Slicing Under Siege: Adversarial-Aware Optimization for Secure Resource Allocation in B5G Networks

The prospect of ultra-dynamic and tailored network slicing in the era of Beyond $\mathbf{5 G}$ (B5G) is associated with the potential to make networks more vulnerable to stealthy adversarial actions. Attackers may impersonate authorised end users, saturate virtual slices, or take advantage of resource gaps at the expense of service availability, quality and user trust. This study presents a novel approach, Secure Intelligent Enforcement of Guaranteed Efficiency (SIEGE), which is a strategic defence of scarce resources that are under siege by optimising resource allocation with adversarial awareness in the core of the defence. SIEGE is created through the use of an Integer Linear Programming (ILP) and is capable of identifying and blocking malicious usage by users without impacting the performance of benign users. The model proposes per-user behavioural metrics ($p_{u}, q_{u}$), which measure slice/resource exposure, and applies them in an optimisation target that is resilience-aware. Early indicators suggest a maximum of $43.7 \%$ reduction in resource hijacking cases, while service-level agreements remain intact. The research is continuing to provide the foundation to a next-generation secure slicing paradigm, which is proactive, explainable and scalable. SIEGE provides a roadmap of security-by-design wireless infrastructure and a bridge to self-protecting, smart B5G networks.