具有时间和结构密度的IP网络中角色识别
Identifying roles in an IP network with temporal and structural density
摘要：
IP流量的捕获包含关于非常不同类型的活动的许多信息，例如文件传输、用户与远程系统的交互、自动备份或分布式计算。识别这些活动对于适当的流量分析、建模和监控至关重要。我们在这里提出了一个概念的密度，捕捉时间和结构特征的相互作用，并概括了经典的概念的聚类系数。我们使用它来指出流量不同部分之间的重要差异，并根据网络中的角色来识别感兴趣的节点和节点组。
英文摘要：
Captures of IP traffic contain much information on very different kinds of activities like file transfers, users interacting with remote systems, automatic backups, or distributed computations. Identifying such activities is crucial for an appropriate analysis, modeling and monitoring of the traffic. We propose here a notion of density that captures both temporal and structural features of interactions, and generalizes the classical notion of clustering coefficient. We use it to point out important differences between distinct parts of the traffic, and to identify interesting nodes and groups of nodes in terms of roles in the network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
