基于网络流量模型的异常检测是流量异常检测的一个重要研究方向。
Anomaly detection based on network traffic model is one of the important research directions in traffic anomaly detection.
接着对网络流量模型算法分析，简单介绍了泊松模型，马尔科夫模型，AR,MA,ARMA模型，重点分析了ARIMA模型算法。
Then algorithm analysis of network traffic model, a brief introduction of the Poisson model, Markov model, ar, MA, ARMA model, focused on analyzing ARIMA model algorithm.
然而近年来研究发现网络流量具有自相似性，传统的基于泊松分布的流量模型已不能很好地表述这一特征。
Recently much research shows that many kinds of network traffics are self-similar, which can't be described accurately by traditional traffic models based on Poisson distribution.
通过研究网络流量异常检测，提出一种新的基于自适应自回归(aar)模型的在线故障检测算法。
A novel online fault detection algorithm based on adaptive auto-regressive (AAR) model is proposed focusing on the anomaly detection of network traffic.
该模型具有减少网络流量、更有利于检索、可从多种不同的数据源集成数据、开放性、可扩充性、可重用性等特点。
The model has so many advantages as reducing network flows, easily retrieving, integrating data from many different data sources, opening, extending and replicating.
实验结果表明，该模型对网络流量的短期预测是有效可行的，并具有良好的收敛性和稳定性。
The experimental results prove that the model is efficient in network traffic prediction with good astringency and stability.
并对该流量控制方法进行了性能分析，与目前常用的漏桶法atm网络流量控制模型进行了性能比较。
The performance of this is analyzed, and compares the performance of this and normal leaky bucket (LB) in ATM network.
利用ARIMA模型法，在研究网络流量具有成长性、非平稳性的基础上得到了更为实用的结论。
In this paper, a practical conclusion has been arrived at that we can transform the nonstationary and growing network traffic to the domain of the stationary signal by the ARIMA modeling.
结合小波变换和人工神经网络的优势，建立一种网络流量预测的小波神经网络模型。
Integrating the merit of wavelet transform with that of artificial neural network, a wavelet neural network (WNN) model for forecasting network traffic was created.
对于网络流量的控制，首先需要了解流量特性并建立准确的流量模型。
We should analyze the characteristic of the internet traffic flow and build the model before we control it.
分析表明，该模型不仅可以模拟与网络实测数据相似的网络流量，而且具有一定的异常流量发现能力。
Analysis shows that this model can not only simulate network traffic corresponding to the real network but also detect anomaly traffic to some extent.
IP数据网络流量分析模型的研究一直是通信网络性能分析中一个及其重要的问题。
One of the focuses of any performance evaluation of IP data networks is their analytical Modeling of traffic source.
随着城域网流量的急剧增长，网络流量的特征发生了很大改变，因而网络流量的数学模型也需要随之改变。
MAN (Metropolitan Area Network) traffic has advanced by leaps and bounds, making its characteristics change drastically and requiring its mathematical model to change also accordingly.
研究了异步光分组交换网的流量特性，提出了网络流量的解析模型和近似模型。
The traffic characteristics of asynchronous optical packet switching networks are studied, the analytic and approximate models of network traffic are proposed.
文章考虑网络流量非线性的特点，通过不同的数学模型将流量时间序列分解成趋势成分、周期成分、突变成分和随机成分。
According to the character of non linear network traffic, the traffic time series is decomposed into trend component, period component, mutation component and random component.
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.
网络流量预测传统方法是利用流量的统计特性建立数学模型。
The traditional methods of network traffic prediction are to build mathematical models using its statistical characteristics.
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.
针对基于传统BP算法的前向神经网络预测网络流量方法的不足，构建了一种二级的网络流量预测-校正模型。
For the shortages of the method in which network flux is predicted by forward neural network based on traditional BP algorithm, a 2-level network flux predict-adjust model is constructed.
分析表明，该模型不仅可以模拟与网络实测数据相似的网络流量，而且具有一定的异常流量发现能力。
Analysis shows that this model can not only simulate network traffic corresponding to the real network but also detect ano...
最后，通过自适应边界值方法进行检测，能够及时发现异常流量行为，说明该模型应用于网络流量预测是可行、有效的。
Finally, abnormal behaviors of network traffic can be found on time through test of adaptive boundary value method, which proves that the model is feasible and effective.
研究了不准确网络信息下的流量优化。提出虚容量的概念，建立了基于本地状态信息的网络流量优化模型；
The traffic optimization under the inaccurate network information was discussed. Defining the virtual capacity, a model was made on the local state information;
基于自回归滑动平均模型（ARMA），利用时间序列建模，提出了利用组合模型对网络流量进行预测的方法。
From the discrete solution of the equation of vibration of engineering structure, the equalility of the neural network based time domain identification and the ARMA model was verified.
提出基于ARMA的网络流量实时在线预测模型，并对该预测模型进行了推导分析；
Purpose of the paper: The paper presents a real-time online prediction model for network traffic based on ARMA, deriving analysis of the prediction model is carried out;
提出基于ARMA的网络流量实时在线预测模型，并对该预测模型进行了推导分析；
Purpose of the paper: The paper presents a real-time online prediction model for network traffic based on ARMA, deriving analysis of the prediction model is carried out;
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