TOWARD SDN-NATIVE CYBERSECURITY: UNIFIED THREAT MODELING, FORMAL ASSURANCE, BEHAVIORAL DETECTION, AND MULTI-CONTROLLER RESILIENCE

Abstract

Software-defined networking (SDN) improves programmability and global policy control, but it also concentrates authority, exposes high-impact interfaces, and couples security to rapidly changing network state. Existing work addresses threat taxonomy, formal verification, behavioral detection, distributed control, and threat-informed defense largely as separate concerns. This study designs and analytically evaluates an SDN-native cybersecurity integration contract that unifies: a multidimensional threat model; invariant-based preventive assurance; governed hybrid detection; security-aware multi-controller resilience; ATT&CK informed traceability; and controlled learning. The framework specifies typed evidence exchanges across Observe, Verify, Detect, Orchestrate, Respond, and Learn, with explicit provenance, freshness,confidence, authority, rollback, and post response verification obligations.Analytical requirement mapping and scenario walk throughs show coherent coverage of representative attack paths under stated assumptions and expose residual uncertainties and empirical obligations. The contribution is conceptual and methodological: it does not establish runtime feasibility, quantitative superiority, classifier accuracy, Byzantine tolerance, or deployment readiness. To make subsequent validation reproducible and falsifiable, the paper defines coverage, overhead, resilience, false positive, and recovery measures together with a versioned cyber range instantiation blueprint.