Two approaches to anomaly detection of program behaviors are presented which both use a Markov process to represent the normal behavior profile of a program.
研究了马尔可夫过程在程序行为异常检测中的应用，提出两种新的检测方法。
A analysis system must adaptive the changes in normal user behavior patterns and update normal profile to reduce the false alert.
正常用户行为活动是随时间变化的，一个异常分析系统要能适应这种变化更新正常行为模型，避免误报警。
It USES agents to build a profile of an application's normal behavior based on the code paths of a running program, then continually monitors those code paths for deviations from the norm.
它采用代理来建立应用程序正常行为剖析，而该剖析是基于运行中程序的代码路径，然后连续监视代码路径，看看有没有偏离。
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