Abstract
The adoption and recent development of Unmanned Aerial Vehicles (UAVs) are because of their widespread applications in the private and public sectors, from logistics to environment monitoring. The incorporation of 5G technologies, satellites, and UAVs has provoked telecommunication networks to advance to provide more stable and high-quality services to remote areas. However, UAVs are vulnerable to cyberattacks because of the rapidly expanding volume and poor inbuilt security. Cyber security and the detection of cyber threats might considerably benefit from the development of artificial intelligence. A machine learning algorithm can be trained to search for attacks that may be similar to other types of attacks. This study proposes a new approach: metaheuristics with machine learning-enabled cybersecurity in unmanned aerial vehicles (MMLCS-UAVs). The presented MMLCS-UAV technique mainly focuses on the recognition and classification of intrusions in the UAV network. To obtain this, the presented MMLCS-UAV technique designed a quantum invasive weed optimization-based feature selection (QIWO-FS) method to select the optimal feature subsets. For intrusion detection, the MMLCS-UAV technique applied a weighted regularized extreme learning machine (WRELM) algorithm with swallow swarm optimization (SSO) as a parameter tuning model. The experimental validation of the MMLCS-UAV method was tested using benchmark datasets. This widespread comparison study reports the superiority of the MMLCS-UAV technique over other existing approaches.