LLM-Driven APT Detection for 6G Wireless Networks: A Systematic Review and Taxonomy
Published in: IEEE Access
Aug 05, 2025
Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill thi...
Deep Reinforcement Learning-Based Joint Trajectory Design and Resource Allocation for Secure and Energy-Efficient UAV Networks
Published in: IEEE Open Journal of the Communications Society
Aug 01, 2025
Unmanned Aerial Vehicles (UAVs) have been extensively used recently for wireless networks. However, such networks encounter several challenges that remain unsolved. In this paper, we address the issue of joint optimization of trajectory design and resource allocation in UAV-based wireless networks in the presence of eavesdroppers. We first formulate an optimization problem with the objective to maximize a utility function defined in terms of secrecy rate, energy utilization efficiency, and inte...