并通过实验，验证了本文所提出的流量分类与异常流量检测方法的实时性和有效性。
What's more, writer has validated the real-time and effectiveness of the classification of traffic and anomaly detection methods by experiment.
为了解决该问题，提出一种基于全局流量异常相关分析的检测方法。
To solve this problem, a detection method based on global abnormal correlation analysis was proposed.
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.
提出了一种基于支持向量机的网络流量异常检测方法。
A network traffic anomaly detection mechanism is presented based on support vector machine (SVM).
基于序贯频繁模式挖掘，提出并实现了一种宏观网络流量异常检测的方法。
This paper presents and implements a macro-network traffic anomaly detection strategy based on sequential frequent pattern mining.
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.
本文研究了一种新的流量异常检测方法。
In this paper, a new method to detect traffic anomaly was studied.
流量异常检测模块的目的是完成具体的检测任务，它也是检测系统的核心部分。
The aim of the traffic abnormality detecting module is to accomplish detecting tasks, the kernel of the detecting system.
设计的网路流量突发异常检测原型系统能够报告持续性突发的出现时间范围、平均聚集值，突变性突发的发生时间、峰值。
The network traffic burst detection archetype system can report the time range and aggregate mean value for the lasting bursts, break time point and peak value for abrupt bursts.
针对传统检测方法存在的问题，提出了一种新型的网络流量异常检测方法。
This paper presents a new method of network traffic abnormity detection in light with the difficulties in traditional procedure.
在研究分析了几种网络流量异常检测算法的基础上，提出了一种改进的广义似然估计（IGLR）的检测算法。
On the basis of studying the algorithms of network traffic abnormality detection, an improved Generalized Likelihood Ratio (IGLR) algorithm is proposed.
通常，在网络流量管理中使用阈值来检测流量异常。
In general, the traffic anomaly is detected using a threshold in network traffic management.
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.
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.
将网络流量分解到不同的频段，根据高频段频谱能量，即小波方差的变化对网络流量异常进行检测。
Network traffic is broken down into different frequency, and anomaly change of network traffic is detected through the high-frequency power analysis, that is the change of wavelet variance.
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.
异常检测在多个MA上采集并分析流量数据，在统计服务器上检测流量的异常特征。
Anomaly detection collects and analyses traffic data from many MAs, then detects the anomaly signature by SS.
一个是对异常数据包的检测，另一个是对异常网络流量来进行检测。
One is analysis abnormal packet, the other is analysis abnormal network flow.
一方面是对异常数据包的检测，另一方面是对异常网络流量来进行检测。
One is analysis abnormal packets, the other is analysis abnormal network flow.
流量异常检测，作为一种网络入侵检测的方法，存在着如何建立正常行为模型的难题。
It is always a difficult problem to erect a model of normal behaviors in the area of network traffic anomaly detection, a method of network intrusion detection.
提出了一种基于带泄漏的积分触发测量方法的电予邮件蠕虫异常检测方法，用来检测邮件蠕虫在传播过程中的流量异常。
An Email flow anomaly detection method based on leaky integrate-and-fire model was presented for detecting flow anomaly in the process of mail worm propagation.
提出了一种基于带泄漏的积分触发测量方法的电予邮件蠕虫异常检测方法，用来检测邮件蠕虫在传播过程中的流量异常。
An Email flow anomaly detection method based on leaky integrate-and-fire model was presented for detecting flow anomaly in the process of mail worm propagation.
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