Security-Sensitive Task Offloading in Integrated Satellite-Terrestrial Networks

TL;DR

This paper introduces a security-aware task offloading strategy for integrated satellite-terrestrial networks using LEO satellites, optimizing delay, energy, and attack mitigation with a reinforcement learning approach.

Contribution

It proposes a novel security-sensitive task offloading framework leveraging MDP and PPO in LEO satellite edge environments, addressing attack risks often overlooked in prior work.

Findings

The proposed algorithm reduces offloading delay and energy consumption.

It effectively mitigates malicious satellite attacks during task offloading.

Performance surpasses benchmark methods in experimental evaluations.

Abstract

With the rapid development of sixth-generation (6G) communication technology, global communication networks are moving towards the goal of comprehensive and seamless coverage. In particular, low earth orbit (LEO) satellites have become a critical component of satellite communication networks. The emergence of LEO satellites has brought about new computational resources known as the \textit{LEO satellite edge}, enabling ground users (GU) to offload computing tasks to the resource-rich LEO satellite edge. However, existing LEO satellite computational offloading solutions primarily focus on optimizing system performance, neglecting the potential issue of malicious satellite attacks during task offloading. In this paper, we propose the deployment of LEO satellite edge in an integrated satellite-terrestrial networks (ISTN) structure to support \textit{security-sensitive computing task offloading}. We model the task allocation and offloading order problem as a joint optimization problem to minimize task offloading delay, energy consumption, and the number of attacks while satisfying reliability constraints. To achieve this objective, we model the task offloading process as a Markov decision process (MDP) and propose a security-sensitive task offloading strategy optimization algorithm based on proximal policy optimization (PPO). Experimental results demonstrate that our algorithm significantly outperforms other benchmark methods in terms of performance.