Design of An Improved Reinforced Transformer Architecture for Real-Time Policy Optimization in Intrusion Resilient Networks

Current Computer networks face increasingly varied behavior patterns and are often evaded.These scenarios arise especially in Software Defined Networks (SDN), where centralization enables fine-grained policy enforcement, creating opportunities for delay in mitigation.Most existing SDN security mechanisms either suffer from static rule sets, limited adaptation, or a lack of contextual decision-making, resulting in latency, false positives, and drift in real-time scenarios.This paper proposes a Reinforced Transformer Architecture for realtime policy adaptation in intrusion-aware SDN controllers.It comprises five analytically novel modules.Temporal Graph Transformer Encoding (TGT-EFRE) captures flow-rule transitions over time via graph positional attention.Multi-Resolution Spatio-Temporal Attention predicts packet trajectories to preempt deployment of rules.Adaptive Reward-Gradient Signal Modeling (ARGRSM) aligns reinforcement learning signals according to intrusion severity for faster policy convergence.Adversarial Intrusion Emulation using GANs (AIE-GAN) will stress-test the policy layer against synthetic zero-day attacks.Causal Influence-Aware Policy Mapping (CIAPDM) captures explainable control decisions via tracing intrusion-policy causality.Empirical evaluations demonstrate significant improvements: latency reduced to 35.4 ms; convergence accelerated by 49%; the false positive rates cut by 54%; and GAN robustness exceeding 91.3%, all under real-time network constraints.These results establish the proposed model