本文在基于数据挖掘的网络入侵检测系统框架基础上设计了一个无导师学习的分析器模型。
Based on the framework of network intrusion detection systems based on data mining, this paper devises an analyzer model of unsupervised learning.
在入侵检测系统框架的基础上，通过建立模式匹配的模型特征库和描述系统的流程来说明入侵检测系统的原理。
Through setting up the model characteristic storehouse and describing the systematic procedure, this paper states its principle based on the description of the frame of Intrusion Detection system.
该系统基于公共入侵检测框架（CIDF）构建，当出现新攻击时，利用数据挖掘对海量数据进行挖掘，得出入侵模型后由系统自动转换为检测规则以实现规则库的自动更新。
This framework is based on CIDF, and uses Data Mining to mine intrusion models, then automatically transforms it into intrusion detection rules for rule base's updating.
本文论证了应用该模型的可行性，并利用通用入侵检测框架CIDF对应用该模型的入侵检测系统进行了描述，最后与其它入侵检测模型进行了比较。
This paper proves that the model is feasible for intrusion detection and describes the IDS that USES this model to describe net-attacks by CIDF (Common intrusion detection Framework).
本文论证了应用该模型的可行性，并利用通用入侵检测框架CIDF对应用该模型的入侵检测系统进行了描述，最后与其它入侵检测模型进行了比较。
This paper proves that the model is feasible for intrusion detection and describes the IDS that USES this model to describe net-attacks by CIDF (Common intrusion detection Framework).
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