Alhoraibi Lamia, Alghazzawi Daniyal, Alhebshi Reemah
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel). 2024 Sep 23;24(18):6156. doi: 10.3390/s24186156.
Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques.
无线通信和自动化的进步彻底改变了移动系统，尤其是通过自动驾驶车辆和无人机（UAV）。无人机的空间坐标通过全球定位系统（GPS）信号确定，由于与GPS欺骗相关的未加密和未认证传输，容易受到网络攻击，而GPS欺骗是一个重大威胁。为了减轻这些漏洞，已经使用机器学习（ML）算法开发并增强了无人机入侵检测系统（IDS）。然而，对抗机器学习（AML）通过利用ML模型引入了新的风险。本研究提出了一种采用AML方法的无人机IDS，以增强对GPS欺骗攻击的检测和分类。关键贡献是开发了一种AML检测模型，该模型显著提高了无人机系统的鲁棒性和安全性。我们的研究结果表明，该模型实现了98%的检测准确率，证明了其在管理大规模数据集和复杂任务方面的有效性。本研究通过引入一种以对抗训练防御方法和先进深度学习技术为中心的新型检测模型，强调了物理层安全对增强无人机IDS的重要性。
