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。
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。
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。
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。
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。
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