针对网络异常检测虚警率偏高的问题，提出了一种基于属性相似度云模型的网络异常检测新方法。
A new method for the anomaly detection based on the attributes similarity and the cloud model was proposed to alleviate the high false positive rate problem in the detection.
实验结果表明，基于相对熵理论的多测度网络异常检测方法对于多种攻击的平均检测率达到83.5%。
Experimental result shows that the average detection rate of RETMMAD can reach to 83.5%. Considering for practicability of the RETMMAD, arriving at a decision for the threshold is often difficult.
本文描述了一个基于相关特征矩阵和神经网络的异常检测方法。
This article presents a anomaly detection method based on correlation eigen matrix and neural network.
在系统中，既综合了基于异常行为的入侵检测和基于特征的入侵检测技术，在配置上又采用了主机配置和网络配置相互配合的方式。
In the system, apply the Intrusion detection technique of the based on unusual behavior and signature-based, and adopt the way of host and network configuration cooperating each other.
实验结果表明，该模型可以较好地检测异常的网络数据包，具有较好的自适应性。
Results show that this model can detect abnormal data packets well, and has a better self adaptability.
提出了大规模网络中一种基于相似度的异常检测模型。
An anomaly detection model based on the multi-feature similarity in large-scale network is proposed in this paper.
文章描述了一个基于相关特征矩阵和神经网络的异常检测方法。
This article presents an anomaly detection method based on correlation eigen matrix and neural network.
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).
该系统模型既综合了基于异常行为的入侵检测和基于特征的入侵检测技术，在配置上又采用主机配置和网络配置相互配合的方式。
This model uses not only misuse but also anomaly detection technology, and at deployment the host based subsystem cooperates with the network-based subsystem.
实时异常检测是目前网络安全的研究热点。
Real-Time anomaly detection is a highlighted topic of network security research in recent years.
我们提出了建模误差和检测网络异常的方法。
We propose methods for modeling errors and detect network anomalies.
其思想是通过将网络审计数据转化为时序数据库，对其进行序列模式挖掘以提炼出用户行为模式，并由此进行异常检测。
The idea is to transform the net audit data into time series database and mine the sequence pattern to extract the user behavior pattern , and then to use behavior pattern in anomaly detection.
其中规则库中包含正常行为规则和异常行为规则，使得原型系统在理论上既可实现误用检测也可实现异常检测，并采用关联规则挖掘模块对网络连接数据进行处理。
The rule sets of the system include normal behavior rules and abnormal behavior rules, it make the system can carry out the anomaly detection and misuse detection in theory.
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.
给出了针对无线网络的入侵检测模型和网络异常行为检测策略。
Moreover, it presents a model of intrusion detection system and strategies for detecting anomaly behaviors.
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.
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.
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.
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.
对异常检测，论述了常见的网络异常并设计异常处理模块。
In Anomaly Mode, we discussed network anomalies and designed modules to detect network anomalies.
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.
入侵检测技术从原理上分为异常检测和误用检测，从检测内容上分为主机入侵检测和网络入侵检测技术。
On principle, Intrusion detection technology is made up of abnormal detection and musing detection and by the detected content, it includes host detection and network detection.
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.
在网络入侵检测算法方面，本文对异常和误用检测算法进行了研究。
On the aspect of network intrusion detection algorithm, the thesis studies the misuse detection algorithm and anomaly detection algorithm.
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.
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.
因此如何在大规模网络环境下检测网络异常并为应急响应人员及时提供预警信息是目前亟待解决的问题。
It's an urgent problem to solve that how to check abnormal network data in order to provide information to incident response person.
将支持向量机应用于网络入侵检测，提出一种基于支持向量机的网络异常入侵检测模型。
Apply SVM technique to network intrusion detection, and propose a network abnormal intrusion detection model based on SVM.
而异常检测模块，它采用基于统计分析模型检测“异常”的网络行为。
But anomaly detection USES based-on statistic analyzed model detection "anomaly" network actions.
而异常检测模块，它采用基于统计分析模型检测“异常”的网络行为。
But anomaly detection USES based-on statistic analyzed model detection "anomaly" network actions.
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