Feisha Hu¹, Qi Wang^1,*, Haijian Shao^1,2, Shang Gao¹, Hualong Yu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732

Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of… More > Graphic Abstract

Yongjie Ding¹, Zhangjie Fu^1,2,*

Journal of Cyber Security, Vol.3, No.2, pp. 69-78, 2021, DOI:10.32604/jcs.2021.019105

Abstract In recent years, with the rapid development of the drone industry, drones have been widely used in many fields such as aerial photography, plant protection, performance, and monitoring. To effectively control the unauthorized flight of drones, using GPS spoofing attacks to interfere with the flight of drones is a relatively simple and highly feasible attack method. However, the current method uses ground equipment to carry out spoofing attacks. The attack range is limited and the flexibility is not high. Based on the existing methods, this paper proposes a multi-UAV coordinated GPS spoofing scheme based on YOLO Nano, which can launch… More >