A network traffic anomaly detection mechanism is presented based on support vector machine (SVM).
提出了一种基于支持向量机的网络流量异常检测方法。
This paper presents and implements a macro-network traffic anomaly detection strategy based on sequential frequent pattern mining.
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。
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.
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。
Network traffic anomaly refers to the status that traffic behaviors depart from the normal behaviors, which has characteristics of a sudden attack and the unknown threatened characteristics.
网络流量异常是指网络的流量行为偏离其正常行为的情形，具有发作突然、先兆特征未知的特点，有可能在短时间内给网络及其设备带来极大的伤害。
Network traffic burst anomaly is the significant abnormal changes in the network traffic.
网络流量突发异常是指网络业务流量突然出现的不正常的重大变化。
A novel online fault detection algorithm based on adaptive auto-regressive (AAR) model is proposed focusing on the anomaly detection of network traffic.
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。
Analysis shows that this model can not only simulate network traffic corresponding to the real network but also detect anomaly traffic to some extent.
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。
Anomaly detection based on network traffic model is one of the important research directions in traffic anomaly detection.
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。
In general, the traffic anomaly is detected using a threshold in network traffic management.
通常，在网络流量管理中使用阈值来检测流量异常。
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.
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。
Detecting the network traffic burst anomaly is with great meaning to locate the anomaly in time and response subsequently.
及时发现网络流量的突发异常变化对于快速定位异常、采取后续相应措施具有重要意义。
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.
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。
The anomaly detection algorithms of the large scale network(LSN) were required to analysis the vast network traffic of G bit level in real-time and on-the-fly.
随着网络规模和速度的增加，大规模网络异常发现要求检测算法能够在无保留状态或者少保留状态下对G比特级的海量网络业务量数据进行实时在线分析。
This paper presents a new method of network-wide traffic anomaly detection.
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.
提出了基于信息熵的大规模网络流量异常检测方法。
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