The Internet of Drones (IoD) allows for coordinated control of airspace for Unmanned Aerial Vehicles (UAVs), also known as drones. The decreasing costs of processors, sensors, and wireless connectivity have made it possible to use UAVs in many variety of military to civilian applications. While most applications utilizing the drones in the IoD have been real-time related, users are now interested in obtaining real-time services from drones that are tailored to a specific fly zone. This study develops a Sea Turtle Foraging Algorithm with Hybrid Deep Learning-based Intrusion Detection (STFA-HDLID) as a algorithm that recognizes and categorizes intrusions in the IoD environment. For this purpose, it is necessary to implement data pre-processing to standardize the input data via min-max normalization. Additionally, the feature selection process is also based on the STFA. Finally, a Deep Belief Network (DBN) with a Sparrow Search Optimization (SSO) algorithm is used for classification. A comprehensive experimental analysis is performed on a benchmark dataset to demonstrate the performance of the STFA-HDLID, which achieves maximum accuracy of 99.51% and 98.85% on the TON_IoT and UNSW-NB15 datasets, respectively, outperforming other techniques.