AI 中文总结
本文提出协同进化智能体威胁建模框架，将对手内生化，证明DC-ABM防御的拜占庭阈值特性，并揭示攻击集中于最不可替代分区，为无线网络提供可操作的攻击面缩减方法。
AI 中文摘要
无线与移动网络的威胁建模目前以静态的、目录驱动的方法为主导，这些方法预先固定对手，且从不询问攻击对实际部署的防御是否可行；基于智能体的韧性研究也存在同样的局限，即针对外生威胁概况优化防御者。我们转而将对手内生化。网络实体运行去中心化共识智能体模型（DC-ABM）防御，而自适应对手群体通过跨漏洞模式分区分配有限攻击预算来协同进化。均衡是一个可排序的、基于可行性的威胁模型，通过三个结构指标表达：简并加权路径鲁棒性、信任加权功能可替代性和鲁棒简并性。我们证明DC-ABM防御收缩到其拜占庭故障阈值以下，且由此产生的对手收益在该阈值处是凸的，因此涌现的攻击按优先级顺序驱动一部分分区达到故障。我们进一步证明，这种攻击集中在最不可替代的分区上，当诱导的损坏质量分数接近故障时严重性发散，且操作员可以在部署时沿着闭式连接性-可替代性交换率缩小攻击面。
英文摘要
Threat modelling for wireless and mobile networks is dominated by static, catalogue-driven methods that fix the adversary in advance and never ask whether an attack is feasible against the defence actually deployed; agent-based resilience studies share this limitation, optimising a defender against an exogenous threat profile. We instead endogenise the adversary. Network entities run a decentralised consensus agent-based model (DC-ABM) defence, while an adaptive adversary population co-evolves by allocating a bounded attack budget across vulnerability-mode partitions. The equilibrium is a rankable, feasibility-grounded threat model expressed through three structural metrics: degeneracy-weighted path robustness, trust-weighted functional substitutability, and robust degeneracy. We prove that the DC-ABM defence contracts below its Byzantine breakdown threshold and that the induced adversary payoff is convex against that threshold, so the emergent attack drives a subset of partitions to breakdown in priority order. We further prove that this attack concentrates on the least substitutable partitions, that severity diverges as the induced corrupted-mass fraction approaches breakdown, and that operators can shrink the attack surface at deployment time along a closed-form connectivity-substitutability exchange rate.
