Digital Twin-Driven Secure Access Strategy for SAGIN-Enabled IoT Networks

TL;DR

This paper introduces a Digital Twin-driven secure access strategy for SAGIN-enabled IoT networks, enhancing data confidentiality and network performance by dynamically assessing risks and optimizing access decisions.

Contribution

It proposes a novel DT-based framework with an evolutionary game model and distributed algorithm for secure, efficient access in SAGIN-IoT networks, addressing dynamic eavesdropping threats.

Findings

Significantly improves network security against eavesdropping.

Balances system load and reduces queuing delay.

Outperforms benchmark schemes in simulation.

Abstract

In space-air-ground integrated networks (SAGIN)-enabled IoT networks, secure access has become a significant challenge due to the increasing risks of eavesdropping attacks. To address these threats to data confidentiality, this paper proposes a Digital Twin (DT)-driven secure access strategy. The strategy leverages a virtual replica of the physical SAGIN environment within the DT framework to continuously assess dynamic eavesdropping risks by quantifying secrecy capacity. Operating within this DT framework, an evolutionary game model dynamically balances the DT-updated secrecy capacity against queuing delay, steering IoT devices toward more secure and efficient access decisions. Furthermore, a novel distributed algorithm, integral to the DT operation, is developed to obtain the equilibrium access strategy for each device in a scalable manner. Simulation results demonstrate that the proposed DT-based approach substantially improves the security of SAGIN-enabled IoT networks. Additionally, it effectively balances system load, prevents overload occurrences, and decreases queuing delay compared to benchmark schemes, thereby comprehensively improving overall network performance.