Secure UAV routing with Gannet Optimization and Shepard Networks

Abstract

In recent times, Unmanned Aerial Vehicle (UAV) networks have been extensively employed in civilian and military scenarios. However, they are also highly susceptible to threats from adversaries owing to its distributed nature. To ensure reliable and secure functioning of smaller drones, designing a robust network architecture and applying tailored privacy as well as security mechanisms is important. This research presents a Gannet Weaving Optimization Algorithm based Adversarial Shepard Convolutional Spinal Network (GWOA+Adversarial ShCSpinalNet) for efficient routing and malicious detection in UAV. Initially, the UAV network is simulated, and then, routing is accomplished utilizing the Gannet Weaving Optimization Algorithm (GWOA) by considering the multi-objectives. The GWAO is designed by incorporating Gannet Optimization Algorithm (GOA) with Carpet Weaving Optimization (CWO). Here, energy prediction is accomplished by a Dilated Residual Network (DRN). Thereafter, data communication is performed by monitoring agents. Then, malicious detection is carried out employing Adversarial ShCSpinalNet by a decision-making agent, wherein packet delivery, round trip time, signal strength count of incoming packets and size of packet are considered as attributes. Moreover, Adversarial ShCSpinalNet is introduced by combining Shepard Convolutional Neural Network (ShCNN) and SpinalNet with an adversarial loss function. Thereafter, attack mitigation is conducted by a defensive agent. The GWOA+Adversarial ShCSpinalNet attained a maximal detection rate of 94.827 %, energy of 44.755J and Packet Delivery Ratio (PDR) of 76.446 % as well as a minimal delay of 0.553ms.