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Statistical en-route filtering of injected false data in sensor networks

机译:对传感器网络中注入的虚假数据进行统计途中过滤

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摘要

In a large-scale sensor network individual sensors are subject to security compromises. A compromised node can be used to inject bogus sensing reports. If undetected, these bogus reports would be forwarded to the data collection point (i.e., the sink). Such attacks by compromised nodes can result in not only false alarms but also the depletion of the finite amount of energy in a battery powered network. In this paper, we present a statistical en-route filtering (SEF) mechanism to detect and drop false reports during the forwarding process. Assuming that the same event can be detected by multiple sensors, in SEF each of the detecting sensors generates a keyed message authentication code (MAC) and multiple MACs are attached to the event report. As the report is forwarded, each node along the way verifies the correctness of the MAC's probabilistically and drops those with invalid MACs. SEF exploits the network scale to filter out false reports through collective decision-making by multiple detecting nodes and collective false detection by multiple forwarding nodes. We have evaluated SEF's feasibility and performance through analysis, simulation, and implementation. Our results show that SEF can be implemented efficiently in sensor nodes as small as Mica2. It can drop up to 70% of bogus reports injected by a compromised node within five hops, and reduce energy consumption by 65% or more in many cases.
机译:在大型传感器网络中,单个传感器会受到安全性的损害。受损节点可用于注入虚假感知报告。如果未被检测到,则这些伪造的报告将被转发到数据收集点(即接收器)。受损节点的这种攻击不仅会导致错误警报,而且还会耗尽电池供电网络中有限数量的能量。在本文中,我们提出了一种统计途中过滤(SEF)机制,以在转发过程中检测并丢弃虚假报告。假设可以由多个传感器检测到同一事件,则在SEF中,每个检测传感器都将生成密钥消息身份验证码(MAC),并且多个MAC附加到事件报告中。在转发报告时,过程中的每个节点都将概率性地验证MAC的正确性,并丢弃那些具有无效MAC的节点。 SEF利用网络规模,通过多个检测节点的集体决策和多个转发节点的集体虚假检测来过滤虚假报告。我们通过分析,模拟和实施评估了SEF的可行性和绩效。我们的结果表明,SEF可以有效地实现在Mica2之类的传感器节点中。它可以在五跳内丢弃多达70%的受感染节点注入的虚假报告,并在许多情况下将能耗降低65%或更多。

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