This chapterReconfigurable Intelligent Surface (RIS) investigates methods for securing Unmanned Aerial VehicleUnmanned Aerial Vehicle (UAVs)-assisted Reconfigurable Intelligent SurfaceReconfigurable Intelligent Surface (RIS) (UAV-RIS) systems in next-generation communication networks. We propose a sophisticated deep reinforcement learning framework named Long Short-Term MemoryLong Short-Term Memory (LSTM) Deep Deterministic Policy GradientDeep Deterministic Policy Gradient (DDPG) with Curiosity-Driven Exploration (LSTMLong Short-Term Memory (LSTM)-DDPG-CDE) to enhance security and ensure reliable communication within UAV-assisted RISReconfigurable Intelligent Surface (RIS) networks. By leveraging curiosity-driven intrinsic rewards, the framework encourages exploration of uncertain and novel network states, enabling the agent to detect and adapt to potential threats more effectively. The LSTMLong Short-Term Memory (LSTM)-DDPGDeep Deterministic Policy Gradient (DDPG)-CDE architecture captures temporal dependencies in UAV trajectories, RISReconfigurable Intelligent Surface (RIS) phase configurations, and dynamic wireless channels, improving learning efficiency, convergence speed, and robustness against adversarial attacks. Through extensive simulations, we demonstrate that integrating UAV-RISReconfigurable Intelligent Surface (RIS) with the LSTMLong Short-Term Memory (LSTM)-DDPGDeep Deterministic Policy Gradient (DDPG)-CDE framework significantly mitigates malicious actions, such as signal nullification, phase manipulation, and eavesdropping, outperforming traditional reinforcement learning approaches.