Adaptive Threat Exposure Minimization within Virtualized Networks through Autonomous Game-Theoretic Misdirection Methods

Abstract

The rapid adoption of virtualized and cloud-native network infrastructures has significantly transformed modern communication ecosystems, particularly within fifth-generation (5G) and software-defined networking environments. While virtualization improves scalability, orchestration efficiency, and resource elasticity, it simultaneously enlarges the cyber attack surface through dynamic resource sharing, hypervisor abstraction, containerized workloads, and programmable control planes. Conventional perimeter-centric security frameworks are increasingly ineffective against adaptive adversaries capable of exploiting orchestration vulnerabilities, lateral movement opportunities, and distributed service dependencies. This paper proposes a comprehensive framework for adaptive threat exposure minimization within virtualized networks through autonomous game-theoretic misdirection methods. The study integrates cyber deception principles, reinforcement learning-assisted decision mechanisms, attack surface analytics, and strategic adversarial interaction modeling to reduce exploitable exposure in virtualized infrastructures.

The proposed architecture combines dynamic virtualization-layer deception, autonomous topology mutation, decoy orchestration, and probabilistic attacker engagement mechanisms. A multi-stage Stackelberg game model is developed to represent interactions between intelligent defenders and adaptive attackers within software-defined and cloud-native environments. The framework further incorporates reinforcement learning-driven deception adaptation to continuously optimize defensive responses under uncertain threat conditions. The methodology evaluates exposure reduction across virtualized radio access networks, cloud-native edge infrastructures, and software-defined transport architectures by considering latency constraints, orchestration overhead, deception persistence, and infrastructure resilience.

Results demonstrate that autonomous misdirection strategies significantly reduce successful attack propagation probabilities, minimize lateral exposure vectors, and improve service survivability without introducing prohibitive computational overhead. The findings indicate that integrating game-theoretic deception into virtualized network management frameworks enhances operational resilience and enables proactive threat containment. Furthermore, the study establishes that adaptive cyber deception strategies are particularly effective in distributed 5G infrastructures where dynamic orchestration and virtualization create rapidly changing attack conditions. The paper contributes a theoretical and operational foundation for intelligent exposure minimization in future autonomous networks while identifying limitations related to deception detectability, orchestration complexity, and adversarial learning adaptation.