  Unmanned Aerial Vehicles (UAVs) are integral in various sectors like
agriculture, surveillance, and logistics, driven by advancements in 5G.
However, existing research lacks a comprehensive approach addressing both data
freshness and security concerns. In this paper, we address the intricate
challenges of data freshness, and security, especially in the context of
eavesdropping and jamming in modern UAV networks. Our framework incorporates
exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and
jamming threats. We introduce a transformer-enhanced Deep Reinforcement
Learning (DRL) approach to optimize task offloading processes. Comparative
analysis with existing algorithms showcases the superiority of our scheme,
indicating its promising advancements in UAV network management.
