Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

In recent years, drones or Unmanned Aerial Vehicles (UAVs) got significant attention among researchers because of their extensive application in commercial applications, border surveillance, etc. As the conventional terrestrial communication system does not work effectively on heavy calamities namely floods, landslides, cyclones, earthquakes, etc., UAVs can offer a potential solution for inexpensive, rapid, and wireless communication. Despite the drones’ benefits in emergency monitoring, security is been a main factor because of the existence of wireless connections for transmission. Therefore, this article introduces optimal deep learning with image encryption-based secure drone communication (ODLIE-SDC) technique. The major intention of the ODLIE-SDC technique lies in the effectual secure communication and classification process in emergency monitoring scenarios. To accomplish this, the presented ODLIE-SDC technique designs a hyperchaotic map-based image encryption technique and its optimal keys are produced by the use of a rider optimization algorithm (ROA). The image classification process is performed encompassing EfficientNet-B4-CBAM feature extraction and enhanced stacked autoencoder (ESAE) classification. Finally, the hyperparameter tuning of the EfficientNet-B4-CBAM technique takes place using the Bayesian optimization (BO) algorithm. The experimental validation of the ODLIE-SDC technique is tested on the AIDER dataset. The comprehensive comparative analysis reported the enhanced performance of the ODLIE-SDC technique over other existing approaches.