A Security-driven Hierarchical Cognitive Fractional Routing Framework for Adaptive Flow Control Integrating Spiking Neural Inference and Metaheuristic DAG Optimization

The future network systems are endangered by the spike of malicious traffic caused by botnets, fuzzers, shellcodes, and large-scale exploit campaigns, impairing service availability and making communication infrastructures critical.Existing defense mechanisms often struggle to provide real-time adaptability and resilience against evolving DDoS attacks and large-scale service disruptions.Unlike existing studies that handle traffic classification, routing Optimization, and adaptive flow management one by one, the proposed HCFR framework mixes them in a single cognitive Optimization setup.It integrates sparse feature selection, a hierarchical SNN-based inference scheme, and security-aware fractional routing within a unified architecture.The architecture uses a dual-phase cognitive filtering system, where the first step detects malicious network flow patterns through sparse L1-norm-based feature selection, and the second applies hierarchical SNN-based pattern recognition for classification and traffic prioritization with temporal precision.The selected features steer fractional linear flow transformations on the optimized DAG topology, enabling dynamic rerouting of legitimate traffic during congestion or under adversarial disruption.This integrated design enables simultaneous enhancement of traffic intelligence, routing adaptability, and network resilience under adversarial conditions.In extensive simulations, the HCFR model significantly boosts malicious-flow detection accuracy, strengthens routing stability under DDoS-like conditions, and reduces packet loss via optimized fractional path allocation.Comparative analyses demonstrate that sparse feature selection, SNN inference, and Metaheuristic DAG Optimization enhance overall system robustness and adaptive network performance.Furthermore, the unified integration of intelligent traffic classification and adaptive routing Optimization improves operational efficiency and security of next-generation communication networks.In general, the proposed HCFR framework provides a secure and optimized architecture for future network systems that can adapt and handle current and upcoming threats.