioral state of game players, and the limitations of the emergent situation; constructs a mathematical model of the stochastic evolution of an aerial-coordinated attack–defense game in uncertain environments; and studies the stability of the strategy interaction and behavioral decision-making process of both players of the aerial attack–defense game. Simulation results show that many factors of the performance and value between both game players have a greater impact on the strategy evolution trend in both game players, which not only causes changes in the results of the strategy selection but also affects the rate of strategy evolution for the game players. In addition, random environmental factors cause a certain degree of interference to the strategy evolution process of the game players, which usually accelerates the game players’ strategy evolution rate and greatly affects the evolution process of the game players’ strategy. This study can provide a theoretical basis and feasible reference for improving mission decision-making, response mechanisms, and system modeling of an aerial attack–defense game, which has important theoretical value and practical significance.

Recent advancements in unmanned aerial vehicle (UAV) technology have facilitated its widespread adoption across a spectrum of sectors, such as commercial logistics, agricultural surveillance, industrial diagnostics, and military maneuvers. However, the widespread adoption has also engendered a burgeoning array of security concerns. Unmanned aerial systems (UAS) networks are characterized by high node mobility, unstable links, open communication environments, and limited platform resources, which in turn exhibit typical vulnerabilities in terms of cybersecurity. Most current studies on UAV cybersecurity issues tend to focus on individual UAVs, often neglecting the holistic cybersecurity of UAS. This paper outlines the composition of UAS network architecture. It summarizes the main cybersecurity challenges UAS faces within six categories—spoofing, tampering, information disclosure, denial of service (DoS), service refusal, and privilege escalation—based on the STRIDE threat model. Corresponding methods for risk mitigation and security protection strategies are proposed. Ultimately, the paper provides a perspective on the future development directions of UAS cybersecurity, aiming to offer a reference for addressing related issues in subsequent research and practice.