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  trace
    Formal Analysis for Network Security Protocols Based on Trace Semantics
    基于语义的网络安全协议形式化分析
短句来源
    The consistently repeated system call sequences in normal process trace were regarded as macros, and then an anomaly detection model based on system call macros Markov chain was created.
    把正常程序行为产生的系统调用中大量有规律的重复出现的系统调用短序列看成一个个独立的基本单位(宏),并以宏为基本单位构建一个基于马尔可夫链的异常入侵检测模型。
短句来源
    Then the model of an Intrusion Detection System based on support vector machine is presented. An example using system call trace data, which is usually used in intrusion detection, is given to illustrate the performance of this model. Finally, comparison of detection ability between the above detection method and others is given.
    首先介绍入侵检测研究的发展概况和支持向量机的分类算法,接着提出了基于支持向量机的入侵检测模型,然后以系统调用执行(system call trace)这类常用的入侵检测数据为例,详细讨论了该模型的工作过程,最后将计算机仿真结果与其他检测方法进行了比较.
短句来源
    An example of using system call trace data,which is usually used in intrusion detection,is given to illustrate the performance of this model. Finally.
    然后以系统调用执行这类常用的入侵检测数据为例,验证该模型的工作效果,最后将计算机仿真结果与其他检测方法进行了比较。
短句来源
    This paper proposes a new simple trace semantics that can be used to specify security properties,this technique supports a protocol designer to provide formal analysis of the security properties.
    本文提出了一种新的简单的语义,用于刻画协议部分安全性质,即只针对协议规约的单个主体,此技术支持协议设计者对安全性质进行形式化规约。
短句来源
  “迹”译为未确定词的双语例句
    The abnormal detection using sequences of system calls can detect the behaviors like the U2R(User to root) and R2L(Remote to Local).
    基于主机系统执行的异常检测系统可以检测类似U2R和R2L这两类攻击。
短句来源
    In the experiments,the one-class classifier acquires 100 % detection rate and 4.9 % false alarm rate for sequences of system calls.
    通过对主机系统执行数据集的测试,试验获得了对异常样本接近100%的检测率,而误报警率为4.9%。
短句来源
    These two-layer Markov chains were used to construct the normal profiles of server process's activity, and then to detect anomaly.
    利用两层马尔可夫链模型,异常将在异常真正发生的局部处被检测出,即检测出的异常将被限制在相应的请求内,而不是整个系统调用
短句来源
    then the deviant estimate of the detect samples is given by the neural network and the Dempster Shafer evidence theory is used to fuse the result derived from the neural network at different times, so that the abnormality in the user behavior can be detected more efficiently.
    用神经网络扫描系统的审计得到的检测样本与用户特征轮廓进行比较,以两者的偏差作为证据,并结合证据理论来提高检测的正确率.
短句来源
    The consistently repeated system call sequences in normal process traces were regarded as macros, then a Markov chains anomaly detection model based on system call macros was constructed.
    把正常程序行为的系统调用中大量有规律的、重复出现的系统调用序列看作独立的宏,以宏为基本单位构建了一个马尔可夫链模型(MCM)来检测异常入侵.
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  trace
Using related sequences of Lucas numbers, other 3-manifolds are constructed, their geometric structures determined, and a curious relationship between the homology and the invariant trace-field examined.
      
Trace Inequalities, Maximal Inequalities, and Weighted Fourier Transform Estimates
      
We show that the trace of an indefinitely oscillating function on a subspace of ?d is not always indefinitely oscillating.
      
In the periodic case, the number of oscillations of the trace depends on the regularity of the function.
      
As applications, we obtain characterizations of the Besov and Triebel-Lizorkin spaces on Lipschitz curves, and the trace theorem on Γ of the Besov spaces onR2.
      
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An intrusion detection system model based on the neural network and evidence theory is discussed. First we use a neural network to capture the user behavior pattern and to create the user normal behavior profile; then the deviant estimate of the detect samples is given by the neural network and the Dempster Shafer evidence theory is used to fuse the result derived from the neural network at different times, so that the abnormality in the user behavior can be detected more efficiently.

讨论了利用神经网络设计识别用户异常行为的入侵检测系统的方案,即提取用户正常行为样本的特征来构造用户正常行为的特征轮廓;用神经网络扫描系统的审计得到的检测样本与用户特征轮廓进行比较,以两者的偏差作为证据,并结合证据理论来提高检测的正确率.

The generalizing ability of current IDS (intrusion detection system) is poor when given less priori knowledge. Utilizing SVM (support vector machines) in Intrusion Detection, the generalizing ability of IDS is still good when the sample size is small (less priori knowledge). First, the research progress of intrusion detection is recalled and algorithm of support vector machine taxonomy is introduced. Then the model of an Intrusion Detection System based on support vector machine is presented. An example using...

The generalizing ability of current IDS (intrusion detection system) is poor when given less priori knowledge. Utilizing SVM (support vector machines) in Intrusion Detection, the generalizing ability of IDS is still good when the sample size is small (less priori knowledge). First, the research progress of intrusion detection is recalled and algorithm of support vector machine taxonomy is introduced. Then the model of an Intrusion Detection System based on support vector machine is presented. An example using system call trace data, which is usually used in intrusion detection, is given to illustrate the performance of this model. Finally, comparison of detection ability between the above detection method and others is given. It is found that the IDS based on SVM needs less priori knowledge than other methods and can shorten the training time under the same detection performance condition.

目前的入侵检测系统存在着在先验知识较少的情况下推广能力差的问题.在入侵检测系统中应用支持向量机算法,使得入侵检测系统在小样本(先验知识少)的条件下仍然具有良好的推广能力.首先介绍入侵检测研究的发展概况和支持向量机的分类算法,接着提出了基于支持向量机的入侵检测模型,然后以系统调用执行(system call trace)这类常用的入侵检测数据为例,详细讨论了该模型的工作过程,最后将计算机仿真结果与其他检测方法进行了比较.通过实验和比较发现,基于支持向量机的入侵检测系统不但所需要的先验知识远远小于其他方法,而且当检测性能相同时,该系统的训练时间将会缩短.

The most common technology used in intrusion detection are anomaly detection technology and misuse detection technology, both of which are based on the mass prior knowledge. When given less prior knowledge, the performance of both techniques can not meet our need. Because SVM solves the small samples learning problem well, it will get good performance using SVM in intrusion detection, given less knowledge. A survey of intrusion detection is described and the basic concepts about SVM are introduced. The procedure...

The most common technology used in intrusion detection are anomaly detection technology and misuse detection technology, both of which are based on the mass prior knowledge. When given less prior knowledge, the performance of both techniques can not meet our need. Because SVM solves the small samples learning problem well, it will get good performance using SVM in intrusion detection, given less knowledge. A survey of intrusion detection is described and the basic concepts about SVM are introduced. The procedure of detection intrusions using SVM are discussed in detail. Finally, the experiment was performed on system call trace. The result shows that its performance is still good when given less prior knowledge.

介绍入侵检测的概况和支持向量机的基本概念和工作原理,提出了应用支持向量机进行异常入侵检测的工作过程,并以程序执行为数据源给出了应用支持向量机进行入侵检测的性能.该结果显示出在先验知识,即训练样本数少的条件下,该方法仍能达到较为满意的效果.

 
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