An Internet user could predict the future status based on the link bandwidth and the local traffic, and then control his own network behavior.
用户根据探测的链路带宽和本地网络的业务状态，对未来状态进行预测，并主动控制自己的网络行为。
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。
Based on statistics character of traffic in a large-scale network, the steady metrics that can estimated network behavior are found and a sampling measurement model is presented in this paper.
基于大规模网络流量的统计特征，寻找能够评价网络行为的稳定测度，并建立抽样测量模型。
The host-based integrity detection technology as well as the user behavior management software based on network traffic analysis technology had been basically mature and used for commercial purposes.
其中，基于主机完整性检测技术以及基于网络流量分析技术的用户行为管理软件已经基本成熟并用于商业用途。
Firstly, this thesis introduces the basic concept and correlative theories of network traffic and network behavior subject.
文章首先介绍了网络流量和网络行为学的基本概念以及相关理论。
Predicting the behavior of network traffic is very important for admission management and congestion control in the communication network.
预测网络业务的行为在通信网络的接入管理和拥塞控制等方面有着重要的意义。
In the thesis, we also introduce the self-similar property of modern network traffic and discuss its effects on the system's behavior and performance.
本文还讨论了现代网络交通的自相似特性和其对系统性能的影响。
By analysis of network traffic (packets), frequent user behavior profiles are mined, and then by comparing the profile similarity, system behavior can be detected in real-time.
通过对网络数据包的分析，挖掘出网络系统中频繁发生的行为模式，并运用模式相似度比较对系统的行为进行检测，进而自动建立异常和误用行为的模式库。
By analysis of network traffic (packets), frequent user behavior profiles are mined, and then by comparing the profile similarity, system behavior can be detected in real-time.
通过对网络数据包的分析，挖掘出网络系统中频繁发生的行为模式，并运用模式相似度比较对系统的行为进行检测，进而自动建立异常和误用行为的模式库。
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