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Forensic Analysis of Packet Losses in Wireless Networks

机译:无线网络中丢包的法医分析

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Due to the lossy nature of wireless links, it is difficult to determine if packet losses are due to wireless-induced effects or from malicious discarding. Many prior efforts on detecting malicious packet drops rely on evidence collected via passive monitoring by neighbor nodes. However, they do not analyze the cause of packet losses. In this paper, we ask: 1) Given certain macroscopic parameters of the network (like traffic intensity and node density) what is the likelihood that evidence exists with respect to a transmission? 2) How can these parameters be used to perform a forensic analysis of the reason for the losses? Toward answering the above questions, we first build an analytical framework that computes the likelihood that evidence (we call this transmission evidence, or TE for short) exists with respect to transmissions, in terms of a set of network parameters. We validate our analytical framework via both simulations as well as real-world experiments on two different wireless testbeds. The analytical framework is then used as a basis for a protocol within a forensic analyzer to assess the cause of packet losses and determine the likelihood of forwarding misbehaviors. Through simulations, we find that our assessments are close to the ground truth in all examined cases, with an average deviation of 2.3% from the ground truth and a worst case deviation of 15.0%.
机译:由于无线链路的损耗性质,很难确定丢包是由于无线引起的影响还是恶意丢弃引起的。在检测恶意数据包丢弃方面,许多先前的努力都依赖于邻居节点通过被动监视收集的证据。但是,他们没有分析数据包丢失的原因。在本文中,我们问:1)给定网络的某些宏观参数(例如流量强度和节点密度),关于传输存在证据的可能性是多少? 2)如何使用这些参数对损失原因进行法医分析?为了回答上述问题,我们首先构建一个分析框架,该分析框架根据一组网络参数计算关于传输的证据(我们称为传输证据,简称TE)存在的可能性。我们通过在两个不同的无线测试台上进行的仿真和实际实验来验证我们的分析框架。然后,将分析框架用作法医分析器中协议的基础,以评估数据包丢失的原因并确定转发不良行为的可能性。通过模拟,我们发现在所有检查的案例中,我们的评估都接近于基本事实,与基本事实的平均偏差为2.3%,最坏情况下的偏差为15.0%。

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