Alturki Nazik, Aljrees Turki, Umer Muhammad, Ishaq Abid, Alsubai Shtwai, Saidani Oumaima, Djuraev Sirojiddin, Ashraf Imran
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia.
Sensors (Basel). 2023 Aug 14;23(16):7154. doi: 10.3390/s23167154.
The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber-physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.
小型无人机技术领域是无人机技术进步取得突破的成果。无人机利用物联网（IoT）提供定位导航服务。但是，由于其架构和设计方面的问题，无人机也无法免受与安全和隐私相关的威胁。建立一个安全可靠的网络对于无人机获得最佳性能至关重要。虽然小型无人机为民用和国防工业的发展提供了有前景的途径，但它们容易受到安全、安保和隐私方面的攻击。小型无人机的当前架构需要对其数据转换和隐私机制进行修改，以符合领域要求。本研究论文调查了与无人机及无人机物联网（IoD）相关的安全、安保和隐私方面的最新趋势，强调了构建不受拦截和入侵影响的安全无人机网络的重要性。为了减轻网络安全威胁，所提出的框架将智能机器学习模型纳入物联网辅助无人机的设计和结构中，形成了适应性强且安全的技术。此外，在这项工作中，构建了一个新的数据集，即一个由无人机数据集和两个基准数据集组成的合并数据集。所提出的策略优于先前的算法，在无人机数据集上的准确率达到99.89%，在合并数据集上的准确率达到91.64%。总体而言，这个智能框架为提高网络物理卫星系统和物联网辅助飞行器系统的安全性和弹性提供了一种潜在方法，应对了互联世界中不断上升的安全挑战。
