The Intrusion Detection Model and Simulation Study Based on the Bayesian

ZHANG Qian; ZHOU Jian

Chinese

您当前的位置：

首页 >

文章列表页 >

The Intrusion Detection Model and Simulation Study Based on the Bayesian

更新时间：2026-09-04

- The Intrusion Detection Model and Simulation Study Based on the Bayesian
- Vol. 30, Issue 11, Pages: 119-122(2013)
- 作者机构：
  
  1. 合肥工业大学计算机与信息学院,安徽,合肥,230009
  2. 合肥工业大学信息与网络中心,安徽,合肥,230009
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
移动端阅览
章倩, 周健. 基于贝叶斯的入侵检测模型与仿真研究[J]. 微电子学与计算机, 2013, 30(11): 119-122.

ZHANG Qian, ZHOU Jian. The Intrusion Detection Model and Simulation Study Based on the Bayesian[J]. 2013, 30(11): 119-122.
章倩, 周健. 基于贝叶斯的入侵检测模型与仿真研究[J]. 微电子学与计算机, 2013, 30(11): 119-122. DOI：

ZHANG Qian, ZHOU Jian. The Intrusion Detection Model and Simulation Study Based on the Bayesian[J]. 2013, 30(11): 119-122. DOI：

摘要

本文提出了一种基于贝叶斯的网络入侵检测模型。首先，使用主成分分析法提取网络数据包关键属性、消除冗余属性、降低维数，再用贝叶斯分类器进行分类。结果表明，该模型不但提高了入侵检测的效率，而且也加快了检测速度，更适合当前的复杂网络检测。

Abstract

This paper proposes a network intrusion detection model based on Bayesian. First

the principal component analysis (PCA) was used to extract the key ability

eliminate the redundant attributes

reduce the data dimension

then use the Bayesian classifier to classify.The simulation results show that the model improves the efficiency of the intrusion detection and speed up the detection speed

and is suitable for the current complex network intrusion detection.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of Mechanical Information Text Classifier

Aerial small object detection algorithm based on improved RT-DETR

A review of large model development: technological evolution from BERT to Deepseek

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Related Author

DONG Li-li

WEI Sheng-hui

Related Institution

School of Information & Control Engineering, Xi′an University of Arch & Tech, Xi′an

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰