Secure and Scalable Network Security Model Using Reinforcement Learning and Deep Learning Techniques

Advanced detection and mitigation strategies are required because network security encounters escalating difficulties from developing cyber threats. A new security framework utilizes DL and RL AI technologies to detect threats in real-time and adapt response functions. The model applies Convolutional Neural Networks (CNNs) for feature analysis needs together with Transformer encoders to process contextual data before implementing RL agents for time-sensitive decision processes. The CICIDS2017 and NSL-KDD datasets achieved 98.76% detection accuracy along with 1.12% false positive detection. The proposed solution manages to reduce response time under 125 milliseconds which shows superior performance over previous systems. The comparative analysis proves that the network solution provides scalability and strong resistance against varying network environments. Continuous threat analysis using artificial intelligence yields security resilience which successfully protects systems from present threats in real time. The Future Work will concentrate on enhancing model deployment performance in enterprise-scale networks because it will improve both efficiency and adaptation capabilities.