A network flow data sampling method based on history memory 一种基于历史记录的网络流量数据采样方法
Provides access to network traffic data , network address information , and notification of address changes for the local computer -提供对网络流量数据网络地址信息和本地计算机的地址更改通知的访问。
It is perfect applicable on transport little data flow continually , such as vehicles attempter , security , navigation , monitor 特别适用于需频繁传送小流量数据的应用,如车辆调度、安全、导航、监控、监测等领域。
It can make the collection system of network usage more dependent from the billing system , and each model of the billing system becomes more dependently from others Ipdr使得计费系统可以将流量数据的收集分离出来,使得计费系统的各个系统之间更加独立。
The process of the background traffic modeling is described in chapter 3 in detail , and solves admirably the technology problem about the interface of traffic data and opnet software 第三章详尽地叙述了模型的建立过程,极好地解决了流量数据输入时与网络仿真软件opnet接口的技术问题。
Caida collects , monitors , analyzes , and visualizes several forms of internet traffic data concerning network topology , workload characterization , performance , routing , and multicast behavior Caida收集、监控、分析和可视化以下几种互联网流量数据:关于网络拓扑、工作量特性、性能、路由和多播行为。
With a large amount of real traffic data collected from the actual network , a nonlinear network traffic model based on artificial neural network ( ann ) theory was constructed to predict the network traffic 根据实际网络中测量的大量网络流量数据,建立一个时间相关的基于神经网络的流量模型,预测和分析网络流量状况。
Based on the observation of real network traffic data , we introduce the lasting factor and abrupt factor in the definition of burst in order to better characterize the burst in the real application 基于对真实网络流量数据的观察,在突发异常的定义中引入持续性因子及突变性因子,以更好地描述其在真实应用环境中的特征。
Ipdr brings out standard data interface between systems , which include network application support systems , mediation systems and network elements . ipdr - based network usage record is of great benefit to billing system Ipdr顺应时代的发展而诞生,它为计费系统带来了福音,利用ipdr标准组织使用数据,使得计费流量数据具有标准通用的格式。
This separation can be performed effectively by both marginal distribution and residuals analysis of parameters for anomalous component . experimental result shows the method can deal with non - stationary traffic data , so anomaly detection of real network traffic is implemented 实验结果表明,由于不必将整个时间序列进行分片和单独拟合,算法可以直接处理非平稳流量数据,实现了真正意义上的网络流异常检测功能。