A new anomaly detection model based on system call macro was presented.
提出了一个基于系统调用宏的异常检测模型。
A new two-layer Markov chains anomaly detection model that operated on system call traces was presented.
提出了一个两层马尔可夫链异常入侵检测模型。
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。
On the basis of the current single layer Markov chain anomaly detection model, this paper proposes a new two-layer model.
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。
Anomaly detection model first summarizes the signatures normal operations which should have to educe models of normal operations, and then monitors the subsequent operations.
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。
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.
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。
Monitoring program behavior is one of the highlighted research topics of host-based anomaly detection recently. The key is to construct a program behavior-based anomaly detection model.
监视程序行为是近年基于主机的异常入侵检测的研究热点，构建程序行为模型是进行异常检测的关键。
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.
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。
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).
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。
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.
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。
This model uses not only misuse but also anomaly detection technology, and at deployment the host based subsystem cooperates with the network-based subsystem.
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。
Moreover, it presents a model of intrusion detection system and strategies for detecting anomaly behaviors.
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。
It detect the anomaly mainly through establishing the normal behavior model database that anomaly detection method based on the procedure behavior.
基于程序行为的异常检测方法主要通过建立程序正常行为模式库来检测入侵。
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.
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。
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。
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。
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.
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。
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。
An Email flow anomaly detection method based on leaky integrate-and-fire model was presented for detecting flow anomaly in the process of mail worm propagation.
提出了一种基于带泄漏的积分触发测量方法的电予邮件蠕虫异常检测方法，用来检测邮件蠕虫在传播过程中的流量异常。
This paper proposes a circular spatial correlation model, which is more suitable for the application of WSN in anomaly detection.
本文随后提出了一种适合于无线传感器网络中异常事件监测应用的环状空间相关性模型。
This paper proposes a circular spatial correlation model, which is more suitable for the application of WSN in anomaly detection.
本文随后提出了一种适合于无线传感器网络中异常事件监测应用的环状空间相关性模型。
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