基于二次决策的深度学习入侵检测模型

江泽涛; 翟振宇

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基于二次决策的深度学习入侵检测模型

更新时间：2026-09-04

- 基于二次决策的深度学习入侵检测模型
- Based ontwice decision for deep learning intrusion detection model
- 微电子学与计算机 2020年37卷第4期页码：32-36
- 作者机构：
  
  桂林电子科技大学机构广西图像图形与智能处理重点实验室,广西,桂林,541000
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61876049国家自然科学基金 61762066国家自然科学基金 61572147广西科技计划项目 AC16380108广西图像图形智能处理重点实验项目 GIIP201701广西图像图形智能处理重点实验项目 GIIP201801广西图像图形智能处理重点实验项目 GIIP201802广西图像图形智能处理重点实验项目 GIIP201803广西研究生教育创新计划项目 2019YCXS043
- DOI：
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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江泽涛, 翟振宇. 基于二次决策的深度学习入侵检测模型[J]. 微电子学与计算机, 2020, 37(4): 32-36.

JIANG Ze-tao, ZHAI Zhen-yu. Based ontwice decision for deep learning intrusion detection model[J]. 2020, 37(4): 32-36.
江泽涛, 翟振宇. 基于二次决策的深度学习入侵检测模型[J]. 微电子学与计算机, 2020, 37(4): 32-36. DOI：

JIANG Ze-tao, ZHAI Zhen-yu. Based ontwice decision for deep learning intrusion detection model[J]. 2020, 37(4): 32-36. DOI：

摘要

针对深度神经网络用于入侵检测方法时存在训练过程中由于数据不平衡和特征冗余两大问题而导致的低检测率和高误报率，提出一种基于二次决策的深度学习模型(TDDL).该模型由深度堆栈自动编码器(DSAE)和神经网络结合，包括二个阶段特征学习，其中第一阶段使用DSAE对特征压缩并加入区分异常数据的概率值特征，第二阶段使用神经网络(NN)接收第一阶段的特征并训练，从而降低特征冗余和平衡对正常数据的偏向，以提高检测效果.经KDDCUP99数据集进行实验测试，仿真实验结果表明，该模型能有效提升深度神经网络在入侵检测数据上特征学习的效果，使其具有更高的准确率的同时，还具有较低的误报率.

Abstract

Aiming at the intrusion detection method of deep neural network

there are two problems of data imbalance and feature redundancy in the training process

resulting in low detection rate and high false positive rate. Based on Twice Decision Deep Learning model(TDDL) is proposed: The model is a combination of Deep Stack Autoencoder(DSAE) and neural network

including two-stage feature learning

in which the first stage uses DSAE to compress features and add probability value features that distinguish abnormal data

and the second stage uses neural networks(NN) receives the characteristics of the first stage and trains

thereby reducing the feature redundancy and balance bias on normal data to improve the detection effect. The experimental results of KDDCUP99 dataset show that the model can effectively improve the effect of deep neural network on feature detection of intrusion detection data

so that it has higher accuracy and lower false positive rate.

关键词

Keywords

references

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