提出了基于信息熵的大规模网络流量异常检测方法。
This paper presents a new method of network-wide traffic anomaly detection.
本文采用一种累加模型将复杂大规模网络流量分解成趋势项、周期项和随机项。
In this paper, according to the characteristics of non linear network traffic, traffic behaviors are decomposed into trend items, period items, and random items by an accumulation decomposition model.
该系统采用分布式的架构，对于大规模网络流量的监测与分析提供了一个很好的支持平台。
The system, employing a distributed architecture, gives a quite well supporting platform for the measure and analysis of large-scale network traffic.
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。
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.
通过实验结果与小波分析结果的对比，证明了基于子空间方法的大规模网络流量异常检测是一种既简单又高效的方法。
Through the comparison of the results from the experiment and wavelet analysis, it shows that network-wide traffic anomaly detection based on subspace method is more simple and effective.
网络流量预测对大规模网络管理、规划、设计具有重要意义。
Traffic prediction has significant meanings for management, layout and design of largescale network.
本文针对大规模网络研究其态势感知方法，数据来源不限于入侵检测的报警记录，更偏重网络链接设备产生的网络流量记录，把觉察扩大到高速主干网。
Large-scale NSA is researched in this thesis, which not only uses alert records but tend to use NetFlow records to get awareness of the high speed backbone.
本文针对大规模网络研究其态势感知方法，数据来源不限于入侵检测的报警记录，更偏重网络链接设备产生的网络流量记录，把觉察扩大到高速主干网。
Large-scale NSA is researched in this thesis, which not only uses alert records but tend to use NetFlow records to get awareness of the high speed backbone.
应用推荐