Dual-Security-Assured Computation Offloading for ISCC LEO Satellite-Enabled Space–Air–Ground Networks

This paper presents a Dual-Security-Assured Computation Offloading (DSACO) scheme for space-air-ground networks (SAGNs), which exploits the integrated sensing, communication, and computing (ISCC) capability of the low Earth orbit (LEO) satellite to support secure and efficient computation offloading. In the proposed scheme, the air-ground mode serves as the default edge-processing strategy due to its low latency and energy consumption, while the LEO satellite senses the malicious aerial eavesdropper and protects the ground device (GD)-to-UAV links through directional anti-eavesdropping jamming. When such protection becomes insufficient, the system switches to direct GD-to-LEO offloading via dedicated frequency bands. Accordingly, the secure offloading process is formulated as a worst-case average long-term energy minimization problem under sensing uncertainty. To solve the resulting dual-timescale mixed-integer nonlinear problem, we develop a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework for the joint optimization of sensing duration, UAV trajectories, computation offloading, and resource allocation. Simulation results demonstrate that the proposed scheme significantly enhances system security while maintaining offloading efficiency, and that the H-MADRL framework outperforms benchmark methods.