Empowering Computer Network Security using K-Means Reinforcement Learning Resource Allocation (KRL-RA)

In today’s digital world, protecting network infrastructures from rapidly developing cyber threats is more important than ever. This research study focuses on introducing Artificial Intelligence-based approaches to optimize network security. It discusses how AI can enhance intrusion detection capabilities, traffic engineering policies, and resource allocation strategies. The vision is to build an innovative and effective network security architecture based on AI features that can predict attacks and enhance network performance through better resource management at high speeds. This research considers the inherent interdependence between traffic engineering and resource allocation in improving network performance. The research study improved the initial model of the k-mean clustering algorithm and combined it with other ML network security algorithms like Q-learning/Reinforcement Learning to give final proposed KRL-RA model. Various existing models of intrusion detection, network security, and resource allocation, are discussed and compared them to the improved version of the proposed KRL-RA model, which uses Machine Learning to improve its efficiency and reliability using past records. It is demonstrated with two different practical cases. Through iterative learning, the system obtains a better understanding of the network environment, leading to a greater intrusion detection success rate.