Adversarial-aware multi-agent reinforcement learning for secure resource allocation in Heterogeneous Aerial Access IoT networks

This paper proposes an Adversarial-Aware Multi-Agent Deep Deterministic Policy Gradient (AA-MADDPG) framework integrating adversarial risk analysis with deep reinforcement learning for robust resource allocation and offloading in heterogeneous aerial access networks. We model adversarial behavior using three rationality paradigms, including Nash equilibrium, level-k thinking, and prospect maximizing, employing Bayesian model averaging for robust adversarial action prediction. The framework enables collaborative decision-making among aerial access tiers and IoT devices while maintaining attack resilience. Simulations demonstrate that AA-MADDPG reduces task drop rates by 27% and energy consumption by 19% compared to baselines.