Research on a dynamic self-learning efficient intrusion detection model

YANG Wu1; ZHANG Bing2; ZHOU Yuan2; WANG Wei1

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Research on a dynamic self-learning efficient intrusion detection model

更新时间：2024-10-14

- Research on a dynamic self-learning efficient intrusion detection model
- Issue 12, Pages: 33-38(2007)
- 作者机构：
  
  1. 哈尔滨工程大学信息安全研究中心
  2. 国家计算机网络应急技术处理协调中心
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393.08
- Published：2007
- 稿件说明：
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YANG Wu1, ZHANG Bing2, ZHOU Yuan2, et al. Research on a dynamic self-learning efficient intrusion detection model[J]. 2007, (12): 33-38.
DOI：

YANG Wu1, ZHANG Bing2, ZHOU Yuan2, et al. Research on a dynamic self-learning efficient intrusion detection model[J]. 2007, (12): 33-38. DOI：

摘要

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。

Abstract

A dynamic self-learning efficient intrusion detection model was proposed based on inductive reasoning.Ap-plying the method of inductive reasoning into intrusion detection

an incremental inductive reasoning algorithm for intru-sion 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.

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