Development of Smart Machine learning Algorithm to Design and Develop the IoT Based Drone Cyber Physical Security System

As a result of significant strides in drone technology, the field of miniature drones has emerged. Drones rely on IoT’s location sharing capabilities for precise aerial exploration. Drones are convenient and useful, but they are not safe from security and privacy breaches because of flaws in their design and construction. To get the most out of your drones, you need to set up a secure and dependable network. The civic and military sectors might benefit significantly from the use of tiny drones, but they are vulnerable to security and privacy breaches. Modifications to the data translation and storage mechanisms of tiny drones are required by their existing design. Privacy methods that are appropriate for the task at hand. This research, which delves into the newest innovations in drone safety, security, and privacy, highlights the need for private drone networks impervious to interception and interference. Drones that rely on the Internet of Things will be more adaptable and secure against cyber attacks because Drones are built with artificial intelligence and machine learning models in mind, thanks to the suggested framework. Additionally, this research creates a new dataset by combining a drone dateset with two standard dateset. The proposed method outperforms competing methods on the drone dateset (99.89%) and the combined dateset (91.64%). In conclusion, this intelligent framework offers a practical means of bolstering the safety of cyber-physical satellite systems and IoT-aided aerial vehicle method, satisfying leveling safety requirements of a more interconnected world.