Secure UAV-RIS-Enabled IoT Systems: Federated DDPG With Attention Mechanism for Adversarial Attack Mitigation

Ensuring the secure communication of unmanned aerial vehicle-assisted reconfigurable intelligent surface (UAV-RIS) is crucial in maintaining seamless connection in next-generation Internet of Things (IoT) networks. For this purpose, intelligent beamforming is essential to ensure secure data transmission from IoT devices to UAV-RIS and optimize communication while preventing adversarial attacks. This paper proposes a novel framework of federated learning for long short-term memory-based deep deterministic policy gradient with an attention mechanism (F-DDPG-AM). The proposed algorithm aims to improve security and mitigate potential threats in UAV-RIS-assisted IoT networks. The F-DDPG-AM combines the federated LSTM’s power to capture long-term dependencies in sequential data with the attention mechanism to focus on key network states and improve decision-making efficiency. The F-DDPG-AM framework improves learning efficiency, accelerates convergence, and enhances resilience against adversarial attacks by selectively prioritizing crucial network information and focusing on insecure scenarios. In addition, federated learning in the proposal ensures secure decision-making through local training for UAV-RIS-enabled IoT networks. The F-DDPG-AM enhances system scalability, trustworthiness, and compliance with secure machine learning principles by decentralizing the training process. The simulation results demonstrate the superior performance of the proposed F-DDPG-AM framework in defending against attacks, significantly outperforming traditional security approaches and other existing reinforcement learning models.