论文标题
反对移动恶意代理商的弹性共识
Resilient Consensus Against Mobile Malicious Agents
论文作者
论文摘要
本文解决了存在可能在网络内移动并引起攻击药物中的不良行为的对手存在的新的共识问题。通过采用来自计算机科学文献的几种移动对手模型，我们开发了可以减轻这种恶意药物影响的协议。该算法遵循均值降低的平均分序（MSR）算法的类别，根据该算法，代理在其状态更新中忽略了从邻居收到的可疑值。与静态对手模型不同，即使在对手移开之后，受感染的药物的价值可能仍然有缺陷，其效果必须考虑在内。我们为完整的图形和不完整的图案案例开发了网络结构的条件，在这些案例下，确保所提出的算法达到弹性共识。在随机图上进行了广泛的模拟，以验证系统中不确定性下的方法的有效性。
This paper addresses novel consensus problems in the presence of adversaries that can move within the network and induce faulty behaviors in the attacked agents. By adopting several mobile adversary models from the computer science literature, we develop protocols which can mitigate the influence of such malicious agents. The algorithms follow the class of mean subsequence reduced (MSR) algorithms, under which agents ignore the suspicious values received from neighbors during their state updates. Different from the static adversary models, even after the adversaries move away, the infected agents may remain faulty in their values, whose effects must be taken into account. We develop conditions on the network structures for both the complete and non-complete graph cases, under which the proposed algorithms are guaranteed to attain resilient consensus. Extensive simulations are carried out over random graphs to verify the effectiveness of our approach under uncertainties in the systems.
