Aerial IRS Deployment-Aided Secure Computation Offloading Against DISCO Jamming Attacks
Minghui Min, Peng Zhang, Jiayang Xiao, Ruixin Yang, Shiyin Li, Huan Huang, Hongliang Zhang, and Zhu Han

TL;DR
This paper introduces an aerial intelligent reflective surface (AIRS) to enhance secure computation offloading in MEC systems under DISCO jamming attacks, using a two-timescale DRL-based optimization framework.
Contribution
It proposes a novel AIRS deployment and optimization framework with a dual-agent DRL scheme to counter DISCO jamming in MEC offloading.
Findings
AIRS deployment significantly improves offloading performance against jamming.
The DDADSO scheme outperforms benchmark schemes in simulations.
Two-timescale optimization effectively adapts to dynamic channel conditions.
Abstract
With the rapid growth of Multi-access Edge Computing (MEC), secure and efficient computation offloading from user equipment (UEs) to edge access points (APs) is critical. However, DISCO intelligent reflective surface-based fully-passive jammers (DIRS-based FPJs) use random time-varying phase shifts to launch DISCO jamming attacks, disrupting offloading performance. This paper leverages an aerial intelligent reflective surface (AIRS) to enable secure computation offloading against DISCO jamming by jointly optimizing offloading ratios, AIRS phase shifts, and deployment. A two-timescale (2Ts) framework is proposed to address the optimization challenge caused by the distinct update frequencies of different strategies. Specifically, AIRS deployment is adjusted on a long timescale to boost antijamming capability due to the impracticality of frequent physical adjustment, while offloading…
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