Resource-Aware Proximal Policy Optimization for Adaptive Intrusion Detection in Dynamic Networks

The growing sophistication of contemporary network infrastructures has increased the pressure on the smart and dynamic intrusion detection systems that can react to the dynamic cyber threats. The machine learning (ML) and deep learning (DL) methods have high classification rates, but they work with fixed decision boundaries which reduces their ability to adapt to dynamic traffic distributions and emerging attack patterns. In order to overcome these issues, the resource-aware Proximal Policy Optimization (PPO)-based adaptive multi-class intrusion detection system (IDS) is suggested in this study. The system characterizes intrusion detection as a sequential decision-making and incorporates computational resource measures into the reinforcement learning (RL) rewarding framework, which allows optimizing detection performance and operational efficiency at the same time. In the ensemble comparison, PPO-Model achieved the highest accuracy (99.4%), recall (98.6%), and Macro AUC (0.998), while reducing CPU utilization by 25.8% and memory consumption by 27.1% compared with the Stacking model. These results demonstrate that the proposed approach can improve detection performance while reducing computational resource requirements. The results suggest that next-generation intrusion detection in the dynamic network environment can be achieved with a scalable and robust solution based on the combination of RL and resource-aware optimization.