提出了一个基于系统调用宏的异常检测模型。
A new anomaly detection model based on system call macro was presented.
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.
提出了大规模网络中一种基于相似度的异常检测模型。
Research on Dynamic Traffic Flow Forecasting Model and Algorithm in Large Scale Transportation Network;
在现有的单层马尔科夫链异常检测模型基础上，提出一种崭新的两层模型。
On the basis of the current single layer Markov chain anomaly detection model, this paper proposes a new two-layer model.
阐述了危险模式的概况及运行机制，提出了一种基于危险模式的异常检测模型以及相关的算法。
The general situation and running mechanism of danger theory are presented in this paper. After that we present an anomaly detection model based on danger theory, describe an interrelated algorithm.
该文对一种基于机器学习的用户行为异常检测模型进行了描述，在此基础上提出一种改进的检测模型。
This paper introduces an user behavior anomaly detection model based on machine learning originated mainly by Terran Lane. Then it presents an improved anomaly detection model.
本文提出了一种自动模型及其系统结构的有效实现，并用算法自动建立了在噪声数据上的异常检测模型。
An automatic model and its system architecture are presented, and an algorithm that automatically builds abnormally detecting models on noisy data is realized.
该文对一种基于机器学习的用户行为异常检测模型进行了描述 ，在此基础上提出一种 改进的检测模型 。
We are having a lot of teething troubles with the new machine, but when we have improved it, it should be the best of its kind.
进一步完善了异常判别中的统计模型，并根据异常检测模型给出数据描述和相应异常判别算法，最后总结了异常判别算法的优点和将来的工作。
Perfect stat. model in the stat. model and give data description and arithmetic according to distinguishing model, summarize advantage of the arithmetic and work that be done in future.
异常检测模型首先总结正常操作应该具有的特征，得出正常操作的模型，对后续的操作进行监视，一旦发现偏离正常统计学意义上的操作模式，即进行报警。
Anomaly detection model first summarizes the signatures normal operations which should have to educe models of normal operations, and then monitors the subsequent operations.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
针对网络入侵的不确定性导致异常检测系统误报率较高的不足，提出一种基于Q-学习算法的异常检测模型（QLADM）。 该模型把Q-学习、行为意图跟踪和入侵预测结合起来，可获得未知入侵行为的检测和响应。
To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).
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.
在使用DeclarativeTransaction模型时，容器将不会针对检测到的异常自动回滚事务。
The container will not automatically roll back a transaction on a checked exception when you use the Declarative Transaction model.
利用背景自动更新模型的背景减法，实现了智能运动检测功能，并进行异常情况告警和启动告警录像；
This system has implemented intelligent motion detection by utilizing a algorithm of difference image based on an automatically-updating background model.
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.
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.
在此基础上，以不定长模式作为基本单位构建了一个马尔可夫链模型来检测异常行为。
Then a Markov chain model is constructed based on variable-length patterns to detect abnormal behaviors.
本文针对不同的需求和应用，提出并研究了几种高效的异常检测方法和模型。
In this thesis, several efficient intrusion detection methods and models are proposed and investigated based on different requirements and applications.
本文提出的网络行为检测模型可以有效地帮助网管人员及时发现网络中的异常行为，为网络管理人员提供便利，具有较强的实用价值。
The detection model outlined in this paper would be able to help the network managers to find the anomaly behavior, which has high practical value.
由实验室模拟真实提取过程(含各种异常情况)，根据各过程参数反馈指标成分的含量变化，建立在线检测模型。
We simulate real extraction process in the laboratory (including various of abnormal conditions) and set up on-line testing model according to each process parameter index content changes feedback.
首先建立了系统运行的HMM，在此模型基础上提出了依赖于节点的异常检测算法。
We focus on the issues related to found a HMM for the behavior of system, and bring forward an algorithm of anomaly detection relied on node.
该模型既能进行滥用入侵检测，又能进行异常入侵检测。
This model not only can check abuse detection but also anomaly detection.
数据分析融合了异常检测和误用检测两种方法，提出了相应的检测模型，并引入了滤噪函数。
The data analysis integrates the two detection methods: anomaly and misuse, which provides corresponding detection models and introduces the noise filtering function.
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.
为降低虚警率，提出嵌入数据异常检测和恢复功能的BIT系统改进模型，并证明了该模型的有效性。
For decreasing FAR, an improved model for BIT systems with embedded abnormal data detecting and renewing functions was proposed, and the effectiveness of the proposed model was proved.
为降低虚警率，提出嵌入数据异常检测和恢复功能的BIT系统改进模型，并证明了该模型的有效性。
For decreasing FAR, an improved model for BIT systems with embedded abnormal data detecting and renewing functions was proposed, and the effectiveness of the proposed model was proved.
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