Altering 5G Network Parameters Using Deep Reinforcement Learning to Optimize QoS and Security

As 5G networks rapidly expand to support higher data rates, lower latency, and increased device density, the associated security risks are also growing. That is why the deployment of 5G networks introduces significant challenges in balancing Quality of Service (QoS) and security, especially in dynamic, high-demand environments. This paper presents a framework leveraging deep reinforcement learning (DRL) to optimize this tradeoff based on our generated QoS_5G_Sec dataset. We evaluated various DRL agents, including Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C), focusing on their ability to manage network parameters in real-time. Among the tested methods, the DQN model tuned using the Optuna hyperparameter optimization technique demonstrated the best overall performance. Our results show that this approach provides a robust solution for dynamically adjusting network parameters, improving both QoS and security in 5G network architectures.