Deep Learning for Secure UAV-Assisted RIS Communication Networks
Umair Ahmad Mughal, Yazeed Alkhrijah, Ahmad Almadhor, Chau Yuen
Abstract
Reconfigurable intelligent surfaces (RIS) represent an important advancement in metamaterial technology, enabling the control of electromagnetic waves to enhance wireless communications. However, integrating RIS with unmanned aerial vehicles (UAVs) introduces potential vulnerabilities that can significantly impact network performance. This research investigates the complexity of securing UAV-assisted RIS systems for next-generation communication networks. We present a deep machine learning framework, Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG), to robustly address security concerns and ensure reliable communication within UAV-assisted RIS networks by countering malicious threats. Simulation results confirm the efficacy of combining UAVs, RIS, and deep learning to mitigate attacks on UAV-RIS communication, with notable improvements compared to other baseline approaches. Finally, we discuss open research challenges and future directions in this rapidly progressing field.