Secure UAV Communication Using AGRO and Deep Neuro Fuzzy Networks for Malicious Detection

ABSTRACT Presently, unmanned aerial vehicles (UAVs) are being extensively employed because of their numerous benefits, including their affordability and ability to be quickly deployed on demand. The exchange of data is performed among the UAVs. Despite its benefits, this type of communication may also pose security risks due to its active network topology. Here, Archimedes gold rush optimization (AGRO) is established for safe communication in UAV. Firstly, the UAV network is simulated and routing is performed. The AGRO is designed for routing establishment. Here, the merging of gold rush optimization (GRO) and Archimedes optimization algorithm (AOA) forms the AGRO. Furthermore, the fitness parameters are measured for the secure routing process. Following this, data communication is performed by the evaluation and monitoring agent. Afterwards, the malicious detection process is conducted employing deep neuro fuzzy network (DNFN) by decision‐making agent, in which the attributes like signal strength, packet size, round trip time (RTT), number of incoming packets, and packet delivery are considered. If the attacks are detected, then the attack mitigation is conducted employing the defensive agent. It can be identified that AGRO has gained a delay of 0.727 s, 0.945 with detection rate, and 0.750 of packet delivery ratio (PDR).