通过网络监测的协同抽样检测Wifi网络中的不良行为节点

M. Shanthi, S. Suresh
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引用次数: 1

摘要

提出了一种利用被动监测方法检测无线网络中自利节点的方法。这不需要访问网络节点。我们的方法需要在整个网络中部署多个嗅探器来捕获多个通道之间的无线流量跟踪。IEEE 802.11网络支持多通道,一个无线接口一次只能监控一个通道。因此,捕获通过所有通道上的接口的所有帧是不可能完成的任务,我们需要捕获最具代表性的样本的策略。当要监视大片区域时,必须部署几个嗅探器,这些嗅探器通常在其覆盖区域重叠。有效的无线监控的目标是捕获尽可能多的帧,同时最小化那些被多个嗅探器冗余捕获的帧的数量。上述目标可以通过协调采样策略来解决,该策略可以在任何时期将相邻嗅探器引导到不同的通道。然后使用隐马尔可夫模型对这些痕迹进行分析,以推断wifi网络中的不当行为节点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting misbehavior node in Wifi networks by co-ordinated sampling of network monitoring
We present an approach to detect a selfish node in a wireless network by passive monitoring. This does not require any access to the network nodes. Our approach requires deploying multiple sniffers across the network to capture wireless traffic traces among multiple channels. IEEE 802.11 networks support multiple channels, and a wireless interface can monitor only a single channel at one time. Thus, capturing all frames passing an interface on all channels is an impossible task, and we need strategies to capture the most representative sample. When a large area is to be monitored, several sniffers must be deployed, and these will typically overlap in their area of coverage. The goals of effective wireless monitoring are to capture as many frames as possible, while minimizing the number of those frames that are captured redundantly by more than one sniffer. The above goals May be addressed with a coordinated sampling strategy that directs neighboring sniffer to different channels during any period. These traces are then analyzed using hidden markov model to infer the misbehavior node in wifi networks.
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