Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers. To tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network. We analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary. Our results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.

中文翻译：

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针对数据伪造攻击的弹性数据融合的社会学习。

物联网 (IoT) 存在易受攻击的传感器节点，这些节点可能会在物理或网络捕获后遭受数据伪造攻击。此外，集中决策和数据融合将决策点变成了单点故障，很可能被聪明的攻击者利用。为了应对这一严重的安全威胁，我们提出了一种新颖的方案，可以通过整个网络实现分布式决策和数据聚合。我们方案中的传感器节点遵循社会学习原则，类似于社交网络中的代理。我们分析检查在哪些条件下单个代理的本地动作可以通过网络传播，阐明注入虚假信息的拜占庭节点的影响。此外，我们展示了我们提出的算法如何保证高网络性能，即使在很大一部分节点已被对手入侵的情况下。我们的结果表明，社会学习原则非常适合设计强大的物联网传感器网络，并能够抵御数据伪造攻击。