动态自学习的高效入侵检测模型研究

提出了一种基于归纳推理的动态自学习的高效入侵检测模型。将归纳推理方法应用到入侵检测中，提出了适用于入侵检测的增量学习推理算法。通过该算法建立的入侵检测模型能够对不断出现的新的网络行为数据进行自学习，并动态修正模型的行为轮廓，从而克服了传统静态检测模型必须完全重新学习才能更新模型甚至无法重新学习的缺陷，同时较大地提高了入侵检测模型的学习效率和检测效率。

Research on a dynamic self-learning efficient intrusion detection model

A dynamic self-learning efficient intrusion detection model was proposed based on inductive reasoning. Applying the method of inductive reasoning into intrusion detection, an incremental inductive reasoning algorithm for intrusion detection was proposed. This model produced by this algorithm can make self-learning over the ever-emerged new network behavior examples and dynamically modify behavior profile of the model, which overcomes the disadva-ntage that the traditional static detecting model must relearn Over all the old and new examples, even can not relearn because of limited memory size. And at the same time, the learning efficiency and detecting efficiency of intrusion detection model are improved greatly.