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.
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.
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).
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.
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。
Network traffic burst anomaly is the significant abnormal changes in the network traffic.
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.
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.
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.
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.
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.
首先对网络流量进行高频统计，然后对其相邻时刻进行相似度分析，根据相似度的变化来发现异常。
First, we focus on the high frequency statistics result, then analyze its likeness of adjacent time and detect the abnormality.
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.
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.
另一方面需要对网络流量进行更好的特征分析，并监控异常流量和网络攻击行为，提高网络安全性。
On the other hand we should diagnostically analyse the network traffic and monitor the abnormal traffic and attacks of network to improve the network security.
基于对真实网络流量数据的观察，在突发异常的定义中引入持续性因子及突变性因子，以更好地描述其在真实应用环境中的特征。
Based on the observation of real network traffic data, we introduce the lasting factor and abrupt factor in the definition of burst in order to better characterize the burst in the real application.
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.
网络流量能准确反映工业以太网的状况，同时异常流量也会对网络造成影响。
The network traffic characteristic shows the working status of the industrial Ethernet accurately, and abnormal traffic will also influence the network running performance largely.
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。
Detecting the network traffic burst anomaly is with great meaning to locate the anomaly in time and response subsequently.
在分析影响服务可用性网络攻击导致网络流量异常改变的基础上，提出了一种主机网络实时流量的安全状况评估方法。
Analysis of the impact of service availability in the network attacks lead to abnormal changes in network traffic, based on a host of real-time network flow method to assess the security situation.
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 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.
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...
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...
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