防御从众驱动型拜占庭攻击的无人机集群协同感知安全
Securing Cooperative Sensing in UAV Swarms Against Conformity-Driven Byzantine Attacks
AI总结：
针对6G无人机集群从众驱动型拜占庭攻击问题，提出融合演化博弈论与MAP估计的鲁棒融合框架，大幅提升态势推断准确率，优于多数投票等传统方法。
AI中文摘要：
在支持集成感知与通信（ISAC）的6G无人机（UAV）集群网络中，广泛采用的基于模仿的从众协作机制可被拜占庭攻击者利用以伪造虚假共识，导致正常无人机的有效错误概率动态演化且远超其固有感知误差，使基于独立性假设构建的传统融合方法失效。本文提出一种感知从众的拜占庭鲁棒融合框架，将演化博弈论与最大后验概率（MAP）估计相结合。首先，正常无人机的策略更新以有限理性观点动态为特征，在死亡-出生更新规则下推导了错误信息率的演化动态及其演化稳定状态（ESS）。随后确立三项理论结果：在节点异质感知误差下，零阶ESS仅通过均值依赖于误差分布；获得了针对ESS的闭式一阶弱选择修正，以及对任意选择强度均有效的精确平均场不动点；研究揭示，当且仅当攻击概率超过二分之一时，集群层面的错误信息可压倒多数，且该阈值与感知误差及恶意节点比例均无关。将预测的误差动态嵌入节点级MAP规则后，所得到的融合机制在不同网络拓扑、攻击强度、网络规模及感知误差分布下实现近100%的态势推断准确率，且在±20%参数失配时保持准确率高于99%。相比之下，多数投票、声誉加权及独立融合方法在越过多数翻转阈值后完全失效。
英文摘要：
In integrated sensing and communication (ISAC) enabled 6G unmanned aerial vehicle (UAV) swarm networks, the widely adopted imitation-based conformity cooperation mechanism can be exploited by Byzantine attackers to fabricate false consensus, causing the effective error probability of normal UAVs to evolve dynamically and far exceed their inherent sensing errors, which invalidates conventional fusion methods built on the independence assumption. This paper proposes a conformity-aware Byzantine-resilient fusion framework that couples evolutionary game theory with maximum a posteriori (MAP) estimation. First, the strategy updates of normal UAVs are characterized by bounded-rational opinion dynamics, and the evolution dynamics of the misinformation ratio together with its evolutionarily stable state (ESS) are derived under death birth updating. Three theoretical results are then established: under heterogeneous per-node sensing errors, the zeroth-order ESS depends on the error distribution only through its mean; a closed-form first-order weak-selection correction to the ESS is obtained, together with an exact mean-field fixed point valid for arbitrary selection intensity; and it is revealed that swarm level misinformation can overwhelm the majority if and only if the attack probability exceeds one half, with this threshold independent of both the sensing error and the malicious ratio. Embedding the predicted error dynamics into a per-node MAP rule, the resulting fusion mechanism achieves nearly 100% situation-inference accuracy under different network topologies, attack intensities, network scales, and sensing-error distributions, and maintains accuracy above 99% under +-20% parameter mismatch. In contrast, majority voting, reputation weighting, and independent fusion collapse completely once the majority-flip threshold is crossed.
