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Device and network classification based on probabilistic model

机译:基于概率模型的设备和网络分类

摘要

Programmatic mechanisms that enable the automatic assignment of categories to network entities based on observed evidence. Agents gather observation data that identifies observations made by agents about the network and a plurality of nodes of the network. The agents provide the observation data to a classification module, which assigns a device category to the nodes of the network based on the observation data and a probabilistic node model. The probabilistic node model considers several probabilities to ascertain a recommended device category for a particular node, such as probabilities based on a manufacturer of a node, an operating system executing on a node, information about other nodes in the local vicinity of a node, and an administrator web page associated with a node. The classification module may also assign a particular network category to the network based on the observation data and a probabilistic network model.
机译:通过观察证据可以自动将类别自动分配给网络实体的程序化机制。 代理收集观察数据,该数据识别由关于网络的代理和网络的多个节点所产生的观察。 代理将观察数据提供给分类模块,该分类模块基于观察数据和概率节点模型为网络的节点分配设备类别。 概率节点模型考虑了几种概率来确定特定节点的推荐设备类别,例如基于节点的制造商的概率,在节点上执行的操作系统,关于节点本地附近的其他节点的信息,以及 与节点关联的管理员网页。 分类模块还可以基于观察数据和概率网络模型为网络分配给网络。

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