Intelligent drone-assisted robust lightweight multi-factor authentication for military zone surveillance in the 6G era

Abstract

In the diverse range of surveillance applications, large-scale deployment of next-generation communication technologies and the fast-growing development of unmanned aerial vehicles (UAVs) are envisioned as key in-novations in the adoption of beyond-fifth generation (B5G) and 6G communication. Due to its self-reliance and versatility, a complex communication network can be formulated strategically to improve the application fea-tures of drone technology, including search-and-rescue, mission-critical services, and military surveillance. In recent times, technological advancements in hardware and software infrastructure have gained momentum to-ward seamless information interaction in aerial communication. Unfortunately, the recurrent process of user authentication causes severe communication instability in an unmanned aerial ad hoc network (UAANET) leading to some serious cyber threats, such as buffer overflow, denial of service, and spoofing. Therefore, building secure and reliable authentication is inevitable in order to protect drone-aided healthcare service en-vironments. To protect aerial zones and improve security efficiency, this paper designs robust lightweight secure multi-factor authentication (RL-SMFA). The proposed RL-SMFA utilizes an AI-enabled, secure analytics phase to verify the genuineness of drone swarms for the ground control station. While protecting communication with drone vehicles, we also observe that power consumption by drones is reduced to a large extent. Using formal verification under a random oracle model, we show that the proposed RL-SMFA can functionally resist system vulnerabilities and constructively decrease the computation and communication costs of the UAANET. Lastly, the simulation study using ns3 shows that the proposed RL-SMFA achieves better performance efficiencies in terms of throughput rate, packet delivery ratio, and end-to-end delay than other state-of-the-art approaches to discovering a proper link establishment.