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A Dynamic En-route Scheme for Filtering False Data Injection in Wireless Sensor Networks

机译:一种用于过滤无线传感器网络中虚假数据喷射的动态挖掘方案

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In this paper, we propose a dynamic en-route filtering scheme for false data injection attacks in wireless sensor networks. In sensor networks, adversaries can inject false data reports containing bogus sensor readings or nonexistent events from compromised nodes. Such attacks may not only cause false alarms, but also drain out the limited energy of sensor nodes. Several existing schemes for filtering false reports either cannot deal with dynamic topology of sensor networks or have limited filtering capacity. In our scheme, a legitimate report is endorsed by multiple sensing nodes using their own authentication keys generated from one-way hash chains. Cluster head uses Hill Climbing approach to disseminate the authentication keys of sensing nodes to the forwarding nodes along multiple paths toward the base station. Hill Climbing guarantees that the forwarding nodes closer to a cluster hold more authentication keys for the cluster than those nodes farther from it do, hence, the number of keys held by each forwarding node can be balanced. In filtering phase, each forwarding node validates the authenticity of the reports and drops those false ones. Compared to existing schemes, our scheme can better deal with dynamic topology of sensor networks. Analytical and simulation results show that our scheme drops false reports earlier even with a lower memory requirement and tolerates more compromised nodes. Our scheme also outperforms others in term of energy efficiency, especially for large sensor networks.
机译:在本文中,我们提出了一种用于无线传感器网络中的虚假数据注入攻击的动态集管路滤波方案。在传感器网络中,对手可以注入包含来自受损节点的虚假传感器读数或不存在的事件的错误数据报告。这种攻击可能不仅会导致错误警报,而且还可以排出传感器节点的有限能量。过滤错误报告的几个现有方案不能处理传感器网络的动态拓扑或有限的过滤容量。在我们的计划中,使用从单向散列链生成的自己的身份验证键来认可,通过多个传感节点核准了合法的报告。群集头使用爬山方法沿着向基站的多个路径传播对传感节点的认证键。 Hill Climbing保证,转发节点更靠近群集的群集比那些远离其所做的节点的群集更验证键,因此可以平衡每个转发节点所持的键数。在过滤阶段,每个转发节点验证报告的真实性并丢弃那些错误的。与现有方案相比,我们的计划可以更好地处理传感器网络的动态拓扑。分析和仿真结果表明,即使内存要求较低,我们的计划也会提前删除假报告,并容忍更受影响的节点。我们的计划在能效期间也优于其他人,特别是对于大型传感器网络。

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